Millisecond Forums

Setting markers for stimlustimes with Tobii Eyetracker

https://www.millisecond.com/forums/Topic27126.aspx

By Aleya - 5/15/2019

Hi,
I've tried to find a solution by reading previous comments on this topic, but cannot seem to send useful markers for the onset of my stimuli.

It is a simple experiment, in which two pictures (one neutral and one emotional) are shown simultaneously on each side of a fixation cross for only 200ms. Before the onset of the stimuli I want a recorded fixation cross time of 300ms. ITI are fixation crosses as well fr 3000-6000ms.
According to an example script on the millisecond homepage, I tried the following:

<values>
/ trialduration = 2200
/ marker = 0
/ stimulusonset = 0
/ image_neu = ""
/ image_emo = ""
</values>

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

I cannot seem to get adequate markers for the stimulus onsets. I know I set it up as a multiple digit number to indicate the current index of each picture shown for the left and the right, but they seem to be random.
Also, I would like to also set markers for the fixation cross, so I have a baseline to analyze the data from. I realize that I have to set markers back to 0 if I have more than one in a single trial, but I just want to get the stimulus setting right first.
I'm sorry if this question is trivial, I just don't seem to be understanding how to implement it.
Thanks in advance,
Aleya
By Dave - 5/15/2019

Aleya - Thursday, May 16, 2019
Hi,
I've tried to find a solution by reading previous comments on this topic, but cannot seem to send useful markers for the onset of my stimuli.

It is a simple experiment, in which two pictures (one neutral and one emotional) are shown simultaneously on each side of a fixation cross for only 200ms. Before the onset of the stimuli I want a recorded fixation cross time of 300ms. ITI are fixation crosses as well fr 3000-6000ms.
According to an example script on the millisecond homepage, I tried the following:

<values>
/ trialduration = 2200
/ marker = 0
/ stimulusonset = 0
/ image_neu = ""
/ image_emo = ""
</values>

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

I cannot seem to get adequate markers for the stimulus onsets. I know I set it up as a multiple digit number to indicate the current index of each picture shown for the left and the right, but they seem to be random.
Also, I would like to also set markers for the fixation cross, so I have a baseline to analyze the data from. I realize that I have to set markers back to 0 if I have more than one in a single trial, but I just want to get the stimulus setting right first.
I'm sorry if this question is trivial, I just don't seem to be understanding how to implement it.
Thanks in advance,
Aleya

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

At the time you set the marker value, /ontrialbegin, the items that the trial *will* display eventually have not been selected yet. That only happens when that trial displays the two pictures. Thus, currentindex in your case will not reflect what will happen eventually during the trial later. Instead, currentindex will still reflect whatever items were selected during any *previous* trial.

Change things to nextindex, and then things should work:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>
By Aleya - 5/15/2019

Dave - Thursday, May 16, 2019
Aleya - Thursday, May 16, 2019
Hi,
I've tried to find a solution by reading previous comments on this topic, but cannot seem to send useful markers for the onset of my stimuli.

It is a simple experiment, in which two pictures (one neutral and one emotional) are shown simultaneously on each side of a fixation cross for only 200ms. Before the onset of the stimuli I want a recorded fixation cross time of 300ms. ITI are fixation crosses as well fr 3000-6000ms.
According to an example script on the millisecond homepage, I tried the following:

<values>
/ trialduration = 2200
/ marker = 0
/ stimulusonset = 0
/ image_neu = ""
/ image_emo = ""
</values>

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

I cannot seem to get adequate markers for the stimulus onsets. I know I set it up as a multiple digit number to indicate the current index of each picture shown for the left and the right, but they seem to be random.
Also, I would like to also set markers for the fixation cross, so I have a baseline to analyze the data from. I realize that I have to set markers back to 0 if I have more than one in a single trial, but I just want to get the stimulus setting right first.
I'm sorry if this question is trivial, I just don't seem to be understanding how to implement it.
Thanks in advance,
Aleya

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

At the time you set the marker value, /ontrialbegin, the items that the trial *will* display eventually have not been selected yet. That only happens when that trial displays the two pictures. Thus, currentindex in your case will not reflect what will happen eventually during the trial later. Instead, currentindex will still reflect whatever items were selected during any *previous* trial.

Change things to nextindex, and then things should work:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

Wonderful Dave, thank you very much!
By Aleya - 5/16/2019

Dave - Thursday, May 16, 2019
Aleya - Thursday, May 16, 2019
Hi,
I've tried to find a solution by reading previous comments on this topic, but cannot seem to send useful markers for the onset of my stimuli.

It is a simple experiment, in which two pictures (one neutral and one emotional) are shown simultaneously on each side of a fixation cross for only 200ms. Before the onset of the stimuli I want a recorded fixation cross time of 300ms. ITI are fixation crosses as well fr 3000-6000ms.
According to an example script on the millisecond homepage, I tried the following:

<values>
/ trialduration = 2200
/ marker = 0
/ stimulusonset = 0
/ image_neu = ""
/ image_emo = ""
</values>

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

I cannot seem to get adequate markers for the stimulus onsets. I know I set it up as a multiple digit number to indicate the current index of each picture shown for the left and the right, but they seem to be random.
Also, I would like to also set markers for the fixation cross, so I have a baseline to analyze the data from. I realize that I have to set markers back to 0 if I have more than one in a single trial, but I just want to get the stimulus setting right first.
I'm sorry if this question is trivial, I just don't seem to be understanding how to implement it.
Thanks in advance,
Aleya

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

At the time you set the marker value, /ontrialbegin, the items that the trial *will* display eventually have not been selected yet. That only happens when that trial displays the two pictures. Thus, currentindex in your case will not reflect what will happen eventually during the trial later. Instead, currentindex will still reflect whatever items were selected during any *previous* trial.

Change things to nextindex, and then things should work:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

And how do I set separate markers for the duration of the fixation cross?
By Aleya - 5/16/2019

Dave - Thursday, May 16, 2019
Aleya - Thursday, May 16, 2019
Hi,
I've tried to find a solution by reading previous comments on this topic, but cannot seem to send useful markers for the onset of my stimuli.

It is a simple experiment, in which two pictures (one neutral and one emotional) are shown simultaneously on each side of a fixation cross for only 200ms. Before the onset of the stimuli I want a recorded fixation cross time of 300ms. ITI are fixation crosses as well fr 3000-6000ms.
According to an example script on the millisecond homepage, I tried the following:

<values>
/ trialduration = 2200
/ marker = 0
/ stimulusonset = 0
/ image_neu = ""
/ image_emo = ""
</values>

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

I cannot seem to get adequate markers for the stimulus onsets. I know I set it up as a multiple digit number to indicate the current index of each picture shown for the left and the right, but they seem to be random.
Also, I would like to also set markers for the fixation cross, so I have a baseline to analyze the data from. I realize that I have to set markers back to 0 if I have more than one in a single trial, but I just want to get the stimulus setting right first.
I'm sorry if this question is trivial, I just don't seem to be understanding how to implement it.
Thanks in advance,
Aleya

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

At the time you set the marker value, /ontrialbegin, the items that the trial *will* display eventually have not been selected yet. That only happens when that trial displays the two pictures. Thus, currentindex in your case will not reflect what will happen eventually during the trial later. Instead, currentindex will still reflect whatever items were selected during any *previous* trial.

Change things to nextindex, and then things should work:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

Also, the way it is coded now, the stimuli seem to be repeating, even though replace is set to false. What am I not getting?
By Dave - 5/16/2019

Aleya - Friday, May 17, 2019
Dave - Thursday, May 16, 2019
Aleya - Thursday, May 16, 2019
Hi,
I've tried to find a solution by reading previous comments on this topic, but cannot seem to send useful markers for the onset of my stimuli.

It is a simple experiment, in which two pictures (one neutral and one emotional) are shown simultaneously on each side of a fixation cross for only 200ms. Before the onset of the stimuli I want a recorded fixation cross time of 300ms. ITI are fixation crosses as well fr 3000-6000ms.
According to an example script on the millisecond homepage, I tried the following:

<values>
/ trialduration = 2200
/ marker = 0
/ stimulusonset = 0
/ image_neu = ""
/ image_emo = ""
</values>

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

I cannot seem to get adequate markers for the stimulus onsets. I know I set it up as a multiple digit number to indicate the current index of each picture shown for the left and the right, but they seem to be random.
Also, I would like to also set markers for the fixation cross, so I have a baseline to analyze the data from. I realize that I have to set markers back to 0 if I have more than one in a single trial, but I just want to get the stimulus setting right first.
I'm sorry if this question is trivial, I just don't seem to be understanding how to implement it.
Thanks in advance,
Aleya

I set up trials for each version emoneu (emotional left and neutral right) and neuemo after defining the pictures and their positions:
<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

At the time you set the marker value, /ontrialbegin, the items that the trial *will* display eventually have not been selected yet. That only happens when that trial displays the two pictures. Thus, currentindex in your case will not reflect what will happen eventually during the trial later. Instead, currentindex will still reflect whatever items were selected during any *previous* trial.

Change things to nextindex, and then things should work:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

Also, the way it is coded now, the stimuli seem to be repeating, even though replace is set to false. What am I not getting?

> And how do I set separate markers for the duration of the fixation cross?

What do you mean by this? Please give a concrete example.

> Also, the way it is coded now, the stimuli seem to be repeating, even though replace is set to false. What am I not getting?

When you sample from a given stimulus pool more often than it has items, then it has to repeat items eventually. I.e. once it has run out of items (sampled without replacement), the stimulus pool resets and all items become available again.
By Aleya - 5/16/2019

Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!

By Dave - 5/16/2019

Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.

By Aleya - 5/21/2019

Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?
By Dave - 5/21/2019

Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.
By Aleya - 5/21/2019

Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>

By Dave - 5/21/2019

Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

By Aleya - 5/21/2019

Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 
By Dave - 5/21/2019

Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 

Please clarify what exactly you mean by "repeating stimuli in the presentation." As stated before, please also provide a data file that illustrates what exactly you mean.
By Aleya - 5/26/2019

Dave - 5/22/2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 

Please clarify what exactly you mean by "repeating stimuli in the presentation." As stated before, please also provide a data file that illustrates what exactly you mean.

When I run the experiment, stimulus S30.jpg for example will be shown twice within the first 10 trials:

subject  group  trialnum   values.image_emovalues.image_neuvalues.iti
1                1          2               S30.jpg         N03.jpg      3004
1                1          3               S03.jpg          N18.jpg      3836
1                1          4               S25.jpg          N01.jpg      4856
1                1          5               S09.jpg          N04.jpg      3431
1                1          6               S27.jpg          N25.jpg      4099
1                1          7               S24.jpg          N24.jpg      3833
1                1          8               S22.jpg          N13.jpg      4304
1                1          9               S28.jpg          N11.jpg      3418
1                1         10              S30.jpg         N20.jpg      4452
1                1         11              S23.jpg          N11.jpg      3350
…..

How can that be?
By Dave - 5/26/2019

Aleya - 5/27/2019
Dave - 5/22/2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 

Please clarify what exactly you mean by "repeating stimuli in the presentation." As stated before, please also provide a data file that illustrates what exactly you mean.

When I run the experiment, stimulus S30.jpg for example will be shown twice within the first 10 trials:

subject  group  trialnum   values.image_emovalues.image_neuvalues.iti
1                1          2               S30.jpg         N03.jpg      3004
1                1          3               S03.jpg          N18.jpg      3836
1                1          4               S25.jpg          N01.jpg      4856
1                1          5               S09.jpg          N04.jpg      3431
1                1          6               S27.jpg          N25.jpg      4099
1                1          7               S24.jpg          N24.jpg      3833
1                1          8               S22.jpg          N13.jpg      4304
1                1          9               S28.jpg          N11.jpg      3418
1                1         10              S30.jpg         N20.jpg      4452
1                1         11              S23.jpg          N11.jpg      3350
…..

How can that be?

Because you have two trial elements that sample from two separate <picture> elements. They are independent. This becomes much clearer when you actually log some more useful information:

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>



This is exactly what the script is set up to do.
By Aleya - 5/26/2019

Dave - 5/27/2019
Aleya - 5/27/2019
Dave - 5/22/2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 

Please clarify what exactly you mean by "repeating stimuli in the presentation." As stated before, please also provide a data file that illustrates what exactly you mean.

When I run the experiment, stimulus S30.jpg for example will be shown twice within the first 10 trials:

subject  group  trialnum   values.image_emovalues.image_neuvalues.iti
1                1          2               S30.jpg         N03.jpg      3004
1                1          3               S03.jpg          N18.jpg      3836
1                1          4               S25.jpg          N01.jpg      4856
1                1          5               S09.jpg          N04.jpg      3431
1                1          6               S27.jpg          N25.jpg      4099
1                1          7               S24.jpg          N24.jpg      3833
1                1          8               S22.jpg          N13.jpg      4304
1                1          9               S28.jpg          N11.jpg      3418
1                1         10              S30.jpg         N20.jpg      4452
1                1         11              S23.jpg          N11.jpg      3350
…..

How can that be?

Because you have two trial elements that sample from two separate <picture> elements. They are independent. This becomes much clearer when you actually log some more useful information:

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>

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

This is exactly what the script is set up to do.

Ok, I understand. But then how do I avoid these repetitions? By sampling them in a dependent manner? 
What I would like is that each picture is shown only once during the 60 trials, but that stimuli are randomly selected and combined to be presented in pairs that differ from participant to participant, while also randomizing the side on which it is shown. 
By Dave - 5/26/2019

Aleya - 5/27/2019
Dave - 5/27/2019
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Aleya - Wednesday, May 22, 2019
Dave - Wednesday, May 22, 2019
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Aleya - Wednesday, May 22, 2019
Dave - Friday, May 17, 2019
Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 

Please clarify what exactly you mean by "repeating stimuli in the presentation." As stated before, please also provide a data file that illustrates what exactly you mean.

When I run the experiment, stimulus S30.jpg for example will be shown twice within the first 10 trials:

subject  group  trialnum   values.image_emovalues.image_neuvalues.iti
1                1          2               S30.jpg         N03.jpg      3004
1                1          3               S03.jpg          N18.jpg      3836
1                1          4               S25.jpg          N01.jpg      4856
1                1          5               S09.jpg          N04.jpg      3431
1                1          6               S27.jpg          N25.jpg      4099
1                1          7               S24.jpg          N24.jpg      3833
1                1          8               S22.jpg          N13.jpg      4304
1                1          9               S28.jpg          N11.jpg      3418
1                1         10              S30.jpg         N20.jpg      4452
1                1         11              S23.jpg          N11.jpg      3350
…..

