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Line Counting Task
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Script Author: Katja Borchert, Ph.D. (katjab@millisecond.com) for Millisecond Software, LLC
Date: 02-28-2018
last updated: 03-13-2020 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC
Script Copyright © 03-13-2020 Millisecond Software
This task has been generously made available to the Millisecond Script Library by Dr. James Bjork.
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BACKGROUND INFO
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This script implements a line counting task, an attentional bias task with target images.
Reference:
Passamonti, L. et al (2017). Atomoxetine effects on attentional bias to drug-related cues
in targetCategory dependent individuals. Psychopharmacology (2017) 234:2289–2297.
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TASK DESCRIPTION
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Participants view pictures of targetCategory (here: cocaine) related and neutral images. Each image has
2-5 blue lines drawn on it. Participants are asked to indicate the number of lines by button press.
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DURATION
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the default set-up of the script takes appr. 8 minutes to complete
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DATA FILE INFORMATION
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The default data stored in the data files are:
(1) Raw data file: 'linecountingtask_raw*.iqdat' (a separate file for each participant)*
build: The specific Inquisit version used (the 'build') that was run
computer.platform: the platform the script was run on (win/mac/ios/android)
date, time, date and time script was run
subject, group, with the current subject/groupnumber
script.sessionid: with the current session id
StimOrder: "C->N" vs. "N->C" (the image sequence per run)
C->N: each run runs 8 targetCategory images and then 8 neutral images
N->C: each run runs 8 neutral images and then 8 targetCategory images
countPracticeRuns: counts the number of practice sessions run
countTestRuns: counts the number of test runs (there should be 9 runs with 16 stims each, 8 targetCategory and 8 neutral)
blockcode, blocknum: the name and number of the current block (built-in Inquisit variable)
trialcode, trialnum: the name and number of the currently recorded trial (built-in Inquisit variable)
Note: trialnum is a built-in Inquisit variable; it counts all trials run; even those
that do not store data to the data file such as feedback trials. Thus, trialnum
may not reflect the number of main trials run per block.
Stimulus_Type: NEUTRAL vs. targetCategory
stimulusitem: stores the image file presented
Number_Of_Stripes_In_Stimulus: stores the number of stripes
Correct_Response: Z, X, N, M
response: the scancode of the key response (44 = Z, 45 = X, 49 = N, 50 = M)
responsetext: for test trials: the character key that was pressed -> Z, X, N, M
correct: 1 = correct, 0 = error/no response
Response_Outcome: CORRECT, INCORRECT, NONE
Trial_Outcome: CORRECT, WRONG, NO RESPONSE
Response_Time: response time in ms; measured from onset of image up until response button is hit
Note: if no response => values.Response_Time = ""
iti: the jittered ITI, iti is randomly selected from a normal distribution
around parameters.ITI_mean with SD = parameters.ITI_SD (with minimum iti = parameters.ITI_min
and maximum iti = parameters.ITI_max)
targetCategoryCravingRating: the selected rating for the craving of the target substance (1-9)
(2) Summary data file: 'linecountingtask_summary*.iqdat' (a separate file for each participant)*
computer.platform: the platform the script was run on (win/mac/ios/android)
script.startdate: date script was run
script.starttime: time script was started
script.subjectid: assigned subject id number
script.groupid: assigned group id number
script.sessionid: assigned session id number
script.elapsedtime: time it took to run script (in ms); measured from onset to offset of script
script.completed: 0 = script was not completed (prematurely aborted);
1 = script was completed (all conditions run)
targetCategoryCravingRating: the selected rating for the craving of the target substance (1-9)
countPracticeRuns: counts the number of practice sessions run
StimOrder: "C->N" vs. "N->C" (the start of the testing blocks)
Interference_meanRT_derived: the interference score in ms: calculate as 'meanRT_targetCategory - meanRT_neutral' (Main DV)
=> positive: it took partipants on average longer to reply to target Category
images than to neutral images (attentional bias)
Interference_medianRT_derived: the interference score in ms: calculate as 'medianRT_targetCategory - medianRT_neutral'
=> positive: it took partipants on average longer to reply to
target Category images than to neutral images (attentional bias)
meanRT_targetCategory: mean response time (in ms) of correct responses to targetCategory related images
medianRT_targetCategory: median response time (in ms) of correct responses to targetCategory related images
meanRT_Neutral: mean response time (in ms) of correct responses to neutral images
medianRT_Neutral: median response time (in ms) of correct responses to neutral images
propCorrect_targetCategory: proportion correct responses to targetCategory related images
propCorrect_Neutral: proportion correct responses to neutral images
propTimeout_targetCategory: proportion timed-out responses (=no responses) to targetCategory related images
propTimeout_neutral: proportion timed-out responses (=no responses) to Neutral related images
* separate data files: to change to one data file for all participants (on Inquisit Lab only), go to section
"DATA" and follow further instructions
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EXPERIMENTAL SET-UP
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(1) Cravings Rating
(2) Practice (with feedback): 30 trials with neutral images
- images are selected randomly from the 30 provided practice images
Note: practice session can be repeated as long as the number of practice sessions is
below parameters.maxPracticeSessions (default: 2, editable parameters)
(3) Test (no feedback): 9 runs of 16 trials: 8 targetCategory and 8 neutral images
(image conditions are presented in blocked format; the starting condition - targetCategory vs. neutral- is selected
based on groupnumber:
groupnumber 1: target category -> neutral (C->N)
groupnumber 2: neutral -> target category (N->C)
- images for each condition are selected randomly (without replacement) from the provided pool
of 72 targetCategory images and 72 neutral images
Response Keys:
z -> 2 stripes
x -> 3 stripes
n -> 4 stripes
m -> 5 stripes
Note: the iti is randomly selected from a normal distribution around parameters.ITI_mean (default: 1750ms) with SD = parameters.ITI_SD
(default: 500ms) and with max itis = parameters.ITI_max (default: 3100ms) and minimum itis = parameters.ITI_min (default: 1100ms)
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STIMULI
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provided by Dr. James Bjork
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INSTRUCTIONS
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see section Editable Instructions
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EDITABLE CODE
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check below for (relatively) easily editable parameters, stimuli, instructions etc.
Keep in mind that you can use this script as a template and therefore always "mess" with the entire code
to further customize your experiment.
The parameters you can change are:
/picSize: proportional (to canvas height) sizing of the images (default: 80%)
/maxPracticeSessions: maximum number of practice sessions that can be run (default: 2)
/waitDuration: the duration (in ms) of the wait-trial (default: 2000ms)
/picDuration: the duration (in ms) of the images (default: 900ms)
Intertrial Interval (in ms) Sampling:
/ITI_mean: the mean of the normal distribution itis are sampled from (default: 1750ms)
/ITI_SD: the standard deviation of the normal distribution itis are sampled from (default: 500ms)
/ITI_max: the maximum iti (default: 3100ms)
/ITI_min: the minimum iti (default: 1100ms)