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										   Alcohol GNAT
									
This script is in part based on the original gnatdemo.iqx by Brian Nosek (nosek@virginia.edu)
last updated: 01-22-2018 by K.Borchert (katjab@millisecond.com) for Millisecond Software LLC


BACKGROUND INFO

											*Purpose*
											
This script implements an Alcohol Go-Nogo Association Task (GNAT) similarly to the one described in: 											
											
Obasi, E.M., Cavanagh, L., Pittman, D.M., & Brooks, J.J. (2016). Effects of evaluative context in implicit 
cognitions associated with alcohol and violent behaviors. Addictive Behaviors Reports 3 (2016) 48–55										

GNAT Literature Reference:
Nosek, B. A., & Banaji, M. R. (2001). The go/no-go association task.  Social Cognition, 19(6), 625-666. 

Adjustments to z-scores as recommended by:
Gregg, A. & Sedikides, C. (2010). Narcissistic Fragility:
Rethinking Its Links to Explicit and Implicit Self-esteem, Self and Identity, 9:2, 142-161 (p.148)


											  *Task*
Participants are asked to categorize attributes (e.g. "happy"; "unhappy") and target items (e.g "BEER") into 
predetermined categories via keystroke presses. 
The basic task is to press the Spacebar if an item (e.g. "happy") belongs to the category currently being tested 
(e.g. "Good" = signal items)  and to do nothing if it doesn't (= noise items).
For practice, participants sort items into signal categories "Good" (as opposed to noise: items from 'Bad' category), 
"Bad" (as opposed to noise: items from 'Good' category), and "Alcoholic Drinks" (as opposed to noise: items from "non-alcoholic drinks" 
category).
For the test, participants are asked to sort items into the paired/combined categories (e.g. 
"Alcoholic Drinks OR Good" and "Alcoholic Drinks OR Bad"). When an item belongs to either one of these two categories,  
participants should press the Spacebar. If an item belongs to neither, participants should simply wait for the next
item to come on screen.


DATA FILE INFORMATION: 
The default data stored in the data files are:

(1) Raw data file: 'alcoholgnat_raw*.iqdat' (a separate file for each participant)

build:							Inquisit build
computer.platform:				the platform the script was run on
date, time, subject, group:		date and time script was run with the current subject/groupnumber 
blocknum/blockcode:				number of the current block and name of current block
trialcode/trialnum:				name and number of current trial
/phase:							"training" (one category) vs. "test" (two categories)
/responsetimeout:				time in ms that is allowed for response in a given trial
/signal:						1 = signal trial (spacebar response is correct); 0 = noise trial (no response is correct)

/targettype:					"A"(here: A-alcoholic drinks)
/pairing:						"AA" -> targetA-attributeA (here: alcoholic drinks and good)
								"AB" -> targetA-attributeB (here: alcoholic drinks and bad)
/trialtype:						training phase: "training"
								test phase: "practice" vs. "test"

stimulusitem:					the presented stimulusitems in order of presentation in stimulusframes (see trials)
response:						response made (either 57 = Spacebar or 0 for no response)
latency:						the latency of the response in ms (or if no response: response timeout duration)
correct:						the accuracy of response (1 = correct; 0 = error)

(2) Summary data file: 'alcoholgnat_summary*.iqdat' (a separate file for each participant)

script.startdate:				date script was run
script.starttime:				time script was started
script.subjectid:				subject id number
script.groupid:					group id number
script.elapsedtime:				time it took to run script (in ms)
computer.platform:				the platform the script was run on
/completed:						0 = script was not completed; 1 = script was completed (all conditions run)

/responsetimeout2_signal:		stores the response timeouts in ms used for signals in the first gnat round (2 blocks) in this script (default: 700ms)
/responsetimeout2_noise:		stores the response timeouts in ms used for noise in the first gnat round (2 blocks) in this script (default: 700ms)
/responsetimeout3_signal:		stores the response timeouts in ms used in this study for signals in the second gnat round (2 blocks) in this script (default: 550ms)
/responsetimeout3_noise:		stores the response timeouts in ms used for noise in the second gnat round (2 blocks) in this script (default: 550ms)
									Notes: responsetimeouts1 (1000ms) are used for practice only

/propcorrect_AA:				overall proportion correct for pairing targetA-attributeA (here: alcoholic drinks-good); test trials only
/propcorrect_AB:				overall proportion correct for pairing targetA-attributeB (here: alcoholic drinks-bad); test trials only