How can that be?

Because you have two trial elements that sample from two separate <picture> elements. They are independent. This becomes much clearer when you actually log some more useful information:

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>

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m98wHwx45Qx6fA+NzW5aBmKiuXJxjfY8we/xfU33X7Jwwh/BqIi8e2B5emGi+zTzt3p1dXhO8BuNNwukzfPa/32TQsuycf6vvvPvzKfV9n0n4vx3/D73KbRfZ851QSivn4+Lj6s2xnIRx+RZrq2gfUwdGtT/5l1sUrA/jjqSwJv/fHIifFJxjfY8we/2fX343758kh/BrQxeOiAP/MpdNRh4PzwDP3vWaDAdC37DNaqouF18j75xB+e6IDlMd/1+/SjGZ31g+/49811Hy6rNrWJRdtLEQLv/0qSjj8BurT8OSqKKVuDDNL+OOpmGd+B5MRqa8/E7PH/1n19wH98+QQfg1o4nVL6+c2ZfBq9ElLDL9eTw2LRyS8xt4/p/B7xZ056/H6rw/Mo+/j2eeXSnK4uf+Mmb31UMOvtFKXlTSB8Jtcnyx1HH88lfnhN7Tn1+OTE4zvMWaP/zPq70P658kh/BoIiTfeN3Ve9MHZw6LwO2PmN7o/Kr/w6xucgv7zXTDnnU1PJCXcOJqP9ltzqx4Tsf7V1qVU9dTzc+qTu01oFvjjqcwOv776GKqZJxjfY8we/xPr78P658kh/BrwiUfwvTP7Vl2x8Ntf8LV4z27arcJuj/czxTsOv+6e5pj/km51dpshj/TfhHAzLs6ebSewmOjJZVtLWZaTQDxrYPV5AH9kwdKZ30l99R3nE4zvMZaM/7H6u0r/hCRWDr/+K3WPGgaDV+N72hlPzlL/SIPvalZ/+BWZaDz7bg3K+4s4WyMqafY08+vbtuFZQtP9l3CTdGO40Wbu+gsZw98BUklZWRkvn8b9MZp5DRUt/JEFwckHdXx2PRI5fgce32MsG/+1+rtS/4Qk+PPGBriPrQ762Ni9fkl7Ogd4l1DPfUcUC/gDNHbvj8xB37wh/BrA/DroY2N/+jVSu7fZGW9g08ON7+KZBX+4AzrwB2jszx/HAn3zhvBrAPProI+NPeo3WpaeLEfHb5s2uaCJYLMY/AEae/THkUDfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8IfwawPw66GMD/UADf4AG/tgW9M0bwq8BzK+DPjbQDzTwB2jgj21B37wh/BrA/DroYwP9QAN/gAb+2Bb0zRtz+L1cLjQajUaj0Wg0WjbNFH7PzFLxzgL62EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV982aj8NtKXRZSFJU0Sz9ZBvjEa+tSiqK4t+rICuhQHGz49ev71r2VdTt4vJFK8d/En5PXp9NUx+/je2bij7aW0j3mbS1lUcr4EKfU5/457mvTwR/PJVx/w8c/Pn7p9eVM+PVdo/4+pn+enfXDb1N1B6SuDl/44uJ1HeGs9YHwa2Oq37XgBQ3VPX5/uPPfsLi2dbnagEW4eS7+8FtKWQ4GRDf8Jtbnti6lKEtPcE4HfzwXb/2dNT6741e8vpyJUH021d8H9s+zs3L4baTqD1hD+O07A8UBlhCa2ZuTXZuqkKKs5Z6F1gu/8FxC4beuq3vNGYXf1PrcSFWUUrf9vxt+CdiMaf2dOz7Hxy+3vpyJlPFtXv2lfz6S7fb8En4DS47nIaRPNyN0bcPC2VRSVM3t8arxLe24y/5Dj/Vn3sOlp6n+wfefnLlfP9OTirt/8Jo32KwbfqdbLuL6u2Hd9zvOe4JoIRh+20aq/piHapBSn5uqX12YO7jijz2hjk8p43PC+EX41VlcfzfpnzCE8GsgJdyduWj79Ll33MH/++LQVDfN+r1RfQDudJwu+3fP6302Dctu8VHff/fhV+77OpP22wWW3YbBwvLdJn38Hlz6jzY+Pq7+514ZsRIOvyJNde0Dc8NvW0t5+7lxcMUfT2Vp+E0fv9j2oGOov4/onyeH8GsgddvDWZeZ/TOXTmcdDs4Dz3R7mmpphwPgqOMPf2c/mMbCa+T9cwi/PdcThenMWU/KRRPzZ5Knn8E3s+e+x3DZffw42zCWo4XffhVlXvh1w+Y24Rd/PAbzzK86fp3jonYNffw31t9H9M+TQ/g1kCTeCXQIEbwafdISw69SEJLCb+z9cwq/V9yZs55u9iahMFr8mRxu7j9jZm891PArrdRlJc2c8Dvx+qPCL/7YAnv4DT8vub4cGE1fc/19RP88OYRfA8nhlz1RHbE9ZIvC74yZ3+getvzC7+3q4MEH7AJxWlEMheckUsKNo/lov/XJb5VkJda/2rqUqk4Pv5NjY90egz+eymrh1zn2c+rLkQnpu0r9fUT/PDmEXwNL9vycidm36oqF336ZaPGe3bRbhd0e72eKdxx+vRdUpA5M1+838edthjzSfxPCzXgmj5mKNYmeXLa1lKHbISXV58Dxwh9ZYA+/oT2rHCMR7T7/xvorYuufkMTK4dd/pe5RO0sw3AWvZD4XqX+kYXzBmxZ+RW4BePHdGpT3F3G2RlTS7Gnm17dtY7IM5p8ZGC0rx/xpDDfazJ3vJu/MWiwjZWVlvPw6tz5vE37xx2NIGZ98277C9SFeX85E8G48i+vvSv0TkuDPGxvgPrY66GNj9/ol7ekc4N12cu7VEQv4AzR274/MQd+8IfwawPw66GNjf/o1Ut/X1D1bSCLhxrM/eckf7oAO/AEa+/PHsUDfvCH8GsD8OuhjY4/6jZalJ8vRkXAj/os2CDbLwB+gsUd/HAn0zRvCrwHMr4M+NtAPNPAHaOCPbUHfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8IfwawPw66GMD/UADf4AG/tgW9M0bwq8BzK+DPjbQDzTwB2jgj21B37wxh9/L5UKj0Wg0Go1Go2XTTOH3zCwV7yygjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/Dvos4d2tdfq9O3d78zmRr/+KyCe/KPLVn5u2T/6eVfBsoX+BBv7YFvTNG8KvAcyvE9fntci7T0TefSTy9rdE3n5J5O0XRN58IPLmpcib90Ref0bk9a+LfP3/E/n6/yvy9V/1N5HuOa//gcjrT4u8/o3u9W9edr/v7RdE3n7Yvc+7r4q8+8rgPQNNROTdx1tKNOajvyzyfnFrl0sx+j/N0z77E487PjuD+gMa+GNb0DdvCL8GML/OTZ8374l8/H+JfOUviHz4p0U++CMiL77p+cEpuX2DyItvF3n5PSIf/EsiX/xTIl/+b0S++hdFPv7bXejuw7IFwi/hdwbUH9DAH9uCvnlD+DWA+XUul4vI1/7a8wPSo9qLb+5mp5dC+J3fCL8AXvDHtqBv3mwUflupy0KKopJm6SfLAF28XoNS6vZhH2lXnC78vl90e1OXkhh+O1/dW1mPH68GjxXV9PWxx1NbU3W/oyH8PoVJ/WlrKYtCiqpxfjauQf1xK4pCirKWcXlqHH8sr+Dd+xx7DNgz4fEpZXz2j19tXY5qT1d/zjnA+fWN9Z94/3pU/zw764ffpuo6TF0dvvBp4rV1KUVZTgaeM0H4nUlC+K1LPbDWZSFVMw66w3Ace5zwmw/+8FtKWQ5qjhN+m2o4oF4Dzm0Aba/+6H9hc/XHsgJG+H0u3vEpcXwOjV9tXRK4rkz1jfWfeP96ZP88OyuH30aqvkM1Zw6/jVRFKXXb//vQj7UbCL8ziYXftpCyGIfXpIBaFtIufHz3jfB7pw+6dXUfEEfh11OP+jAUqFHjwRhyYjo+pY7P4fGL8HsnJTzF+s/4cfrnI9luz++Jw29T9WdrhF9f+B0v6wyCV9PNavaPV819ib9u7693l/2HM4/9zOZwaWj4WvX935/OjEozMxxuPPNbeT7zI8Ovq32K/m5Y9/2OpbPPhN8Bt6DbSNUPiMPwO6nJ7e1YhPLMvMG19Rzb6ftVTeP4I/Y7mN1agjq4K+OzNn4Rfu+sHn43758whPBrwCteW0t5+96EXzf8doU1EL6aexBq63EA7sORu+zfP68PYG5YdsOd+v4ZhN9+9jd1v25sW4Nl20N/vNzwOwy87vFx9R8eW8LvPMLhV6SprnXHDb+jUFxI1bhLqUOMy6oJg3m3h/T+nHvwuj+f4LuMReE3Mn5N9vyeOHjFw1Os/ziPP7p/nhzCr4HQnp+7GQm/bvit3JnYa5ir23GYaut76LwFpOtz3T2m1SBsxcKr+v45hF8nePpmVt0gGtqTG3t8afh1P89Q88r5vG1tu+iO8DvADbpV4w2/TV0OllJDg+sKFy0Hwu/4vYY1srn6Y/iVmGlcyvzwO3f8us7gnzQAp13wHuo/nscf3T9PDuHXwES84Zlb9wPC7zD8DmctnZYUfj1hyw1caniNvX9O4XcYHj2a9Fs73C0fqY+vHX77nzHzux5q+JVW6rKSZrLtwQ0r08Ap0l+sZqxdyeH3/jNmftdjdvhdMn6dYJwPoekb6z/exx/dP08O4deAK95oL6nbTnh2HAq/wcC1MPwmz/zG3j/D8NtrMvxOfSAOfc/Y46uGX0fzSR+pjJ+B8HvHubNDW5dS1YOf3ZZS3deM63Q7mnkykBJ+fXej4FZOqzA3/C4Zv9xtK2cipG+s/wQff3T/PDmEXwNpe37Oa1Lftgf1Vl2x8HsNupY9uym3Crs93s8U7zj8ut9/leA7mCFXt0QkhF93pney7cTaCL933Hv6trWUzu2q9FspJQ6s/f2EY/U9IfyOZ3rPXS/XZukFb4Mn6cfj6oOzzsz79F0cfK+s0j8hiZXDr/9K3aMeLMKvTuhuDxOPOBe8aeH3FoDd1yaGX/X9neBXFIU0e5r59W3bcD5bNel7433BscfXCL/azG4fvrXvQPhNIxp+xbc86tTo0TRTo/jDfZ/l4Veb2fX9EYUzrpqtQeialPTxeTp+ubPDZ56Y999KTus/Kf1rhf4JSfDnjQ3w5w11uM/vTHL788aJe35vLbDtxHTHCcLvfkna8zvAE965on05u/dH5qBv3hB+DWB+HcLvTDIIv7Xz1+LcmV01/Hr2Jy/5wx2E34791Z9GauevWY1nriLh13dDf98+SEhif/44FuibN4RfA5hfh/A7kwzC72jbgme7ghp+3/dc8GYJvoTfZ3+ECaNtC5PtCpHwK/6Lrgi+y9ijP44E+uYN4dcA5tch/M4kg/C7u0b4BfCCP7YFffOG8GsA8+sQfmdC+J3fCL8AXvDHtqBv3hB+DWB+HcLvTAi/8xvhF8AL/tgW9M0bwq8BzK9D+J3JR39F5P1vvLXL5RtH/z9d+/KfDz/24ttEXv1+kS/8m+sZNjOoP6CBP7YFffOG8GsA8+ssD7/fKPLim7uA8+J3iLz8XSIv/wmRl98n8uqHRF79iMhnf0zksz/e/fuqvP78+0Vefo/Iy+8SefEdIi++XeTFt4p89p+9/j6nffDHRF58i8iLf1Tk/d/2/PDr0w8gAP4ADfyxLeibN+bwe7lcaDSl/XW5XL4h0L5NLpefksvl35PL5b+Xy+UXdvB5+/a35HL5X+Ry+bNyufzbcrn8cblcfkgul2+Uy6VQ2i/u4LPTaDQajUbTmin8npml4p2Fw+rz7uNwW5HD6gergD9AA39sC/rmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65g3h1wDm10EfG+gHGvgDNPDHtqBv3hB+DWB+HfSxgX6ggT9AA39sC/rmzUbht5W6LKQoKmmWfrIM8InX1qUURTFqZd0+4dM9H4qDDb9+fd/S/BXrf+PfsdSfTXX8Pr5nJv5oaymLQoqqcX5Wyv0QN1IN61PlHj5dbDIAACAASURBVL3Y4+ngj+cSrr9r1If+OUNvnYtt9H1c/zw764ffpuo6RF0dvvAFwy+GFBHCr5Wpfteiqfkr2v+64rnGCRnh5rn4w28pZTkIJKPw2/nnbh/XC7HH54E/nou3/q5UH9q6lKIsnROrc7G+vo/tn2dn5fDbSNUf8Ibwe3YIvzZCM3the8X7X1MxW3AUQuG3rqv7gDiZ+R3TVIUUZS2h4TP2OOyXaf1dqz40UhWl1G3/72ofOSu209d5Pv1zE7bb80v4PT0hc3UzQtc27LhNJUXV3B6vGt/SmrvsP/RYf2Y8XBqaFufg+0/OrK+f6UnFxV9cE4udt/95vt8splsu4vq77+f7HcxeLCEYfttGqt4jDw2/+GNPqIO7oT7cAxzhN8hK9Zfwux2EXwNJe35PbEyfPu6Z76jzNtVtoOt17ANwN/hNl/275/U+m4Zltzio77/78Cv3fZ2x/V7e/ncdrJrB77AMXpP3uAeX/qONj4+r//DYwlzC4Vekqa7HVQ2/sWVT47Iq/ngq88NZQn1oaylvryP8Blml/m7cP08O4ddAXLwZM3UHxD9z6Sum158NPNPtKaulHQ6Ao8I7/J39YBoLr5H3zyH89lxPFKYzZ8PHHa364Dz4Pm74mEUg3Iw/z1Dz4bEavD8rJYvQwm+/ihIOv6kX5RhqOP54KrPDWbQ+uCcjhN8g5vr7gP55cgi/BpLEO4EOIYJXo09aYviNLiVFwmvs/XMKv1eCxVObeWjdny3cCpEcbu4/Y2ZvPdTwK63UZSVNIPx2W38i2yGswQZ/PJXFM5Oh+jCphYTfIMb6+5D+eXIIvwZM4eQE6IOzh0Xhd8bMb+z9Mwy/t6uL3Q8Y1Wr4s4VFNCXcOJqP9ltzqx4Tsf7V1qVU9dTzXU0KH/PY48ngj6eyLJyF68Pk2Jx8e9/a+vY8rH+eHMKvgah415nGs85czL5VVyz89ttIFu/Zjd0qzHncs0z1SFI6Z/CCh0D/u+uqvP42Qx7pvwnhZjyTd+6ZorWJnly2tZTO7ahWGVjxRxbMD2eJ9eH+S059vLbQl+D7OFYOv/4rdY96sIIXdAWvZD4XqX+kYXzBmxZ+RW4BePHdGpT3F3G2RlTS7Gnm17dtI/bdPP0vfLcL932WhRtt5s73R2DOOGu0BikrK+PlUafvTOpU7PHh++CPvROcfLDWh/szCb8jrPqu1D8hCf68sQHuY6uDPjZ2r1/Sns4B3m0nXLG8FPwBGrv3R+agb94Qfg1gfh30sbE//Rqpnb82NJ65i4Qb3/7k6B/ugBD4AzT2549jgb55Q/g1gPl10MfGHvUbLUtPlkTjN3H3XTRDsFkG/gCNPfrjSKBv3hB+DWB+HfSxgX6ggT9AA39sC/rmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65o05/F4uFxqNRqPRaDQaLZtmCr9nZql4ZwF9bPT6ff3dJ/LJu0/k43dfk4/efSRfffcV+crb35Lfevtl+fLbL8mHb78oH779gnzh7efl828+kA/efFY+++alvHrzUl6+eV9evHlP3nv9G/Le68/Ib7z+h/KZ178un379D+STd5/Ip1//2qSJiPzD178un3n9afnN15+R917/hrx48568fPO+vHrzUj5480o+9+YD+fybz8kX335BvvT2Q/ny2y/JV97+lnz13Vfko3cfycfvviafvPtEvv7u6/JGXstbeSvv5N0z5Twc9C/QwB/bgr55Q/g1gPl10MfG5XKRv/bRX5LvfL84TPvUi2+WH3/5Kfnjn/2D8rOf+5fl3//CvyH/2Yd/Wv6nL/95+Utf/T/k7338/8hvvv6MfPzua8+Wf/fQv0ADf2wL+uYN4dcA5tdJ0efjdx/Lh2+/KC/fvC+ffv1r8iuf/JL88ie/6G2/8skvSfv1X5VPv/41+c3Xn5GXb96Xz735QD58+0X5/JvPyQdvXsnLN+/Le69/Qz7z+tdFRLwzmy/evCefef3r8t7r35BXb15cf8cX5MtvvyQfvfuqfPLuE3kjbzZWJ84Rw++c9uMvPyV/4oOfkT/9hX9H/tsv1fIXv/K/yi987a/K3//k78qnX/+afOHt53dxnJ4F9Qc08Me2oG/eEH4NYH4dV5/PvflA/tbX/rr8j1/+r+VPfv5n5Sdefu/TA5bWfvf73yA//epH5d/9/L8mf+5Lf0Z+/qs/J7/0yd+XD99+8WH6nTn8prZPvfhm+fmv/txDjsmeoP6ABv7YFvTNG8KvAcyv0+vzdz7+2/JnPvyPnx6S1my//MkvPkQ/wm9a+9+/8hc2Px57g/oDGvhjW9A3bwi/BjC/zuVykb/5tb/29GBE+D1+I/wCjMEf24K+eUP4NYD5dQi/dv0Iv4TfENQf0MAf24K+ebNR+G2lLgspikqapZ8sA8Li9d+/a2XdPvRz7QXCr10/b/gdeKsoCvnO2nl88FhReT5/7PHUVl3ffwfHg/ArIm0tZVFIUTXOz0oZl6BAfe5f72nVgkLeVMcfA/ZMfHwKHZvw421dTrxx5vHNj6V/NVKN6rN7BGKPQyrrh9+mkqIopa6rwxc+v3idOc9aEIYQfu36TcJvGQmsZSHf2YyD7igcxx4n/GaDP/yWUpaDsOuG37n12Rue0yD8Phfv+BQ7/pHH27okcF1ZpK/LqH91odkNwvcsEXsc5rBy+G2k6g94c87w21ScjfUQfu36jcJvew2rzYzPWhVSlIbHM2mEX7kPpHV1HxBHg+v8+kw9y5fp+BQ7/nF/EH7vzNd3Sqx/NVUhRVlLKN7GHocw2+35PWX4dc/Mzo03/PYzj8Nl+9YTyPrH3WDmzlw2nueEXt9cZ02vj39nU9y2EIw+g7utYC/h9/2rbnPC6trh19Fmoo/v+Lph3fc7ls4+E37v3IJuI1U/IIZmblPq83WZNr2ejbd7dW34Hn19HC/djn+/73cwu7UEdXCPHX/CbxSTviJJ/Yvwux2EXwP+M79S6ma4t2fZkuERCIXfUdh0w1flLOu7j8fCr/b6ZhC06nEAvoUvd1tB7Ql4zwy/7SBYVtPPNWmxYGkJno1Hm7IYB15XP/f4lIb3J/yOfzAIuk11rTuG8NvWpW1gnbzHPdj2A363h/T+nPFMWPd8gu8yNgu/wxOTEwcva/iN96/Ytga2PVgg/BoIXnAyMLRb3M+EOvMbCq/uLGzr/Mzy+mFYqwev6wNY/1xfQHRmL59+t4dGmVl1g2goNMYeXxh+J59neEzcz1sbL7oj/N4ZBt2m6kLk4vDbDaymSb5A+B3/zuuEwW1bxvhxZhqXs0X4dZ7UzeCfNADbwm+sfy2/KBHSIPwaCM78tu7PzrkVYnb4Hc5qusviKeE39vpY+PWFuUCge3r4HYZH32euAltKUh9fO/w2g/etnOcz87sINfxKK3VZSbMw/Jpnfb3vEQq/958x87se24ffGc87IBZ9Y/2ru1g0vGocexziEH4N+MOvNrNxLpaGXzWQWV6/NPzucebX/U7D71xHgm3s8TXDr3tMKufEpDJ+BsLvHSfotnUpVb0k/K60nJoSfp3P3Ez8ceQRZFseEX7PvrIZxNC/Ok3DmSH2OKRB+DXgE889ozvzhvRF2x4SbuV1e7yf6U19fSz8XoOuuud4b+HX/XxrBN/BDLo5/FaOnmuEbsKviMTDr7S1lGU5P/zGavftfqWR+p4QfsczveedKNiCzcPv1QdnnZlfrK/yGMH3cawcfv1X6h71YIXEG81enDT4iiwMv32AHV1U4Q9m/X7X5NenhN/3x78/dCeEZ97qLKiN+9kdnZIeXyH8qjO7tef9V7jVGuFXvPt7x8ujKfU5YdbXGH61mV3fH1E4cw21ELobUfj4x/3hzsyfeWJ+vr4iev9y/oDFROfY4zAH/ryxAf68oQ73+bXrt+s/b5y657dvoW0pRcGtzhaw+/qTtOd3gHd/Mle0L2X3/sgc9M0bwq8BzK9D+LXrt7vw6/y1uMnMrhZ+ffuTl/zhDsKviOyx/jRSO3+NajyzGwm//V/HGubc2fcahp79+eNYoG/eEH4NYH4dwq9dvz2GX3W7ghZ+3y+mF7ytEHwJv/thtG1hsl0h/keAJhe8EXwXs0d/HAn0zRvCrwHMr0P4tev3C1/7q/KpF98cbN/34lvlB158m/zQi2+XH37xO6R88R3y+1/+LvnRl98lP/byu+UPvPwe+YmX3yt/6NX3yx9+9YPyz736IfnnX/2T8lOvfkR++tWPyh/57I/Jz3z2n5E/9tkflz/62X9afvrVj8pPvfoR+clXPyx/+NUPyh969f3yEy+/V/7Ay++RH3v53fJPvfxO+ZGX/5j8vhe/U37oxbfLD7z4Nvm+F98qv+fFN8k//v5vk9/9/jc85XgQfgHG4I9tQd+8IfwawPw6hF+7fjnyTt7JV97+lrx48560X7/I3/34/5a/8dFfkf/tK/+z/Fdf+i/kP/jCvyX/ygc/LT/28rsJvwZy9Qc8BvyxLeibN4RfA5hfxxp+f9+L3yl/6NX3y0+++mH5iZffKz/68rvkh1/8Dvm9L74lOBP6e198i/zgi98u5YvvkB99+V3y4y8/JX/41Q/KT776YfmDL79Pfuzld0v54jvkB158m/yeF9+0eKaS8LsOH737SD7z+tfl73z8t+Xnv/pz8j98+c/Jf/rF/1D+5Od/Vv7VD/6o/AuvSvnBF7+d8OvhDP6A5eCPbUHfvCH8GsD8OnPC75/44Gfkz374n8jPf/Xn5Fe//svy9XefPOxzfvj2i/LLn/yi/OWPfl7+uy//l/KnvvCvy7/46vfLp158s3zP+/+It/3q139588+Fv+68lbfyybtP5ON3H3vbGcEfoIE/tgV984bwawDz61wuF/k/v/Y3ggHyx19+Sv7zD/8j+cVP/t6zP+ouwV+ggT9AA39sC/rmjTn8Xi4XGo1Go9FoNBotm2YKv2dmqXhnAX1soB9o4A/QwB/bgr55Q/g1gPl10McG+oEG/gAN/LEt6Js3hF8DmF8HfWygH2jgD9DAH9uCvnlD+DWA+XXQxwb6gQb+AA38sS3omzeEXwOYXwd9bKAfaOAP0MAf24K+eUP4NYD5ddDHBvqBBv4ADfyxLeibN4RfA5hfB31soB9o4A/QwB/bgr55s1H4baUuCymKSpqlnywDJuK1tZRFIYWnVUcWIgDFwYZfv75v3VtZt4Hn+Pqf83qDMZvq+H18zwTrz/CYtrWURSl3izRSFdrxdx4fvXYe+OO5hOuvVh90f7R1OR7bzjiwXVmmb6z+Pq5/np31w29TdQekrg5f+JLEmww+54Hwa2Oq37VwagOO2v+619/Dsvv/eRBunos//JZSloN6M6o/3fG+26cbSDU/tHUpRVnLEofgj+firb8J9SHsj8kvk+qkEzsiy/UN97fH9s+zs3L4baTqD3hD+BW5DgAnrQ6EXxuhmb2wnSL9r62ldH92gn56VELht66r+wAZOfluqmIweHrCDP7Ilmn9nT8+j/3hYjt5zp3Z+i6ov/TP7dhuz+8JDkpUvGhYOTYhfboZoWsbFtamkqJqbo9XTb9ENBy83WX/ocf6M+fh0tB04A++/+TM+/qZnnRm7S+u2mA0fKqn/wULcurKxHTLRVx/1/++33HeAdRCMPy2jVS9R2aF335Zu39+bObPBX/sCXV8WiP8nnhVU2SBvgvq77r9E4YQfg3ExDv7koRPH3cmfNS5m+o20PV7y/oA3HXw6bJ/97zeZ9Ow7BYP9f13H35lvK88uv3B7X/+ZbTF+8Ym73EPLv1HGx8fV/9zzxxZCYdfkaa6HlN1cA0Mntd+aL5WAX88FXv49ftjOHlw5mMzX9+59Xfj/nlyCL8GdPHOvR9KJDRz6XT04eA88Mz9xGEwAPqWjUY6x8Jr5P1zCL89sQIY6n+jizJLqRvD7E0g3Iw/z1DzaZ9o6/K024KsaOG3X0UJh1//RTmjmaXByegi8MdTsYXflIvWE65BODCL9E2uvw/onyeH8GtAE+/ss74ic+6GkRh+vZ4aDqiR8Bp7/5zC7xV35uxGav+z9NPkcHP/GTN766GGX2mlLitpAuG3m71zfx7aU7j1yRH+2AJL+PX7Y/7vOTJrbCsJPe8h/fPkEH4NhMVjL45IbHD2sCj8zpj5je5Ryy/8BovfnD19S2duUsKNo/lov/XJb5VkJda/2rqUqp56frxvMPz64M9SwR9PZWk4C/oj9HtOOsmz2p5q7+3kHtA/Tw7h10BQvBN89xRm36orFn77C74W79mNLdM5j/czxTsOv8ELUhI8GLwV1W2GPOLhhHAzLu6ebSewmOjJZVtLWZaTQBzbYzjpX96LdPDH3lkSzmYF35NP8ljDr69vrdI/IYmVw6//St2jTsv7xTt3QRiS+kcaxhe8aeFX5FYAFt+tQXl/EWdrRCXNnmZ+fds2Yt/N6X+jmbXQSYAx3Ggzd5Ob5D/x5CJ3UlZWxsun7g3yfXdccJ+z/skR/ngMwcmHYH2I+WP6+jNPzM/XN1Z/V+qfkAR/3tgA97HVQR8bu9cvaU/nAO8SHSeLS8EfoLF7f2QO+uYN4dcA5tdBHxv706+R2r1Nz3gDpx5ufPuTT34vbAv4AzT2549jgb55Q/g1gPl10MfGHvUbLUtPlqMj4UY8FzQRbBaDP0Bjj/44EuibN4RfA5hfB31soB9o4A/QwB/bgr55Q/g1gPl10McG+oEG/gAN/LEt6Js3hF8DmF8HfWygH2jgD9DAH9uCvnlD+DWA+XXQxwb6gQb+AA38sS3omzeEXwOYXwd9bKAfaOAP0MAf24K+eWMOv5fLhUaj0Wg0Go1Gy6aZwu+ZWSreWUAfG+gHGvgDNPDHtqBv3hB+DWB+HfSxgX6ggT9AA39sC/rmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65g3h1wDm10EfG+gHGvgDNPDHtqBv3mwUflupy0KKopJm6SfLAL94jVRFIcWtlVK3D/9ou4DiYMOvX9+37q0cGczxX+X2wPX82VTH7+N7ZuKPtpbSPeZtLeXkGOv1uTuu11bWsrR84Y/nEq6/4ePf1uWotkzrh1N/Jo+fh6Txf1H9fUz/PDvrh9+m6g5oXR2+8E3F60w7DCNtXZ7WoIRfGyF/hQec7vH7w12hvftxXX8Sbp6LP/yWUpaDAdUNv5H63FTDATXmNx388Vy89XfW+NzVj/vhd+vHtJ6ciVB9NtXfB/bPs7Ny+G2k6g9Yc8bw6xYLOYUOIQi/NkIze3Nq3bhY4s8jEQq/dV3dB9hR+I3V50YqdyaqH4zPmW+yxj8+zRmfnbDW1lK6rzlx/UgZ3+bVX/rnI9luz+8JOoVPvG7ZaDjYcGbsEly2aSopqub2eNX0yz/Dzu0u+w891p95D5eWpoUhvGzknrlfP9OTZu5DJ1dzPs+4+Fr9Od1yEdffDeu+33HePmIhGH7bRqr+mHu3PYi/Pk9+dj9WaSdc+GNPqIN7yvjsXTVwXhPy1wmYH35n1N9N+icMIfwaCIrXVIHCfi58+jTVeJlmVByuupV1e9t71gfgrkBMl3m65/U+m4Zlt/io77/78Cv3fZ1J++0CxXUtfyYU4/HxcfU/97KplXD4FWmqax+YG35Hofne/xb5BH88laXhdzg54LuewF3GP+vMYzw8GervI/rnySH8GojO/A7C3Bnxz1w6hXI4OA88c98LNRgAfctuo6WkWHiNvH8O4bcnWkD9F02s6s9AuBl/nqHm02W/ti7Zs7YQLfz2qyhLwm8zmp1aP/zij8dgnvn17SkdnnwXpdQNM79+jPX3Ef3z5BB+DaTv+aU4iIhTOD1XvMbCr9dTw84fCa+x988p/F5xZ856utkb13cr+zM53Nx/xszeeqjhV1qpy0qa2dse3G01Hs+kgj+eij38JjzvBON8CE1fc/19RP88OYRfA/rgo/zsJCTpM2RR+J0x8xs9FvmFX1/xHO8rk+ED6/ozJdw4v3+03/rkt0qyEutfbV1KVc8Iv74LKr2rLYngj6eyWvhV6p+7jexMhPRdpf4+on+eHMKvgeAFSe6e0oPrEGL2rbpi4Tekb/Ke3bRbhd0e72eKdxx+9QsqJs9O8+dthjzi24RwMx4cPdtOYDHRk8u2lrIs0wdX8fenSX/BH1lgD7/6BbFnHttEUi54d5mRDyz9E5JYOfz6r9Q96rJ/2h+5oDiM8XhkdMGbFn5FJvrOvluD8v4iztaISpo9zfz6tm14lsCm/W9670nVn8Zwo83cTW6i/8STi9xJWVkZL7+m1OeEP2KAP7IgOPkQ2fblrxsdo5n5k4eu4OTX4vq7Uv+EJPjzxga4j60O+tjYvX5JezoHeJf4zn07QAv4AzR274/MQd+8IfwawPw66GNjf/o1Uru3ORpvQNPDje/ijgV/uAM68Ado7M8fxwJ984bwawDz66CPjT3qN1qWnixHx2+7M7mgiWCzGPwBGnv0x5FA37wh/BrA/DroYwP9QAN/gAb+2Bb0zRvCrwHMr4M+NtAPNPAHaOCPbUHfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8IfwawPw66GMD/UADf4AG/tgW9M0bc/i9XC40Go1Go9FoNFo2zRR+z8xS8c4C+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffNmo/DbSl0WUhSVNEs/WQb4xeu/+7VVR1ZAh+JgI8lfRSFl3Y6e0VSDx8taWu31Bn9273PsPr5nJv5oayndY9rWUhalDC0S9Ef/ek9bYhP88VzC9VcbnxupCr0+6PXlPAT7X6T/PKo+g8764beppChKqevq8IVvKl5n3HsYcf9/Lgi/NkL+0gpiUw0Lqvv8df1JuHku/sG3lLIchF0n/Or+8OAJz6ngj+firb/q+Nz54W6HLggP68Ns/xyYpPFtVv8jPzySlcNvI1XfoZoTht+2ltL9zifQIQTh10ZoZiE81jRSuUGlH+xawZ8HIxR+67q6D5ijwTfiDw9Ndd5wkzvT+jt/fB6Htfn+OTIp49u4/1Cf98R2e35PcNAm4vm+s2HmJHdCM5dVM1xam2oTXhZyZyak09xZOgq+vqmkqJrb41XTLzENP4O7reB5HvYPXspS48R/9+9SNb7HZaY/p1sufO83Pr5uWPf9DmY3lhAMv20jVe+R4fGN+cMlerI1eQH+2BHq4L4k/M71z8GJhie3/2xen2EOhF8DoXDiLltwZtwz1WM8szCdafItE2nhV319U90G0rYuRwG4O2bTZbzuec/xsbdzDveVuSPOUItb4R1qtrI/EwZDV7/x8WFZz0I4/Io01XA2aRB+VX+4v6607enEH0/FHn6dejHTP0cnFp4m/efR9RlUCL8GouGkKKVuznvmFp75HfxoFF49y0KjM1/j6weevBemwQDrW3a6FqRnFPf44OXMuly1aOpyUDAdzdb0ZyDcjLUaHpOplm1dsqy+EC389qscvvCr+uPGCr7HH0/FFn49F8XN8s/x0cOTp/88uj6DCuHXQJJ4J9AhxOzwG7xaNjH8xl4fC7/eY/W84p7ir9HMWR+IR7N1kRBj8WdyuLn/jJm99VDDr7RSl5U0k5O/NH+YZ31F8MeTsYTfbmuYb3/qzPpyYDR9vf3n0fUZVAi/BuZveD8Xy8KvdpZrfP2i8LvTmd+eyQUTzmf1zmYPX27wZ0q48V3tHLmVEqShh9/O41U9+FmyP6ZX+S8CfzyVpeG3Hc1Mjh6YXV+OTFjfQP95dH0GFcKvgZh4Z7/Vz/xtD7Fb5ziP9zO9qa+Phd/+grLgnuPHknxy5e55TtQj6M/bDHrEuwnhJnq1MywmFn6lraUsy/m3OovVbvyRBUvCbzD43l7Grc56gvoq/WeV+gyrsHL49V+pe9QN2z7xRjMXJy0KPfPD7/05I/8Eb8RfSTPn9dHwK3ILwMGbkD+OpJuoz7xJepI/jeFGm7nrLzTUvwOkEA2/4lu+jt1EP2HWF39kQfiC49D47NQ+7x05+CMMPf7wFOs/K9RnWAX+vLEB7mOrgz42dq9f0p7OAd5tKSstsZ8Q/AEau/dH5qBv3hB+DWB+HfSxsT/9Gqnd2/CMN7Dp4cZ3Q/zZ95KFHvwBGvvzx7FA37wh/BrA/DroY2OP+o2WpQNbLrSgMrmgiWCzGPwBGnv0x5FA37wh/BrA/DroYwP9QAN/gAb+2Bb0zRvCrwHMr4M+NtAPNPAHaOCPbUHfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8IfwawPw66GMD/UADf4AG/tgW9M0bc/i9XC40Go1Go9FoNFo2zRR+z8xS8c4C+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffNmo/DbSl0WUhSVNEs/WQaExdO/f1MVUhTXVtbSbvkhnwjFwYZfv95b91bWYwfp/nJeXy3vod37HLuP75mJP9paSveYtrWURSlDi+CPc7BsfGqkKsLHv63LUe3x1Z+zkFSfPf3nUf0PdNYPv00lnhcQDgAAIABJREFURVFKXVeHL3xe8SLfv6mGhr8a/aAGJ/zamOoX94vur+7/98HK/f88CDfPxR9+SynLQdh1wi/+OA/zx6fueN/LSxeEh8e/rcvDjldzCdVnrf88sv+Bzsrht5Gq71DNGcNv7Ps3UjmzMLdidEB/E35thGb2wmNPxF9tLaXryRP006MSCr91Xd0HzFH4xR9nYv74NGUc1gi/Q/z9T+s/9L89sd2e3xMcNFU83/ef/Oy+xHHEehLSJ7js01RSVM3t8arp9RkWDHfZf6pn1QyX7qYnFuFlJ3fm4/qZnrQ1xT94KVtlYv7yedKzLB5muuUirr/rbd/vYHZjCcHw2zZS9R4ZHl/8cSpmj0/epxF+Q0z0jfWfzfsfzIHwa2BR+B0NSveAd8R64tOnqcbL9qPi2lS3ga7fW9br0w1+02X/7nm9ztOw7BZv9f13H37lvq/Ttx8s6i93GdN3cjGDhJO58fFx9WdZz0I4/Io01XA2aTD44o/TYA+/gW0PwxOTA1+zEiM0ORHsP4/uf6BC+DWwNPw2dTkw9JnCr2fZJ3Bm3NbltVAMBkDfstC1YHT6xcJr5P1zCL891xOF0cxZir+G4bkopW4MMwuBcDP28lDz4bHqPw4zSUvRwm+/iuILv/jjHNjCb8pF65GVqIMTnZxw+8+j+x+oEH4NLNv24BaLacE/CsGr0SctMfx6PTUsHpHwGnv/nMLvldHM2RJ/Wfppcri5/4yZvfVQw6+0UpeVNJOTS/xxFizht9salhC6TjDOh0gKT0N9Ht3/QIXwa2B2cfFdsOSdzTwG+uDsYVH4nTHzG90/lV/4nV4wMc9f7jaQWaSEG9/dBpRbKUE6sf7V1qVU9eBn+ONULA2/7WhmUsfdtnImUurzqP88uv+BCuHXwJLicu5bnUW+byz89stsi/fsxvR2Hu9ninccfr17mhO/b/BWVLcZ8kj/TQg34+Lt2XYCi4meXLa1lGU541ZnY/BH3iwZn+YE394HZ52Zj9VnX/9Zpf/BKqwcfv1X6h51w3Yw3Knf/zw3sU79Iw3jC9608CtyC8CL79agvL+IszWikmZPM7++bRszb5I+mlkLec8YbrSZO99N8s+6Z9BKysrKdPkaf5yF+eOTU1sHbbQtxfPzMxK8oFvtPyv0P1gF/ryxAe5jq4M+NnavX9KezgHebSfTK8ohDfwBGrv3R+agb94Qfg1gfh30sbE//Rqp3dvwjDew6eHG9wddon+4A0LgD9DYnz+OBfrmDeHXAObXQR8be9RvtCwd2HKhBZXJBU0Em8XgD9DYoz+OBPrmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65g3h1wDm10EfG+gHGvgDNPDHtqBv3pjD7+VyodFoNBqNRqPRsmmm8Htmlop3FtDHBvqBBv4ADfyxLeibN4RfA5hfB31soB9o4A/QwB/bgr55Q/g1gPl10McG+oEG/gAN/LEt6Js3hF8DmF8HfWygH2jgD9DAH9uCvnlD+DWA+XXQxwb6gQb+AA38sS3omzeEXwOYXwd9bKAfaOAP0MAf24K+eUP4NYD5ddDHBvqBBv4ADfyxLeibNxuF31bqspCiqKRZ+skyICxe7PufXR9Iwa9f7517K+s28ByfvxqpBq8tquUObKrje3jPTPzR1lK6x7StpSxKGVqkO27XVtbiumet+oQ/nsuS8amty1Ft8dWHuH/OwbLxP15/H9U/z8764beppChKqevq8AfHK17s+59dH0hmqt+16GmBVfVX9/r7y7tCPA3PaRBunos//JZSloOw64TfphoOqB4/rVif8MdzWTQ+jZ8sVTGsFwn+ORHz9Y3X30f2z7OzcvhtpOoPSHP8gzMVL/b9z64PzCE0sxcea+b7a1xsISdC4beuq/uAOgq/jVTOLPBtML09fp76dHTmj08uXfi6h7OYf86FXV+3/tI/H8l2e35PcHBU8WLf/8T6BJd1mkqKqrk9XjX98s6wILjL/kMN+zPr4dLStDCHl5XcM/PrZ3pSOPQX18Swukn4nW65iOvvhnXf71g++3xmguG3baTqj+kw/E48cT8WkxOqRfUJf+wJ0/gkMt0yM8c/J8Csrzj1d/P+CUMIvwYIvzo+fZpqvIwz7fzdQNfvPesDcDf4TZeBuuf1Ok7Dshvu1PffffiV+77O2H7dJH/Ztj2kFOvx8XH1d2eWYA7h8CvSVNc+4IbfUSi+969NBlf88VSWjk/DyYFyvFk83T8nwB5+nfr76P55cgi/Bgi/Ov6ZS2cmNjAz1dbltRAMBsC2ltK7laQvDrHwGnn/HMJvz/VEITjrEvXXChdNBMLN+PMMNZ/uIWzr8rR7Bq1o4bdfRfGF36YuByeIjw+/+OMx2MOZM9kwxz8nwKavp/4+un+eHMKvAcKvTvBq9ElLDL9ezYbFIRJeY++fU/i94s6c3Yj4q5vdMe7VSw43958xs7ceaviVVuqykmZyculuc5kGzu7Hjwy/+GML1liWHz1vjn9OgEVfb/19dP88OYRfA4RfHX1w9rAo/M6Y+Y29f4bhN3jBieKvdjSzYCAl3PjuNrDSrdbOTqx/tXUpVT34me+CSe9qijwu/OKPzVgt/E6W4gePh/xzApbqG6y/j+6fJ4fwa4DwqzP7Vl2x8Ntf8LV4z27s1jzO4/1M8Y7Db/CCtYC/koLvbYY84s+EcDOeyfNsO4HFRE8u21rKspx3q7MerT7hjyxYfU+qzPDPCViib6z+rtI/IYmVw6//St2j3golGO4iy+rn1UfEq4Hnald/+BW5BeDFd2tQ3l/E2RpRSbOnmV/fto3Ydxv5y9EudMW9MdxoM3eTm+g/8eQid1JWVqbLq84xGh/4tPqEP7JgjfFpmrs0/5yL+fqm1N8V+ickwZ83NsB9bHXQx8bu9Uva0znAu+3EeMeJE4M/QGP3/sgc9M0bwq8BzK+DPjb2p18j9X1N3bMkFwk3vv3J0T/cASHwB2jszx/HAn3zhvBrAPProI+NPeo3WpaeLEfHb3s0uaCJYLMY/AEae/THkUDfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8IfwawPw66GMD/UADf4AG/tgW9M0bwq8BzK+DPjbQDzTwB2jgj21B37wh/BrA/DroYwP9QAN/gAb+2Bb0zRtz+L1cLjQajUaj0Wg0WjbNFH7PzFLxzgL62EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV982aj8NtKXRZSFJU0Sz9ZBoTF075/I1VRSNG36rgKURxs+PXrvXVvZd0GnjP1X1uXo9f6X59GUx2/j++ZiT/aWkq3prS1lEUp90OcUn/Wqd/447ksG5+c+rKhP3LHr+8a/esx9fvsrB9+m0qKopS6rg7fObziqd+/M/W9P3Qd5ajmJfzamOp3LYraCVOk/7V1udoJF+HmufjDbyllOQi7o/CbUH9WrN/447ksHZ/ufnD/H3v9uQjVZ1P/emD9Pjsrh99Gqv6ANcfvHFPx5n//piqkKGs5Yvwl/NoIzeyFa1/cfxTP4xAKv3Vd3QfcyczvmHH9OVf9Pjqzx6e2ltL92eh5+GNIyvg2r39Rvx/Jdnt+T9A5VPEIv8qZ8XBpaDowdzNG1zbSxj2zlk5nR7/g65tKiqq5PV41/fLS8DO42wqe52H/4JXol02K53TLxfg9fMfXDeu+33Hc1Y8tCYbftpGq98is8Dt6YIH38ceemD0+BQOxxz8nGN9jzA+/owd0/Qi/m0P4NWAPv2fb9jANm25xaKrxsv748Xj4VV/fVLeBtN871Qfg7hhMtxV0z3uOj73+6vd1xvaLa8VzGCwsJ16T97gHl/6jufqNj49nWRWSCYdfkaa69jE1/Cr1Z436jT+eyvzxyfWDb3JAe/25iIcnQ/96RP0+OYRfA7bwe/yLBuJ7osQJr41UbqFV9yzOfP3gmLR1eX3dYID1LftdC9gzTrbj/vLNnA0fTzv5WlxAA+Fm/HmGx2SqJTMZy9HCb7/KEQ6/kfqzYfjFH49h0fg0PLkuSqkbZn5D6OHJ2L8eUb9PDuHXgCX8dkvv4eXIIzA7/I4K77Alht/Y62Ph13vMfAP2Y0jpnMGZ6dT+Z+mnyeHm/jNm9tZDDb/SSl1W0gTCb7T+PDT84o8tWGNbXvB5JxjfY2j6mvvXI+r3ySH8GlhaXLrAcuzgK7I0/Gq6GF+/KPzudOa3p786eOHMjGlbR0q4cY7JaD/2wW/1tzV6+O2ObVVP+0RS/XlU+MUfm7HaNSm+Y3CC8T1GSN9V+tcj6vfJIfwaWFJczhJ8RZZse4jdyst5vJ/pTX19LPz2y0jBPcePZdMLKkRu+k1m1m4z6JHXJ4Sb8eDp2ZYCi4mFX2lrKctyEoiT6o/mH/yRBdbwq96q7gTjewyfvqv0r5THuzfz129IYuXw679S96hhL3xBV+j7OzfADl7xfAzmh9/7c4Kb+kdbGypp5rw+Gn5FJsfoifupgn/EIPj54v3PnVnz34PdFm60mTvfTdrZs7aMaPgVd/k1Vn8S6zf+yIL545NTHybF4Vzje4zg3XgW96+V6jckwZ83NsB9bHXQx8bu9Uva0znAuy3l2Hc82RL8ARq790fmoG/eEH4NYH4d9LGxP/0aqd3bII03cOrhxrc/OfqHOyAE/gCN/fnjWKBv3hB+DWB+HfSxsUf9RsvSk+Xo+J0xJhc0EWwWgz9AY4/+OBLomzeEXwOYXwd9bKAfaOAP0MAf24K+eUP4NYD5ddDHBvqBBv4ADfyxLeibN4RfA5hfB31soB9o4A/QwB/bgr55Q/g1gPl10McG+oEG/gAN/LEt6Js3hF8DmF8HfWygH2jgD9DAH9uCvnljDr+Xy4VGo9FoNBqNRsummcLvmVkq3llAHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65g3h1wDm10EfG+gHGvgDNPDHtqBv3hB+DWB+HfSxgX6ggT9AA39sC/rmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb95sFH5bqctCiqKSZukny4CweOHv39alFEVxb9VxFaI42PDr13vr3sq6HT2jqQaPl7UMH13Tf937HLuP75mJP9paSveYtrWURSlji8Tq8zr1G388l1XGJ6e+xB4/Ez590+rrY/of6KwffptKiqKUuq4Of/C84s36/o1URSFHzb+EXxtT/a5FUTFMUw0Db+z5Nv8Rbp6LP/yWUpaDsOuG31h9WrF+44/nsmR8autSrS+xx89EfHzz1NcH9j/QWTn8NlL1B6w5/sGbijf3+3fh5KhnzoRfG6GZvfDY00jlzvL1xdRrsWP77+iEwm9dV/djOgq/sfp0rvp9dJaMT4TfdOLjm1tf6X97Yrs9vyc4eKp4Kd/fuyR5HEL6BJflm0qKqrk9XjX98s9QI3fZf6hx91jVdGfc3eNTfcPbAvrXj5482TrwKPyD13Qrw/1h13N3rfyrb3P9N91yEdfffW/f7yCALyEYfttGqt4joWMcq0+L6jf+2BNLxifCbzrR8KTV1036H8yB8Gtgafgdhq8jF3WfPk01XoYfLdM31U2Tfu9UH4A7nabL+N3zep2nYXm8DSDy/rsPv3Lf1+nbTzb8rLdZ4ul3Ws1/CWF7fHxc/Zl5thAOvyJNde0DDw2/sd+BPx7J4vA7PPGIXTPwpNq4B1Imd4LeJfw+HcKvAfPMb8Iezpzxz1w6A/FwcB5o1tbltbAOBsC2ltK7VNQPprHwGnn/HMJvz/VEYTRzdv2sTV0OTgA83+mG0X+BcDP+dUPNp3vgmElajhZ++1WUPYZf/PEY7ONTZKUp+vixSd324PUv4ffpEH4N2IvLjOdlSPBq9ElLDL9erYYDaiS8xt4/p/B7ZTRz1gfi0eeNXNRm8V9yuLn/jJm99VDDr7RSl5U0WYRf/LEFDxmfDjx+xUgKTyF90PXpEH4NrFZcDnrmrA/OHhaF3xkzv9E9rvmF39EFbb4L4ryz5c7rl36/lHDjaD7ab33wW/1tTax/tXUpVb3z8Is/NmON8cndljL38SOTXJ999ZXw+3QIvwbWWlY66szG7Ft1xcJvv8y2eM9ubJnfebyfKd5x+PXuaZ55q7OJ/24z5BH/JoSb8UyeZ9sJLCZ6ctnWUpbl+uEXf2TBOhdkK+NT7PGDk3qrM68+hN+ns3L49V+pG77VUt4Ew11kWX342JEnNlL/SMP4gjct/IrcAvDiuzUo7y/ibI2opNnTzK9v28bksznfbzzNluY/Y7jRZu58N8k/6srH1qSsrHQzqeH6E6tP3vqNP7Jg/vg0nXmfXlN7nvErRoq+KXcymd3/YBX488YGuI+tDvrY2L1+SXs6B3i3nRx79WNL8Ado7N4fmYO+eUP4NYD5ddDHxv70a6S+r6l7tlREwo3vD25E/3AHhMAfoLE/fxwL9M0bwq8BzK+DPjb2qN9oWTqw5UILKpMLmgg2i8EfoLFHfxwJ9M0bwq8BzK+DPjbQDzTwB2jgj21B37wh/BrA/DroYwP9QAN/gAb+2Bb0zRvCrwHMr4M+NtAPNPAHaOCPbUHfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8MYffy+VCo9FoNBqNRqNl00zh98wsFe8soI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvNgq/rdRlIUVRSbP0k2VAWLyU798/p5S63ebzPRuKgw2/fr1v7q0cGaiRavBYUY0d2Nbl6LXu43NoquP38T0z8UdbS+ke07aWclRjdH8MXmiuT/jjuSwdn7rjdm1lLePDn+qf4+PX19q/Hle/z8764bepugNaV4cvfF7xEr9/W5dSlKUzMB0Lwq+NqX7XghkpqPeHu0JaBg3WPb60fhJunos//JZSloOaMgq/6f5Yoz7hj+eyZHxqqmHgdevN3PpybEL1eXn/emz9Pjsrh99Gqr5DNWcMv6nfv5GqKKVu+3+3/qTPgfBrIzSzN6fYjQczl67YnnXwyp1Q+K3r6n5MJzO/Y/z+OEd9OjrzxyfP8e7D8iz/nIOU8c3av6jf27Hdnt9Tht8Byvdvqv5s+tiDS/jMeLi0M/3+4WU398xYOp2d4hB8fVNJUTW3x6vGt/Tkbit4nof9g9e8wUYtnpFg5HnBZMvFWB/f8XXDuu93UMCXEAy/bSNVf8wXhN/l9Ql/7InZ49PkZ/djETrhJvzqWPvXuvUbhhB+DSwKv20t5e3n5wy/w7Dpdu57YfA9Hg+/6uub6jaQ9nun+gDcDa7TbQXd857jY6+/+n2dSfu9/Mtmw5MDU6hIGCxd/cbHh5kLC+HwK9JU1z6mDpAef6xZn/DHU1kUfkcnTff66C81bHvQsfavjev3ySH8Gpgfft1ifs7wGw6vHj3UPYszXz84Jt2eq1ra4TEZFabbGzxtX1XcX9qszIyLLpdv+g3M7I2eNDgmUy3buuSijYVo4bdf5QiHX58/Vq5P+OOpLA2/TV0OJihC4fccF7Vr6OHJ2r8eUL9PDuHXgOnMuvsB4dcz2+AueaqFeM7rY+FXOWHZXfi9EpqZ7mYHErxl6afJ4eb+M2b21kMNv9JKXVbSBMKv1x9r1yf88VSWbXtwl9n9J//J9eXAaPpa+9dD6vfJIfwamFtcRntR3XbAfVPLwq/W4Y2vXxR+dzrz2+O5IKUdzdwkvH6p91LCjXNMJn2AWYvF6OG380FVT/tEyB+r1yf88VRmh1/fBbWe1bBZ9eXAhPS19q+H1e+TQ/g1sPSCt8GTmPkddd60W3ndHu9nelNfHwu//QVlwT3Hj2XJBRXzBqbAnr3bDHrEvwnhZjyTd2y/P5pY+JW2ltK5ndJ8f3ieiz+yYMn4pN/qjOA7xKevtX+tUr8hiZXDr/9K3aN2lvAFXanf/9jFfn74vT8nOOs02tpQSTPn9dHwK3ILwDuYkQ/+EYPg53M+++SK+qk23vMEY7jRZu4mN2k/6KrHI4iGX3GXT2P+cNkm/OKPx7BsfHKeMy7WM/1zbIJ341ncv1aq35AEf97YAPex1UEfG7vXL2lP5wDvthRmL5aCP0Bj9/7IHPTNG8KvAcyvgz429qdfI/V9Td2zxSQSbnw3zF/whzugA3+Axv78cSzQN28IvwYwvw762NijfqNl6clydPzOGL6LPgg2y8AfoLFHfxwJ9M0bwq8BzK+DPjbQDzTwB2jgj21B37wh/BrA/DroYwP9QAN/gAb+2Bb0zRvCrwHMr4M+NtAPNPAHaOCPbUHfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8MYffy+VCo9FoNBqNRqNl00zh98wsFe8soI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN28IvwYwvw762EA/0MAfoIE/tgV984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvNgq/rdRlIUVRSbP0k2VAWLzw92/rUoqiGLWybrf+qE+B4mDDr1/vLc0/Wv9rpBr5r5Sl9muq4/fxPTPxR1tLWRRSVI3zs+Exdo5/1Ti/Yr36hD+ey+rjU+8vT6tOeJD9+q7Rvx5Tv8/O+uG3qboDUleHL3xe8SLfv63LSYc4KoRfG1P9rkVR84/qv+71w2Lb1qUUZS1L6ifh5rn4w28pZTkYEEfhtzv+d/t0A+nEDyvVJ/zxXB4yPk1Ors5DqD6b+tcD6/fZWTn8NlL1B6w5Y/iNf3/CL6QSmtkL2yfmv64Yj15/gn56VELht64rZ7YuHE6aqhgNnmeqT0fnEeNTU0VOxg9Myvg2r39Rvx/Jdnt+T3BQVPEIv8qZ8XDpZjowdzNG1zY6q3XPrKXT2TnzDb6+qaSomtvjVdMvLw0/g7ut4Hke9g9eriYBNP/dvu90ZkJnuuVi/B6+4+uGdd/vOO7Wny0Jht+2kar3yEPDL/7YE5uPT9GT8WOzfvgdvXCD+g1DCL8GFheXIhDODkZw2X4wGLvFwZ1JGD8eD7/q65vqNpD2x6EPwF0BmW4r6J73HB97/TXcdxddPgt87qsO5r16k/e4B5f+97r6jY/PdBkP0gmHX5GmuvYxNfwGlmXXqk/446lsPT6dfck9Hp4M/esR9fvkEH4NLCkuzpPSZ/IyJL4nSpzw2kjlDtTqnsWZrx8ck3vhHgywbS1lylLTg4j7SymAKTMHg5OBRQTCzfjzDI/JVMszrYSsjRZ++1WOcPhNuSjZWJ/wx1PZdnx6Xl3cC3p4MvavR9Tvk0P4NWAvLjOelyGzw2/wauLE8Bt7fSz8eo+Fb8B+DCmdMzgzPWvP2MILVpLDzf1nzOythxp+pZW6rKQJhN9u60/CcbfUJ/zxVLYcn84+6yui62vuX4+o3yeH8GtgjeLyzGX1rVkWfrWObHz9ovC705nfnlDx830Xnz6Wq7VTwo3z+0f7sWNbN0BFD7+dx6t6enzH+wZ1TPUJfzyV7cYn9pqKhPVdpX89on6fHMKvAXNxuc5UHrWIzN/2ELuVl/N4P9Ob+vpY+O2XoYJ7jh/LkgsqBg8EB63J9/MW2YSL/RLCzXgmz7MtBRYTC7/S1lKW5SQQJ88UheoT/siCzcanE4ztKfj0XaV/idjqNySxcvj1X6l71Gn58AVd4e/vzmwceWJjfvi9Pyd4UcBoa0MlzZzXR8OvyK3A7OCCxOAfMQh+vpT+594k3VM4jeFGm7nz3eT97MunS4mGX3GXX91jP61DSfUJf2TBNuMTs749wbvxLO5fK9VvSII/b2yA+9jqoI+N3euXtKdzgHeJjsF0KfgDNHbvj8xB37wh/BrA/DroY2N/+jVS39fUPVtMIuHGtz/55PcKtYA/QGN//jgW6Js3hF8DmF8HfWzsUb/RsvRkOTp+Z4zJBU0Em8XgD9DYoz+OBPrmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65g3h1wDm10EfG+gHGvgDNPDHtqBv3pjD7+VyodFoNBqNRqPRsmmm8Htmlop3FtDHBvqBBv4ADfyxLeibN4RfA5hfB31soB9o4A/QwB/bgr55Q/g1gPl10McG+oEG/gAN/LEt6Js3hF8DmF8HfWygH2jgD9DAH9uCvnlD+DWA+XXQxwb6gQb+AA38sS3omzeEXwOYXwd9bKAfaOAP0MAf24K+eUP4NYD5ddDHBvqBBv4ADfyxLeibNxuF31bqspCiqKRZ+skyICxe7Pv3j3etrNvtPuQToTjY8Os39s7UP41Ug8eKyufAdfpnUx2/j++ZiT/aWkr3mLe1lEUp4xITPv5tXY685fdPGvjjuSwfn4bPcb2T+vrjs0TfeP96XP0+O+uH36bqOkxdHf7geMWLfv/O3EcNvEMIvzam+l2LXjCWtarUAAAaKElEQVSQdI/fH/Z4bcX+Sbh5Lv7wW0pZDgKLG35nHf/OP0vzL/54LsvGp462LqUoy+mJ04nG9xgWfa9PdvrXY+v32Vk5/DZS9QekOf7BmYoX//5NZZtNyQnCr43QzN4c+zRVIUVZS1c+z9U/j04o/NZ1dR8wR+F37vHvBuMznKgfkSXj0/15pdRt/+/c15+D5fr2xPsX9Xs7ttvze4KDo4rn/f7umd2xCc1cVs1waWe6rNbNGF3breMPXz96svMc5fVNJUXV3B6vGt/Snrut4Hke9hdXVxOdcfEcPbDgu023XIx/h+/4un73/Q4C1hKC4bdtpOqPuXfbg6Qd/9Brwy/AHzti/vjUP9RP0LjhN+31Z2GpvjcS+te69RuGEH4NzDf/tZg01715gfB3FILL9oPv7HZud2Z8/Hg8/Kqvb6rbQNrvveoDcDe4TrcVdM97jo+9/moH3omeRSlbbNbon5PfcQ8u/Udz9RsfH2YWLYTDr0hTXfvYgvA7PHk0HRv88VQWhbO2lvL2c8KvhunkIql/bVy/Tw7h18Bs8/fBZRDWnhmutiY88zv40Si8eortaPA2vn5wTLo9bbW0wwF2VPhvb2Da92gh7i/fzFlP5KKIDcPv+PMMj8lUy7YuT7MNaG208NuvcphmfqN7zCPgj6eydGXyHrYIvxrmmV+1fz2gfp8cwq+BxTO/rfuzY26FmB1+h7Oao5YYfmOvj4XfnW1VSemcoZOnbnZBWVV4aPi9/4yZvfVQw6+0UpeVNKbwO+N5Sa/FH49k9vg02UJG+NWwh9/w8x5Sv08O4dfAsvCrzXwci2XhV9PC+PpF4XenM789/dW/g+/cBeKIpx4Vfp1jMtqPbbyV1tnRw2/ng6peIfzO2GOuvwf+eCRzx6eJ9sPmeuAE43uM1cKvo+3D6vfJIfwaWGL+e+jqnzbvAqacmL/tIe1WXrfHJ9tIIq+Phd/+grLgnuPHktI53c+XVDi7F4b7520GPdJ/E8LNeCbvuCd6zyAWfqWtpfTdrkoksT4H9hzijyywhzNmfjXW0Xfcv1ap35DEyuHXf6XuUS/qCl/QpX//8N0MjsX88Ht/TnDWYbS1oZJmzuuj4VfkFoB3cHyCf8Qg+Pmczz65oj6xfxrDjTZzN7nJ+8H7wJZEw6+4y6ex4z993L8dEX/kwNLx6Y4bfs81vseYr2+sf61UvyEJ/ryxAe5jq4M+NnavX9KezgHebSnn+aMva4M/QGP3/sgc9M0bwq8BzK+DPjb2p18j9X1N3bPFJBJuPPuTl/zhDujAH6CxP38cC/TNG8KvAcyvgz429qjfaFl6shwdvzOG76Iags0y8Ado7NEfRwJ984bwawDz66CPDfQDDfwBGvhjW9A3bwi/BjC/DvrYQD/QwB+ggT+2BX3zhvBrAPProI8N9AMN/AEa+GNb0DdvCL8GML8O+thAP9DAH6CBP7YFffOG8GsA8+ugjw30Aw38ARr4Y1vQN2/M4fdyudBoNBqNRqPRaNk0U/g9M0vFOwvoYwP9QAN/gAb+2Bb0zRvCrwHMr4M+NtAPNPAHaOCPbUHfvCH8GsD8OuhjA/1AA3+ABv7YFvTNG8KvAcyvgz420A808Ado4I9tQd+8IfwawPw66GMD/UADf4AG/tgW9M0bwq8BzK+DPjbQDzTwB2jgj21B37wh/BrA/DroYwP9QAN/gAb+2Bb0zZuNwm8rdVlIUVTSLP1kGRAWL/