Return to the Self-Esteem GNAT page
						
									Self Esteem GNAT 
SCRIPT INFO

This script is in part based on the original gnatdemo.iqx by Brian Nosek (nosek@virginia.edu)

last updated: 1-08-2018 by K.Borchert (katjab@millisecond.com) for Millisecond Software LLC

Millisecond Software thanks Dr. Debbie Roy for her collaboration on this script!


BACKGROUND INFO

											*Purpose*
											
This script implements the self esteem Go-Nogo Association Task (GNAT) similarly to the one described in: 											
											
Gregg, A. & Sedikides, C. (2010). Narcissistic Fragility:
Rethinking Its Links to Explicit and Implicit Self-esteem, Self and Identity, 9:2, 142-161											

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

										
The Self Esteem Gnat is seen as a measure of a global marker for ego fragility.


											  *Task*
Participants are asked to categorize nice and nasty words (e.g. "friendship"; "murder") and target items (e.g 'myself', 'them') 
into predetermined categories via keystroke presses. The basic task is to press the Spacebar if an item (e.g. "friendship")
belongs to the category currently being tested (e.g. "nice") and to do nothing if it doesn't.
For practice, participants sort items into categories "nice", "nasty", "me", and "not me".
For the test, participants are asked to sort categories into the paired categories (e.g. 
"me OR nice"). When an item belongs to either one of these two categories,  participants should press the Spacebar.
Otherwise they should do nothing.


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

(1) Raw data file: 'selfesteemgnat_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:				nameand number of current trial

/phase:							"training" (single category categorization) vs. "test" (2 categories categorization)

item.targetAlabel.item.1:		the category used for target A
item.targetBlabel.item.1:		the category used for target B
item.attributeAlabel.item.1:	the category used for attribute A
item.attributeBlabel.item.1:	the category used for attribute B


/responsetimeout:				time in ms that is allowed for response in a given trial
/signal:						1 = signal trial (spacebar response is correct); 0 = noisetrial (no response is correct)

/targettype:					"A" vs. "B" (here: A-me; B-not me)
/pairing:						"AA" -> targetA-attributeA (here: me and nice)
								"AB" -> targetA-attributeB (here: me and nasty)
								"BA" -> targetB-attributeA (here: not me-nice)
								"BB" -> targetB-attributeB (here: not me-nasty)
/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: 'selfesteemgnat_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)

/responsetimeout1_signal:		stores the response timeouts in ms used for signals in the testblocks in this script
/responsetimeout2_noise:		stores the response timeouts in ms used for noise in in the testblocks in this script

item.targetAlabel.item.1:		the category used for target A
item.targetBlabel.item.1:		the category used for target B
item.attributeAlabel.item.1:	the category used for attribute A
item.attributeBlabel.item.1:	the category used for attribute B

/propcorrect_AA:				overall proportion correct for pairing targetA-attributeA (here: me-nice); test trials only
/propcorrect_AB:				overall proportion correct for pairing targetA-attributeB (here: me-nasty); test trials only
/propcorrect_BA:				overall proportion correct for pairing targetB-attributeA (here: not me-nice); test trials only
/propcorrect_BB:				overall proportion correct for pairing targetB-attributeB (here: not me-nasty); 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: me-nice) across all responsetimeouts; test trials only
/rFA_AA:						false alarm (FA) rate for pairing targetA-attributeA (here: me-nice) across all responsetimeouts; test trials only
/zhit_AA:						z-score of hit rate for pairings targetA-attributeA (here: me-nice)
/zFA_AA:						z-score of FA rate for pairings targetA-attributeA (here: me-nice)
/AA_dprime:						Computes d' (parametric measure of discriminability) for me-Nice Pairings
/AA_beta:						Computes ß (beta) (parametric measure of bias) for me-Nice Pairings

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

/rHit_BA:						hit rate for pairing targetB-attributeA (here: not me-nice) across all responsetimeouts; test trials only
/rFA_BA:						false alarm (FA) rate for pairing targetB-attributeA (here: not me-nice) across all responsetimeouts; test trials only
/zhit_BA:						z-score of hit rate for pairings targetB-attributeA (here: not me-nice)
/zFA_BA:						z-score of FA rate for pairings targetB-attributeA (here: not me-nice)
/BA_dprime:						Computes d' (parametric measure of discriminability) for Not Me-Nice Pairings
/BA_beta:						Computes ß (beta) (parametric measure of bias) for Not Me-Nice Pairings

/rHit_BB:						hit rate for pairing targetB-attributeB (here: not me-nasty) across all responsetimeouts; test trials only
/rFA_BB:						false alarm (FA) rate for pairing targetB-attributeB (here: not me-nasty) across all responsetimeouts; test trials only
/zhit_BB:						z-score of hit rate for pairings targetB-attributeB (here: not me-nasty)
/zFA_BB:						z-score of FA rate for pairings targetB-attributeB (here: not me-nasty)
/BB_dprime:						Computes d' (parametric measure of discriminBBility) for not Me-Nasty Pairings
/BB_beta:						Computes ß (beta) (parametric measure of bias) for for Not Me-Nasty Pairings

GNATpart1:						(a) subtracting d' for the Not-me & Nice block from d' for the me & Nice block;
									=> if positive: participant is better at discriminating btw. signal and noise
									when both 'me' and 'nice' are signals than when 'not me' and 'nice' are signals
GNATpart2:						(b) subtracting d' for the me & Nasty block from d' for the Not-me & Nasty block
									=> if positive: participant is better at discriminating btw. signal and noise
									when both 'not me' and 'nasty' are signals than when 'me' and 'nasty' are signals
GNATALL:						A composite index of implicit self-esteem (Gregg & Sedikides, 2010)
								=> sum of GNATpart1 and GNATpart2

								
(3)	datafile "instructions_survey.iqdat" stores the name input raw data				
								
								
								
EXPERIMENTAL SET-UP:

Default GNAT Set-Up in this script:
(1) 4 training blocks: one training block each for targetA (here: me), targetB (here: not me), attributeA (here: Nice), 
attributeB (here: Nasty)
with response timeout of 600ms
-> block order is determined randomly
-> run 20 trials each (10 target:10 noise)

(2) 4 test blocks that combine targets and attributes with a response timeout (default: 600ms)
-> block order is determined randomly
-> each block runs 16 'practice trials' followed by 48 test trials (summary variables based on test trial performance only)
-> signal : noise = 1 : 1 (samenumber of attributeA, attributeB, signal and noise trials)
-> each attribute (12) is selected once during the test trials
-> each target is repeated 4x during the test trials


STIMULI
see section Editable Stimuli (stimuli from Gregg & Sedikides, 2010)

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 blocks
													that test attributes and targets separately (default: 1000ms)
(samefor noise trials: by default they are the same in this script)

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


Copyright © Millisecond Software. All rights reserved.
Contact | Terms of Service | Security Statement | Employment