Note: z-score calculations: adjustments (see Gregg & Sedikides, 2010, p.148)
If the hit rate / FA rate is 0 => 0.005 is used instead (aka 0.005 is added to the hit/FA rate)
IF the hit rate / FA rate is 1.0 => 0.995 is used instead (aka 0.005 is subtracted from the hit/FA rate)

/rHit_AA:						hit rate for pairing targetA-attributeA (here: alcoholic drinks-good) across all responsetimeouts; test trials only
/rFA_AA:						false alarm (FA) rate for pairing targetA-attributeA (here: alcoholic drinks-good) across all responsetimeouts; test trials only
/zhit_AA:						z-score of hit rate for pairings targetA-attributeA (here: alcoholic drinks-good)
/zFA_AA:						z-score of FA rate for pairings targetA-attributeA (here: alcoholic drinks-good)
/AA_dprime:						Computes d' (parametric measure of discriminability) for alcoholic drink-Good Pairings
/AA_beta:						Computes ß (beta) (parametric measure of bias) for alcoholic drink-Good Pairings

/rHit_AB:						hit rate for pairing targetA-attributeA (here: alcoholic drinks-bad) across all responsetimeouts; test trials only
/rFA_AB:						false alarm (FA) rate for pairing targetA-attributeA (here: alcoholic drinks-bad) across all responsetimeouts; test trials only
/zhit_AB:						z-score of hit rate for pairings targetA-attributeA (here: alcoholic drinks-bad)
/zFA_AB:						z-score of FA rate for pairings targetA-attributeA (here: alcoholic drinks-bad)
/AB_dprime:						Computes d' (parametric measure of discriminability) for alcoholic drink-bad Pairings
/AB_beta:						Computes ß (beta) (parametric measure of bias) for alcoholic drink-bad Pairings


EXPERIMENTAL SET-UP:

Default GNAT Set-Up in this script:
(1) 3 training blocks: one training block each for categorizing targetA (signal: alcoholic drinks vs. noise: nonalcoholic drinks), 
attributeA (signal: good vs. noise: bad), attributeB (signal: bad vs. noise: good)
with response timeout of 1000ms (editable under section Editable Values)
-> block order is determined randomly
-> run 20 trials each (10 target:10 noise)

(2) 2 test blocks that combine targets (here: alcoholic drinks) and attributes (either good or bad)
with a faster response timeout (default: 750ms)
-> block order of pairings is determined randomly
-> each block runs 16 'practice trials' followed by 60 test trials (summary variables based on test trial performance only)
-> signal : noise = 1 : 1

(3) 2 test blocks that combine targets (here: alcoholic drinks) and attributes (either good or bad) 
with an even faster response timeout (default: 550ms)
-> block order of pairings is determined randomly
-> each block runs 16 'practice trials' followed by 60 test trials (summary variables based on test trial performance only)
-> signal : noise = 1 : 1

Notes: 
* Number of trials run as well as 'signal : noise' ratios can only be edited on block level (see section BLOCKS)

STIMULI
24 stimuli are used for signals (ALCOHOLIC DRINKS) and noise items.
Go to section Editable Stimuli to check out the stimuli.

Note: the default set-up in this script uses alcoholic drinks as the signals and and non-alcoholic drinks as noise items.


INSTRUCTIONS
see section Editable Instructions

EDITABLE CODE:
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:

/responsetimeout1_signal:						stores the longest response timeouts in ms used in this study for targets
													Note: by default, the longest response timeout in this script is used for practice blocks
													that test attributes and targets separately (default: 1000ms)
/responsetimeout2_signal:						stores the next response timeouts in ms used in this study for targets
													Note: by default, in this script this is the first response time used for blocks
													that test attributes and targets simultaneously (default 750ms)
/responsetimeout3_signal:						stores the next response timeouts in ms used in this study for targets
													Note: by default, this is the second response time used for blocks
													that test attributes and targets simultaneously (default 550ms)

(same for noise trials: by default they are the same in this script)

												!!!NOTE: "The most effective response deadlines for measuring automatic cognition are those fast enough
												to eliminate perfect responding but not so fast as to lower accuracy substantially, with an 
												approporiate range of response deadlines falling between 500 and 850ms." (Nosek & Banaji, 2001, p.636)

/isi:											stores the interstimulus interval (time between offset of one stimulus and onset of next)
											


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