D921rKopDC06oDCkVxsOHXr/fWvZV1G3jOtP+1dTn2nsF4TXX8Pr5nJv7o68vwmLa1lEUpQ4t0x+3aylra0a/AH0dh9fHpZONXDJ++af3nMfUZdNYPv00lRVFKXVeHL3xe8eZ+f8/gdBQIvzam+l2LplYQZ/mvkcowcBFunos//JZSloN64tSXphoG3pif8EfOPGR8OvD4FSM+vnn6zwPrM+isHH4bqfoD2pwx/M7//k113LM7wq+N0Mxe2C5z/deFn+nMMeRAKPzWdXU/pqNw0kjlBpV+MPZaAH/kzCPGpyOPXzHi45vbf6jPe2K7Pb+nDL8DUr5/NMzkTWjmsmq6M9puaWc68IaXZfvXj548WboNvr6ppKia2+NV0y8/DT+Du63geR72D17TpWovyf6bM2sz3XIxfg/f8XX97fsdFPglBMNv20jVe2R4jCeeuB8L/+os/siZzceng49fMaLhSes/m9RnmAPh14C1uLR1mRZkMiW4bB9chp3OJPiWabXwq76+qW4Dab+3qg/A3eA6XQbunvccH3v9Ndx3F93+4P/cw5MDU6hICFOufuPjw8yGhXD4FWmqax9zw+8oFN/9P7QS/jgGW49PRx+/YoT0Teo/j6jPoEL4NWArLsffzxOe+R38aBRePcuyo7Nf4+sHx+ReuAcDbFtLOTlmzztOcX8ps3YzltUWL1sGws341w2PyVTLti5Pu2xqRQu//SqHL/w2dTk4AfUds9svxB8Zs+34dPzxK0bqtgevfx9Rn0GF8GvAUlzOcNY8O/wGryZODL+x18fCr/eYaeFgW1I6Z3BmOrX/Wfppcri5/4yZvfVQw6+0UpeVNJOTP3fbTCTE4I9s2XJ8OsP4FSMpPIV0fkR9BhXCr4HlxaUr+Ecv6svCr7bHyfj6ReF3pzO/PaELluYU16WDWEq48d1tgFv5rIIefjuPV/XgZ749mt7VjgH4I1u2G5/OMX7FSK7Pvv7ziPoMKoRfA4uLywm0EVmy7SG2zOM83s/0pr4+Fn77C8qCe44fS0rnDH6+JI8FBrHbDHrk9QnhZjyT59mWAouJhV9paynL0nyrM/yRJ5uNTycZv2Kk3urMe5Jgqc+wCiuHX/+VuuFb6eRN+IIu7fufx9Dzw+/9OSP9ho+PtjZU0sx5fTT8itwCsPduE48l+EcMgp8v5r/p4+Gr/JeHG23mbnIT9ydrnDPR8Cv9TKpyN5PxNCz+OBDbjE/nGb9ipOibcieT2fUZVoE/b2yA+9jqoI+N3euXtKdzgHdbCoPpUvAHaOzeH5mDvnlD+DWA+XXQx8b+9Gukvq+pe5bMI+HGtz/55PcKtYA/QGN//jgW6Js3hF8DmF8HfWzsUb/RsvRkOTp+Z4zJBU0Em8XgD9DYoz+OBPrmDeHXAObXQR8b6Aca+AM08Me2oG/eEH4NYH4d9LGBfqCBP0ADf2wL+uYN4dcA5tdBHxvoBxr4AzTwx7agb94Qfg1gfh30sYF+oIE/QAN/bAv65g3h1wDm10EfG+gHGvgDNPDHtqBv3pjD7+VyodFoNBqNRqPRsmmm8Htmlop3FtDHBvqBBv4ADfyxLeibN4RfA5hfB31soB9o4A/QwB/bgr55Q/g1gPl10McG+oEG/gAN/LEt6Js3hF8DmF8HfWygH2jgD9DAH9uCvnlD+DWA+XXQxwb6gQb+AA38sS3omzeEXwOYXwd9bKAfaOAP0MAf24K+eUP4NYD5ddDHBvqBBv4ADfyxLeibNxuF31bqspCiqKRZ+skyICye9v0bqYpCilsrpW63/ZzPguJgw69f7617K0cGcvxVDRzY1lIW49f2rVrQUZvq+H18z0z80R/fyTG/15i2LsfH3nPgu+N6bWUtS8sT/nguy8an4eO++pLy+nPg09fevx5Xv8/O+uG3qbpAV1eH7xxe8dTv3xWNYTFp69I0wOwZwq+NqX7XQSdY6brH7w93hXQ6eA1fMg5HcyDcPBd/+C2lLAfHUz2+nT+Gdmqq4YAc85sO/ngu88cnkWjNONH4HiM+vs3tX4+t32dn5fDbSNV3iOb4nWMqXuz7TzvDkXUi/NoIzezNySLjYht4nGmDLAmF37qu7gOmOji6J+ONVO5z+7DD4Jod88enWD041/geIz6+2fsX9Xs7ttvze4LOoYoX+P7dskhv9oQzu4wJzVxWzXBpZ9rxw8tC7pmxdDo7xSH4+qaSompuj1dNv3w3/AzutoLnedg/eM1bilaL5+wwPd1yMdbHd3zd3+/7HcftA1sSDL9tI1V/zLXw6z42qVn3Y5XmEfyxJ+aPT576Ouv15yIanlboX+vWbxhC+DWwJPzeHzv+Xp3gsv2gILid2z2T9S0TaeFXff1V97Jub3uz+gDcDa7TZd7uec/xsddfw31fUfPoJ1fmLTcJxdzVb3x8ptuAIJ1w+BVpqmsf84Tf4clhOX7g7ofbwDojELngj6cyf3y6zkw2w72lgROnE4zvMUL6rte/Nq7fJ4fwa8A88zsIY0ckPPM7+NEovHqWhUaDt/H1g2NyLxyDAbatpUzZqvIg4v7STqBiF6Ws8L0C4Wb8O4fHZPqebV2ybLcQLfz2qxwp2x5u+l/7UjNanVo//OKPxzB7fOpPrAeBKnjyf4LxPUbqtodl/esB9fvkEH4NLDuz9u35PeaeutnhN3g1a2L4jb0+Fn6tS4Erk9I5Q4NTN/sQ9tUqswbJ4eb+M2b21kMNv9JKXVbSxC6IGR7D/oRq5AvDIIs/nsrimd/W/Znn+J9gfI+RFJ4W9q+H1O+TQ/g1sOzMWpvZPBbLwq+mhfH1i8LvTmd+ezwnT+N95d4XrbPXPCXcOMdktB87aesGhNDDb+eDqk7oE5OlWPd3Lqzj+OOprDI54w3Eodefi+T6PLN/Pax+nxzCr4GlxWWyJ/WgOs3f9pB2K6/b45NlusjrY+E3dHyedIad0jndzxcvnBLvm7cZ9IgvE8LNeCYvMJDCImLhV9payrKM3upsOIgm3eoMf2TB4m157jUUvvp3gvE9Ruqtzub0r1XqNySxcvj1X6l7tmV9/fu7f+TiuCaeH37vzxnpN3x8tLWhkmbO66PhV2RyfJ64tBT8IwbBz+d6y3dFfeq9I5eHG23mbnIT+CdrnDPR8Cvu8un0+IT3Giozr/gjC5aNT87se6y2Hnh8j5Gi77z+tVL9hiT488YGuI+tDvrY2L1+SXs6B3i3pVDMl/L/t3dGSa6CQBTNulwQ63E1rOVVuRjeh3EiCA3aooLnVPkxE50kl2tzBXTwB0g83h+Ng75tQ/hVgPll0EfH8/SzbvzNqUemxDPhJnZzJ8+qPAz+AInn+aMv0LdtCL8KML8M+uh4on7etPRmOjr/ZIzNDU0Em8PgD5B4oj96An3bhvCrAPPLoI8O9AMJ/AES+KMu6Ns2hF8FmF8GfXSgH0jgD5DAH3VB37Yh/CrA/DLoowP9QAJ/gAT+qAv6tg3hVwHml0EfHegHEvgDJPBHXdC3bQi/CjC/DProQD+QwB8ggT/qgr5tow6///79Y2NjY2NjY2NjY2tmU4XfN3NUvLeAPjrQDyTwB0jgj7qgb9sQfhVgfhn00YF+IIE/QAJ/1AV924bwqwDzy6CPDvQDCfwBEvijLujbNoRfBZhfBn10oB9I4A+QwB91Qd+2IfwqwPwy6KMD/UACf4AE/qgL+rYN4VcB5pdBHx3oBxL4AyTwR13Qt20Ivwowvwz66EA/kMAfIIE/6oK+bVMp/E5uHD7u8zHOHv1kDZAWT/r+y2vfzfSrEMVBR1y/wD+fjxvGKbFP6vzz/8b2+DKs6f8cfzIbf0yjG8KaMo1u+AzOb+K0P6Zx8LyFP9rlSP9U1v7v6N9z3KfvOfX77Zwffq1xn8/gxtF0f3JExRO//2zan1nDn/uC8Ktjq9+36EkXTNnzzzpzUsEk3NxLPPwObhhWYTcMvxl/TONw2gU5/riX/f1TQfu/qH/PcY++59Xvt3Ny+LXOLA1m+z85tuJlvv80uiH8Xcc6EX51pEb20rUzf/5Z0/dsw5tIhd9xNL/O0Qu/eX+cGX7hXnb3Ty7X/u/q33PcoS/1+zzqrfl9wckhihf7/slAHE5L9kFq5NLY+ep1nrbZfvd5xOi7DaObNsd7Owf7CMdb4z7G/r1u7DJ9tP4M4bKC+zwcL66hJgmi519Ev11sl1z47xFr3/D9Yn+DkYwjJMPvZJ1ZPJKqL1XCL/54Erv7J7ej/V/Qv+e4Xl9t/YY1hF8F+80fTlnEwlc/JKftV9/XGj/MhVe2/uv58Cseb81fR7qsvVoC8Nwm22UF8373+Djqr2VdZ269eNJ/gxvt6m9ovLd5j19wWT5aqJ/fPn0v+6lNOvw6Z823XY+E33XwLLnQSoE/buVwOCtp/xf07zmu1/fk+v1yCL8KjpjfCy9/Ru7TwOmR39WvvPD6PbnXWnidt/L4VZtM4/A9btXBxpalfC9Y7rjazvsrNnK2fj02y+AXXFW4T4Qb//Os22SrJdPsx5HC7zLLsTf8BjuVzzRED8cfd3Kof/J3Srf/C/r3HJfre3b9fjmEXwV68+/Yr0F2h1/vwmC9FYbf3PG58PuwqaaSkzNZ/KSRgyn83cHvVxxufr9jZO88xPDrJjcOxllV+N2xX9Gx+ONKqvZPHfdbpVyv78n1++UQfhWcYf6eF7AfC7/SKLjy+EPh96EjvwvL3cFF4Sb2XWIFtZCScBO0ibceu/NH/dVGDr+zx82oC7/VZwbwRzXO6J/2XVy/i+v1Pbl+vxzCrwKt+Xt/FND+ZQ+5R3kFr2+mgTLH58LvMg2VXHN8LSUnZ/LzSWvOwjXS4fF/I+gZbxaEG//ijkJ9Jrnw66bRDcNwPPx+fbAZecUfTaAOZ6n2Lz2+c+7Qt6h+QxEnh9/4nbq9LspO39CV/v7eyEbnoxr7w+9vn+RNAd7SBuPsnuOz4de5vwB8xg0/SpL/xCD5+crOv/TTNML3ORZupJG72EPeKd7HyIZft7S1v2youD6lZjzwRxOo+6fCJ3H02r/nuEvfbP2GIvj3xgp4jq0M+uh4vH5FazpXRJel8ND2o+APkHi8PxoHfduG8KsA88ugj47n6WfdGD6mz1/AKYeb2Prk7D/ugBT4AySe54++QN+2IfwqwPwy6KPjifp509KbKbf8kzE2NzQRbA6DP0Diif7oCfRtG8KvAswvgz460A8k8AdI4I+6oG/bEH4VYH4Z9NGBfiCBP0ACf9QFfduG8KsA88ugjw70Awn8ARL4oy7o2zaEXwWYXwZ9dKAfSOAPkMAfdUHftiH8KsD8MuijA/1AAn+ABP6oC/q2jTr8/vv3j42NjY2NjY2Nja2ZTRV+38xR8d4C+uhAP5DAHyCBP+qCvm1D+FWA+WXQRwf6gQT+AAn8URf0bRvCrwLML4M+OtAPJPAHSOCPuqBv2xB+FWB+GfTRgX4ggT9AAn/UBX3bhvCrAPPLoI8O9AMJ/AES+KMu6Ns2hF8FmF8GfXSgH0jgD5DAH3VB37Yh/CrA/DLoowP9QAJ/gAT+qAv6tk2l8Du5cfi4z8c4e/STNUBcPOvM5+M+y2a2Clizen0Y3VT/o94CxUFHXL/l3Pptw+g7SPZX3p+lzO/T9zn+ZDb+mEY3hG06jW74DG5tkbL6s/jMP3YP+ONe0vVX6p+D+rBu/8VfkU1RRppF7t/S589V9Rlkzg+/1swNPpruC99WvNnwP7/ORl6HE2vWhv+eIJ0anPCrI+UvyS+yv/L+3APh5l7i4XdwwxAGlt/PpfVnGgf3GYZNcN4D/riXaP0V++fZD+t6MPtAGKCJXFy9Bal/S50/V9ZnkDk5/FpnlhPKvjH8bvHNbp0JC8VSjDr0N+FXR2pkL5199/vL9ye0RCr8jqP5dZheOCn1x7JfZH9ohm39zfXPc9jy6kumH7em38GbHOn+LXX+UJ+fRL01v4Rf51xg3o0mvynsHutHemR8PbWzZ1oovDJ2s6ZBcUgeb437GPv3urGxqalwWcF9Ho53XkIxPOCvfcV1u+Qi9n5++4bvHfsbjG4cIRl+J+vM0qbr8Fvoj1+g2Rt+8ceTEPunRP88jcOqHmZGHrMX432T0jd5/lSvz7AHwq+CvHhB8VgHtb/CEQl0nZCctk9Ow25HEmLTRFL4FY+35q8jnYv8T/+5jbbTwPN+9/g46q/1urvQNLv9pZxWKyjmoX5++2ynWaGcdPh1zprvORaG35w/ptENf+2lHPnFH7dyJPz+XssPymSXRHROuj4nzp+r6zOIEH4VlC14X2nwNb/1rq7fF37T4TXS2XrTtsrjV578Fe5VB+sVrr832E4FXkS+8wo6qF3+OuGm1ES48d9r3SZbLadxeO20qRYp/C6zHLHwm/ZHGDbrhF/8cQ3qkd/VYEHkD9xWF59Cqn9Lnj9X12cQIfwqkMSbp9Zj63vCaYx+i8ju8Ju8m7gw/OaOz4XfqGfvuzgpOTm9kbMd/or6cy/F4eb3O0b2zkMMv25y42Cc3Vz8Cf7YLCG6KvzijxrsD7+pNb9bD7x91Ne5iL658+fq+gwihF8FKfH8dVPeC9s1UtHRxj44Fn6lE155/KHw+9CR34V151Tor6Q/91ISbmJPG+BRPqcgh9+5nc24+l3GH5u2WW9Hgg7+uJXd4TdWP6M1lel457b6Zs+fq+sziBB+FcTEyxn33Y86yy1byOkRvL6M9JYenwu/yw1lyTXH17L/aSJ5fxUV1r8R9Mz5WxBu/JE8nh5wJrnw66bRDcHjlvbVn0R74Y8mODryu6l/4X4v6NtLKLvnZ8+jzgi+V3Jy+I3fqdtrYybvxhcfAh5o1Gnwde5I+P3tkxx18pY2GGf3HJ8Nv85t2vDGqb3kPzEQP5/krxJ/OnW4kUbulhsN1aOKkA+/LjZ9uqf+1Am/+OMa0jccS/1zWCPiAfnto77OHQu/p9RnOAX+vbECnmMrgz46Hq9f0ZrOFUyhngr+AInH+6Nx0LdtCL8KML8M+uh4nn7Wjb859ciUeSbcxG6eefmzQjXgD5B4nj/6An3bhvCrAPPLoI+OJ+rnTUsnllxIQSV2UwjB5hj4AySe6I+eQN+2IfwqwPwy6KMD/UACf4AE/qgL+rYN4VcB5pdBHx3oBxL4AyTwR13Qt20Ivwowvwz66EA/kMAfIIE/6oK+bUP4VYD5ZdBHB/qBBP4ACfxRF/RtG8KvAswvgz460A8k8AdI4I+6oG/bHG2//6zZNX9SwQ3lAAAAAElFTkSuQmCC

