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									ATTRACTION pGNAT
SCRIPT INFO

Script Author: Marisa Okano
last updated: 01-12-2016 by K.Borchert (katjab@millisecond.com) for Millisecond Software LLC

Millisecond Software thanks Marisa Okano for sharing her script!


BACKGROUND INFO

											*Purpose*
This script implements the “Attraction pGNAT” as described in:

Eastwick, P. W., Eagly, A. H., Finkel, E. J., & Johnson, S. E. (2011). 
Implicit and explicit preferences for physical attractiveness in a romantic partner: 
A double dissociation in predictive validity. Journal of Personality and Social Psychology, 101, 993-1011.

This “Attraction pGNAT” is a modified version of the Go/No-go Association Task (GNAT), 
which was originally designed to measure automatic associations regarding a single target 
construct (Nosek & Banaji, 2001). Specifically, the same basic procedure of the GNAT is 
used to assess the strength of participants' positive versus negative associations 
regarding a single target category (e.g. physical attractiveness); however, the GNAT 
will be modified to reflect the personalized version utilized in the study of explicit and 
implicit preferences of physical attractiveness by Eastwick, Eagly, Finkel, & Johnson (2011).


											  *Task*
Participants are asked to categorize items (e.g words  "attractive", "wealthy" etc) into predetermined 
categories via keystroke presses. The basic task is to press the Spacebar if an items (e.g. "attractive")
belongs to the category currently being tested (e.g. Physical Appearance) and to do nothing if it doesn't.
For practice, participants sort items into categories "Physical Appearance", "Social Status", "Personality" 
as well as evaluative preference categories "I like" and "I don't like".
For the test, participants are asked to sort categories into the paired/combined categories (e.g. 
"Physical Appearance OR I don't like"). When an item belongs to either one of these two categories, 
participants should press the Spacebar.


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

(1) Raw data file: 'Attraction_pGNAT_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 
blockcode, blocknum:			the name and number of the current block
trialcode, trialnum: 			the name and number of the currently recorded trial
									(Note: not all trials that are run might record data) 
/targetcategory:				1 = PA 1000 trial; 2 = SS 1000 trial; 3 = PE 1000 trial; 0 = other
/testtrial:						0 = practice/training; 1 = testtrial (trialcount >= 20 in each testblock)
stimulusitem:					the presented stimuli in order of trial presentation
latency: 						the response latency (in ms)
response:						the participant's response (57 = spacebar)
correct:						the correctness of the response (1 = correct; 0 = incorrect)
error:							1 = error response; 0 = correct response
numcorrect:						running total of correct responses in the current block

(2) Summary data file: 'Attraction_pGNAT_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 (prematurely aborted); 1 = script was completed (all conditions run)
/rHit_PA_like:					hit rate for pairing Physical-Appearance (PA) - Like 
/rHit_PA_dislike:				hit rate for pairing Physical-Appearance (PA) - DisLike 
/rHit_SS_like:					hit rate for pairing Social Status (SS) - Like 
/rHit_SS_dislike:				hit rate for pairing Social Status (SS) - DisLike 
/rHit_PE_like:					hit rate for pairing Personality (PE) - Like 
/rHit_PE_dislike:				hit rate for pairing Personality (PE) - DisLike 

(and the same for False Alarm Rates)
/rFA_PA_like-
/rFA_PE_dislike

/propcorrect_PA_like:  		overall proportion correct for pairing Physical-Appearance (PA) - Like 
/propcorrect_PA_dislike:  	overall proportion correct for pairing Physical-Appearance (PA) - DisLike 
/propcorrect_SS_like:  		overall proportion correct for pairing Social Status (SS) - Like 
/propcorrect_SS_dislike:  	overall proportion correct for pairing Social Status (SS) - DisLike 
/propcorrect_PE_like:  		overall proportion correct for pairing Personality (PE) - Like 
/propcorrect_PE_dislike:  	overall proportion correct for pairing Personality (PE) - DisLike 


EXPERIMENTAL SET-UP

3 target categories (Physical Appearance (PA), Social Status (SS), and Personality (PE)) x 2 preference categories (like vs. dislike)

* 3 practice blocks of categorizing items into 3 target categories Physical Appearance (PA), Social Status (SS), and Personality (PE) 
	=> target categories are tested in blocked format, order of blocks is random
	=> 25 trials each
* 2 practice blocks of categorizing items into 2 preference categories (Like/Dislike)
	=> preference categories are tested in blocked format, order is random
	=> 20 trials each
* 6 test blocks: combination of 3 target categories (PA, SS, PE) x 2 like categories (like, dislike)
	categorization of items into paired/combined categories (e.g. sort items into "Physical Appearance" OR "I like")
	=> combined categories are tested in blocked format, order of the test blocks is random
	=> 15 practice trials followed by 60 test trials

* responsewindow:
	practice: 750ms
	test: 1000ms
	
STIMULI
under section Editable Stimuli

INSTRUCTIONS
under section Editable Instructions as well as section 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.

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