Partner GNAT

Technical Manual

Last Modified: January 04, 2025 by K. Borchert (katjab@millisecond.com), Millisecond

Script Copyright © Millisecond Software, LLC

Background

This script implements a partner Go-Nogo Association Task (GNAT) using the category 'partner' as the target category.

In general, the GNAT procedure uses the Go-Nogo framework of responding to signal and noise stimuli to investigate implicit bias towards a target category. In contrast to reaction time based tests of implicit bias (e.g. Implicit Association Test), the GNAT framework mainly focuses on accuracy data and specifically d prime measures (measures of sensitivity to distinguish signals from noise in signal detection theory) to infer implicit bias.

For example, a positive association of 'partner' (the target signal) is suggested if the dprime measure in the condition 'Partner-Good' (aka both 'partner' and good attributes are signals) is greater than the dprime measure in the condition 'Partner-Bad' (aka both 'partner' and bad attributes are signals).

This script implements the Partner GNAT procedure described in Lee et al (2010, study 2).

References

Partner Gnat
Lee, S., Rogge, R. D., & Reis, H. T. (2010). Assessing the seeds of relationship decay: Using implicit evaluations to detect the early stages of disillusionment. Psychological Science, 21(6), 857-864.

Adjustments To Z-Scores
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)

Duration

5 minutes

Description

Participants are asked to categorize attributes and partner-related items (e.g words "supportive", "partner name") into predetermined categories via keystroke presses. The basic task is to press the Spacebar if an item (e.g. supportive") belongs to the category currently being tested (e.g. "Constructive"") and to do nothing if it doesn't. For practice, participants sort items into categories "Constructive", and "Destructive". For the test, participants are asked to sort categories into the paired/combined categories (e.g. "Partner OR Constructive"). When an item belongs to either one of these two categories, participants should press the Spacebar.

Procedure

• 2 Practice Blocks of 16 trials (8 go/8 nogo): good -> bad
-> items are randomly sampled
• 2 test blocks (order counterbalanced by groupnumber):
-> testing of paired categories "partner OR good" vs. "partner OR bad"
-> items are randomly sampled
-> 70 trials: 20 partner (go trials), 20 attribute go trials, 30 attribute no-go trials

Stimuli

can be edited under section Editable Stimuli

Instructions

can be edited under section Editable Instructions

Scoring

Zscore Adjustments
zScore Adjustements of hit and false alarm (FA) rates are based on recommendations by Gregg & Sedikides (2010, p.148).
If the hit rate / FA rate is 0 => 0.005 is used instead
IF the hit rate / FA rate is 1.0 => 0.995 is used instead

Summary Data

File Name: partnergnat_summary*.iqdat

Data Fields

NameDescription
inquisit.version Inquisit version number
computer.platform Device platform: win | mac |ios | android
computer.touch 0 = device has no touchscreen capabilities; 1 = device has touchscreen capabilities
computer.hasKeyboard 0 = no external keyboard detected; 1 = external keyboard detected
startDate Date the session was run
startTime Time the session was run
subjectId Participant ID
groupId Group number
sessionId Session number
elapsedTime Session duration in ms
completed 0 = Test was not completed
1 = Test was completed
pgHits Number of Hits for the partner/good pairing
pgFalseAlarms Number of false alarms for the partner/good pairing
pbHits Number of Hits for the partner/bad pairing
pbFalseAlarms Number of false alarms for the partner/bad pairing
Partner-Good Pairings: Signal = Partner, Good; Noise = Anything Else
pgHr Hitrate for partner/good pairing
pgMr Miss rate for partner/good pairing
pgFr False alarm rate for partner/good pairing
pgCr Correct rejection rate for partner/good pairing
pgZHr Z-value of hitrate for partner/good pairing
pgZFr Z-value of false alarm rate for partner/good pairing
pgDPrime D' (parametric measure of discriminability btw. signals and noise) for partner/good pairing
=> Range (in this script)
-5.1516586840152740479 <= dprime <= 5.1516586840152740479 (=perfect performance)
=> The higher the value, the better signals (go stims) were distinguished from noise (nogo stims)
(d' = 0: chance performance; negative d-primes: participant treated nontargets as targets and targets as nontargets)
pgC C-criterion in signal detection:The absolute value of c provides an indication of the strength of
the response bias/response style
negative: participant more likely to report that signal (go stims) is present (liberal response style)
may favor faster responding in speed-accuracy trade-off response paradigms
positive: favoring caution (conservative response style)
Partner-Good Pairings: Signal = Partner, Bad; Noise = Anything Else
pbHr Hitrate for partner/bad pairing
pbMr Miss rate for partner/bad pairing
pbFr False alarm rate for partner/bad pairing
pbCr Correct rejection rate for partner/bad pairing
pbZHr Z-value of hitrate for partner/bad pairing
pb z-fr Z-value of false alarm rate for partner/bad pairing
pbDPrime D' (parametric measure of discriminability btw. signals and noise) for partner/bad pairing
pbC Measure of response bias (c-criterium)
D-Prime Difference
dPrimeDiff The difference in dprime btw. 'partner-good' and 'partner-bad'
=> if d prime for 'partner-good' is larger than for 'partner-bad' (positive difference)
participant more closely associated Partner with Good than with Bad attributes
=> if d prime for 'partner-good' is smaller than for 'partner-bad' (negative difference)
participant more closely associated Partner with Bad than with Good attributes

Raw Data

File Name: partnergnat_raw*.iqdat

Data Fields

NameDescription
build Inquisit version number
computer.platform Device platform: win | mac |ios | android
computer.touch 0 = device has no touchscreen capabilities; 1 = device has touchscreen capabilities
computer.hasKeyboard 0 = no external keyboard detected; 1 = external keyboard detected
date Date the session was run
time Time the session was run
subject Participant ID
group Group number
session Session number
blockcode The name the current block (built-in Inquisit variable)
blocknum The number of the current block (built-in Inquisit variable)
trialcode The name of the currently recorded trial (built-in Inquisit variable)
trialnum The number of the currently recorded trial (built-in Inquisit variable)
trialnum is a built-in Inquisit variable; it counts all trials run
even those that do not store data to the data file.
stimulusItem The presented stimuli in order of trial presentation
response The participant's response (scancode of response buttons)
correct Accuracy of response: 1 = correct response; 0 = otherwise
latency The response latency (in ms); measured from: onset of stimuli

Parameters

The procedure can be adjusted by setting the following parameters.

NameDescriptionDefault
interTrialInterval The intertrial interval (in ms) btw. words400
responseTimeout The time (in ms) that participant have to respond600