This is exactly what the script is set up to do.

Ok, I understand. But then how do I avoid these repetitions? By sampling them in a dependent manner? 
What I would like is that each picture is shown only once during the 60 trials, but that stimuli are randomly selected and combined to be presented in pairs that differ from participant to participant, while also randomizing the side on which it is shown. 

You have 30 items. You cannot possibly achieve that each stimulus is only shown once if you run 60 trials.
By Aleya - 5/27/2019

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Aleya - Friday, May 17, 2019
Sorry for being unclear. In the example used before the fixation marker is always 0, independent of the trial or the stimulus pair shown:

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation; 2001 = emoleft, neuright, marker]
/ posttrialpause = (picture.fixation.item)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ ontrialend = [values.iti = trial.iti.timeout]
/ timeout = values.trialduration
</trial>

I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file. Or am I missing some general hint?

Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?

<list trialselector>

/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

Sorry, if my questions are a bit trivial and thanks for your help!


Re. 1: "I would like to use the fixation cross as a baseline for the eye movement measured and to do so I have to be able to differentiate the fixation crosses of the different Trials in the output file.

Then define two <port> elements, one sending a marker indicating emoneu baseline, one with a marker indicating neuemo baseline.


<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

Define a port that resets everything to zero in between markers as well so you can get a clear separation between your baseline and your stimulus marker

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

Then display the appropriate marker along with the fixation cross in your <trial> elements, e.g.

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.currentindex*100) + picture.neuright.currentindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1900=allbitstozero; 2001 = emoleft, neuright, marker]
...
</trial>


Re. 2: "Concerning the repetitions: I have 60 items (30 of each category) and have indicated the poolsize accordingly, have I not?"

Your list is sampling *trials* not *items*

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
/ replace = false
</list>

You should end up with 30 "emoneu" trials and 30 "neuemo" trials.

It is *those* trials, however, that display stimuli and their respective items. The code you have shared tells me nothing about how you select items or how often a given stimulus element is sampled from.


Sorry about that. I defined the pictures that are read into the trials in the following way:

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emo
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emo
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

and then thought that the list would take care of the equal distribution (30 neuemo and 30 emoneu trials) without replacement:

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

Is there something missing in the trials coding or am I missing something altogether?

> Is there something missing in the trials coding or am I missing something altogether?

I can't say because those code snippets don't give me a picture of all relevant parts. There's no info about how many trials you are actually running, i.e. the <block> element(s) in your script. The actual <trial> definitions aren't there, you only shared the code for <trial emoneu> in your very first post in this thread. I have no way to see whether the trials are displaying the correct <picture> elements and/or whether there are any other <trial> elements that display those same stimulus elements and thus affect the sampling from those stimulus elements' items. The <item> elements are missing, I have no way to see whether there actually are 30 items, and so forth. Finally, you have not explained what you actually understand a "repeat" to be. Please provide a data file that illustrates what you mean.

Sorry. This is the current script I am running (including your implementations):

<eyetracker>
/ plugin = "tobii"
</eyetracker>

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1200
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<port marker>
/ items = (0)
/ port = eyetracker
/ erase = false
</port>

<port emoneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neuemo_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"
/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"
/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"
/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"
/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

<picture fixation>
/ items = fix
/ position = (50%, 50%)
</picture>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (picture.neuleft.nextindex*100) + picture.emoright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = picture.emoright.currentitem; values.image_neu = picture.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(2000-5000))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 1000
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 60
</list>

<block eyetracking>
/ trials = [1-60 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum values.image_emo values.image_neu values.iti]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>


Thanks.

Your <picture> elements should *not* be set to /selectionrate = always.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = always
/ select = noreplace
</picture>

They should use the default, /selectionrate = trial.

<picture neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = noreplace
</picture>

With /selectionrate = always, you're effectively sampling *two* items from each <picture> stimulus per trial, one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>

and another one here

<trial emoneu>
/ ontrialbegin = [values.marker = (picture.emoleft.nextindex*100) + picture.neuright.nextindex;]
/ ontrialbegin = [port.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(1000,2000))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = eyetracker
/ recorddata = true
/ ontrialend = [values.image_emo = (picture.emoleft.currentitem); values.image_neu = picture.neuright.currentitem]
/ timeout = values.trialduration
</trial>


I see, thank you Dave! 

I just tried it though and it is still repeating Stimuli in the presentation!? 

Please clarify what exactly you mean by "repeating stimuli in the presentation." As stated before, please also provide a data file that illustrates what exactly you mean.

When I run the experiment, stimulus S30.jpg for example will be shown twice within the first 10 trials:

subject  group  trialnum   values.image_emovalues.image_neuvalues.iti
1                1          2               S30.jpg         N03.jpg      3004
1                1          3               S03.jpg          N18.jpg      3836
1                1          4               S25.jpg          N01.jpg      4856
1                1          5               S09.jpg          N04.jpg      3431
1                1          6               S27.jpg          N25.jpg      4099
1                1          7               S24.jpg          N24.jpg      3833
1                1          8               S22.jpg          N13.jpg      4304
1                1          9               S28.jpg          N11.jpg      3418
1                1         10              S30.jpg         N20.jpg      4452
1                1         11              S23.jpg          N11.jpg      3350
…..

How can that be?

Because you have two trial elements that sample from two separate <picture> elements. They are independent. This becomes much clearer when you actually log some more useful information:

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>

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

This is exactly what the script is set up to do.

Ok, I understand. But then how do I avoid these repetitions? By sampling them in a dependent manner? 
What I would like is that each picture is shown only once during the 60 trials, but that stimuli are randomly selected and combined to be presented in pairs that differ from participant to participant, while also randomizing the side on which it is shown. 

You have 30 items. You cannot possibly achieve that each stimulus is only shown once if you run 60 trials.

Even if I set the poolsize to 30, it keeps repeating stimuli...
By Dave - 5/27/2019

Aleya - 5/28/2019

Even if I set the poolsize to 30, it keeps repeating stimuli...

Yes, because the <picture> elements your two <trial> elements use are independent selection pools. If you do not want that you can use <list> elements to form a single selection pool across the various <picture> elements.

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1100
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<text marker>
/ items = ("0")
/ port = eyetracker
/ erase = false
</text>

<text emoneu_baseline>
/ items = ("1")
/ erase = false
/ port = eyetracker
</text>

<text neuemo_baseline>
/ items = ("2")
/ erase = false
/ port = eyetracker
</text>

<text allbitstozero>
/ items = ("0")
/ erase = false
/ port = eyetracker
</text>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"

/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"

/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"

/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"

/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<list neuitems>
/ poolsize = 30
</list>

<list emoitems>
/ poolsize = 30
</list>

<text neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.neuitems.nextindex
</text>

<text neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.neuitems.nextindex
</text>

<text emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.emoitems.nextindex
</text>

<text emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.emoitems.nextindex
</text>

<text fixation>
/ items = fix
/ position = (50%, 50%)
</text>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (list.emoitems.nextindex*100) + list.neuitems.nextindex;]
/ ontrialbegin = [text.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(100,200))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = keyboard
/ recorddata = true
/ ontrialend = [values.image_emo = (text.emoleft.currentitem); values.image_neu = text.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (list.neuitems.nextindex*100) + list.emoitems.nextindex;]
/ ontrialbegin = [text.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(100,200))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = keyboard
/ recorddata = true
/ ontrialend = [values.image_emo = text.emoright.currentitem; values.image_neu = text.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(200-500))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 100
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 30
</list>

<block eyetracking>
/ trials = [1-30 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>

You can see the difference in the attached data file.
By Aleya - 6/2/2019

Dave - 5/28/2019
Aleya - 5/28/2019

Even if I set the poolsize to 30, it keeps repeating stimuli...

Yes, because the <picture> elements your two <trial> elements use are independent selection pools. If you do not want that you can use <list> elements to form a single selection pool across the various <picture> elements.

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1100
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<text marker>
/ items = ("0")
/ port = eyetracker
/ erase = false
</text>

<text emoneu_baseline>
/ items = ("1")
/ erase = false
/ port = eyetracker
</text>

<text neuemo_baseline>
/ items = ("2")
/ erase = false
/ port = eyetracker
</text>

<text allbitstozero>
/ items = ("0")
/ erase = false
/ port = eyetracker
</text>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"

/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"

/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"

/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"

/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<list neuitems>
/ poolsize = 30
</list>

<list emoitems>
/ poolsize = 30
</list>

<text neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.neuitems.nextindex
</text>

<text neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.neuitems.nextindex
</text>

<text emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.emoitems.nextindex
</text>

<text emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.emoitems.nextindex
</text>

<text fixation>
/ items = fix
/ position = (50%, 50%)
</text>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (list.emoitems.nextindex*100) + list.neuitems.nextindex;]
/ ontrialbegin = [text.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(100,200))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = keyboard
/ recorddata = true
/ ontrialend = [values.image_emo = (text.emoleft.currentitem); values.image_neu = text.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (list.neuitems.nextindex*100) + list.emoitems.nextindex;]
/ ontrialbegin = [text.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(100,200))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = keyboard
/ recorddata = true
/ ontrialend = [values.image_emo = text.emoright.currentitem; values.image_neu = text.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(200-500))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 100
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 30
</list>

<block eyetracking>
/ trials = [1-30 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>

You can see the difference in the attached data file.

Thanks a bunch Dave!
I am not sure whether it is due to the version I am using or not, but my inquisit does not recognize the "/ port" command in "<text>" and also gives me the error "Redefinition of event 3" when I run it as written above for the following command:

/ ontrialbegin = [values.marker = (list.neuitems.nextindex*100) + list.emoitems.nextindex;]
By Dave - 6/2/2019

Aleya - 6/3/2019
Dave - 5/28/2019
Aleya - 5/28/2019

Even if I set the poolsize to 30, it keeps repeating stimuli...

Yes, because the <picture> elements your two <trial> elements use are independent selection pools. If you do not want that you can use <list> elements to form a single selection pool across the various <picture> elements.

<defaults>
/ fontstyle = ("Candara", 24pt)
/ minimumversion = "4.0.4.0"
/ screencolor = darkgrey
</defaults>

<values>
/ trialduration = 1100
/ marker = 0
/ iti = 0
/ image_neu = ""
/ image_emo = ""
/ emoneu_baseline = 0
/ neuemo_baseline = 0
</values>

<instruct>
/ nextkey = (28)
/ lastlabel = "Press Enter"
/ nextlabel = "Press Enter"
/ fontstyle = ("Candara", 24pt)
/ screencolor = darkgrey
/ txcolor = (0, 0, 0)
/ wait = 500
/ windowsize = (100%,100%)
</instruct>

--------------------
STIMULI

<text marker>
/ items = ("0")
/ port = eyetracker
/ erase = false
</text>

<text emoneu_baseline>
/ items = ("1")
/ erase = false
/ port = eyetracker
</text>

<text neuemo_baseline>
/ items = ("2")
/ erase = false
/ port = eyetracker
</text>

<text allbitstozero>
/ items = ("0")
/ erase = false
/ port = eyetracker
</text>

<item emotional>
/ 1 = "S01.jpg"
/ 2 = "S02.jpg"
/ 3 = "S03.jpg"
/ 4 = "S04.jpg"
/ 5 = "S05.jpg"
/ 6 = "S06.jpg"
/ 7 = "S07.jpg"
/ 8 = "S08.jpg"
/ 9 = "S09.jpg"
/ 10 = "S10.jpg"

/ 11 = "S11.jpg"
/ 12 = "S12.jpg"
/ 13 = "S13.jpg"
/ 14 = "S14.jpg"
/ 15 = "S15.jpg"
/ 16 = "S16.jpg"
/ 17 = "S17.jpg"
/ 18 = "S18.jpg"
/ 19 = "S19.jpg"
/ 20 = "S20.jpg"

/ 21 = "S21.jpg"
/ 22 = "S22.jpg"
/ 23 = "S23.jpg"
/ 24 = "S24.jpg"
/ 25 = "S25.jpg"
/ 26 = "S26.jpg"
/ 27 = "S27.jpg"
/ 28 = "S28.jpg"
/ 29 = "S29.jpg"
/ 30 = "S30.jpg"
</item>

<item neutral>
/ 1 = "N01.jpg"
/ 2 = "N02.jpg"
/ 3 = "N03.jpg"
/ 4 = "N04.jpg"
/ 5 = "N05.jpg"
/ 6 = "N06.jpg"
/ 7 = "N07.jpg"
/ 8 = "N08.jpg"
/ 9 = "N09.jpg"
/ 10 = "N10.jpg"

/ 11 = "N11.jpg"
/ 12 = "N12.jpg"
/ 13 = "N13.jpg"
/ 14 = "N14.jpg"
/ 15 = "N15.jpg"
/ 16 = "N16.jpg"
/ 17 = "N17.jpg"
/ 18 = "N18.jpg"
/ 19 = "N19.jpg"
/ 20 = "N20.jpg"

/ 21 = "N21.jpg"
/ 22 = "N22.jpg"
/ 23 = "N23.jpg"
/ 24 = "N24.jpg"
/ 25 = "N25.jpg"
/ 26 = "N26.jpg"
/ 27 = "N27.jpg"
/ 28 = "N28.jpg"
/ 29 = "N29.jpg"
/ 30 = "N30.jpg"
</item>

< item fix>
/ 1 = "fixationcross.png"
</item>

<list neuitems>
/ poolsize = 30
</list>

<list emoitems>
/ poolsize = 30
</list>

<text neuleft>
/ items = neutral
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.neuitems.nextindex
</text>

<text neuright>
/ items = neutral
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.neuitems.nextindex
</text>

<text emoleft>
/ items = emotional
/ position = (25%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.emoitems.nextindex
</text>

<text emoright>
/ items = emotional
/ position = (75%, 50%)
/ size = (900, 600)
/ selectionrate = trial
/ select = list.emoitems.nextindex
</text>

<text fixation>
/ items = fix
/ position = (50%, 50%)
</text>

------------------------
TRIALS

<trial emoneu>
/ ontrialbegin = [values.marker = (list.emoitems.nextindex*100) + list.neuitems.nextindex;]
/ ontrialbegin = [text.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(100,200))]
/ stimulustimes = [0 = fixation, emoneu_baseline; 1000 = allbitstozero; 1001 = emoleft, neuright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = keyboard
/ recorddata = true
/ ontrialend = [values.image_emo = (text.emoleft.currentitem); values.image_neu = text.neuright.currentitem]
/ timeout = values.trialduration
</trial>

<trial neuemo>
/ ontrialbegin = [values.marker = (list.neuitems.nextindex*100) + list.emoitems.nextindex;]
/ ontrialbegin = [text.marker.setitem(values.marker, 1);]
/ ontrialbegin = [values.iti = round(rand(100,200))]
/ stimulustimes = [0 = fixation, neuemo_baseline; 1000 = allbitstozero; 1001 = neuleft, emoright, marker]
/ posttrialpause = (values.iti)
/ inputdevice = keyboard
/ recorddata = true
/ ontrialend = [values.image_emo = text.emoright.currentitem; values.image_neu = text.neuleft.currentitem]
/ timeout = values.trialduration
</trial>

<trial iti>
/ stimulusframes = [1 = fixation]
/ timeout = round(rand(200-500))
/ recorddata = false
</trial>

<trial fixation>
/ stimulusframes = [1 = fixation]
/ timeout = 100
/ recorddata = false
</trial>

<list trialselector>
/ items = (trial.emoneu, trial.neuemo)
/ itemprobabilities = (.50, .50)
/ poolsize = 30
</list>

<block eyetracking>
/ trials = [1-30 = list.trialselector]
</block>

<data eyetracking>
/ file = "Sequence.iqdat"
/ columns = [subject trialnum trialcode values.image_emo values.image_neu values.iti values.marker]
/ separatefiles = true
</data>

<expt eyetracking>
/ blocks = [1 = eyetracking]
</expt>

You can see the difference in the attached data file.

Thanks a bunch Dave!
I am not sure whether it is due to the version I am using or not, but my inquisit does not recognize the "/ port" command in "<text>" and also gives me the error "Redefinition of event 3" when I run it as written above for the following command:

/ ontrialbegin = [values.marker = (list.neuitems.nextindex*100) + list.emoitems.nextindex;]

You can ignore the warnings for the /port attribute in the <text> elements. Those <text> elements are placeholders only, you are going to use proper <port> elements anyway. I'm not seeing any "redefinition of event" error -- I've tested the script under Inquisit 4.0.10.0, which is what you should be using, it's the latest release. If you're still running some older, outdated version, please update your installation (4.0.10.0 is available via https://www.millisecond.com/download/ as always).
By Aleya - 6/5/2019

OK thank you!

Now it gives me an error about the ports though :-/
Specifically it tells me that "Item values must specify exactly 8 bits" for all of the ports:
<port marker>
/ items = ("0")
/ erase = false
/ port = eyetracker
</port>

<port sexneu_baseline>
/ items = ("1")
/ erase = false
/ port = eyetracker
</port>

<port neusex_baseline>
/ items = ("2")
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = ("0")
/ erase = false
/ port = eyetracker
</port>

when I change these to 00000000 etc. the script runs, but I receive no useful markers and warnings...
Sorry to budge you again with this...I hope we'll have it up and running soon!
By Dave - 6/6/2019

Aleya - 6/6/2019
OK thank you!

Now it gives me an error about the ports though :-/
Specifically it tells me that "Item values must specify exactly 8 bits" for all of the ports:
<port marker>
/ items = ("0")
/ erase = false
/ port = eyetracker
</port>

<port sexneu_baseline>
/ items = ("1")
/ erase = false
/ port = eyetracker
</port>

<port neusex_baseline>
/ items = ("2")
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = ("0")
/ erase = false
/ port = eyetracker
</port>

when I change these to 00000000 etc. the script runs, but I receive no useful markers and warnings...
Sorry to budge you again with this...I hope we'll have it up and running soon!

The syntax is wrong for port elements. You use question marks if you want to specify markers as 8-bit binary values (e.g. "00000001"). You don't use any quotation marks if you want to specify marker values as decimals instead (e.g. 2).

<port sexneu_baseline>
/ items = (1)
/ erase = false
/ port = eyetracker
</port>

<port neusex_baseline>
/ items = (2)
/ erase = false
/ port = eyetracker
</port>

<port allbitstozero>
/ items = (0)
/ erase = false
/ port = eyetracker
</port>
By Aleya - 6/10/2019

Right sorry, silly me.

Another more general question: I am used to using both calibration and validation in eye tracking, yet I cannot seem to find this option for tobii in inquisit. Is there one, or is the calibration accurate enough?

And if I want to make sure that the participants are fixating the fixation cross for at least 300 ms at the beginning of each trial, I know that I can use:

<eyetracker>
/ plugin = "tobii"
/ aoidurationthreshold = 300
</eyetracker>

but do I need to define the aoi as the fixation cross in some way?

Thanks in advance
By Dave - 6/10/2019

Aleya - 6/11/2019
Right sorry, silly me.

Another more general question: I am used to using both calibration and validation in eye tracking, yet I cannot seem to find this option for tobii in inquisit. Is there one, or is the calibration accurate enough?

And if I want to make sure that the participants are fixating the fixation cross for at least 300 ms at the beginning of each trial, I know that I can use:

<eyetracker>
/ plugin = "tobii"
/ aoidurationthreshold = 300
</eyetracker>

but do I need to define the aoi as the fixation cross in some way?

Thanks in advance

The eyetracker plugins currently do not surface validation data or allow for manually triggering re-calibration. They basically rely on the information from the tracker, i.e. whether it reports a calibration success or not. The calibration quality should be good enough.

The aoidurationthreshold setting is the correct one, the fixation cross you would define as AOI by defining it as the /validresponse in the <trial>.
By Aleya - 6/10/2019


Thank you so much for all your help Dave! Much appreciated!