Partner Attraction GNAT

Script Author: Marisa Okano

Credits:
Millisecond thanks Marisa Okano for sharing her script!

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

Script Copyright © Millisecond Software, LLC

Background

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).

In general, the GNAT uses uses the Go-Nogo framework of responding to signal and noise stimuli to investigate implicit bias. 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 'physical attractiveness' (the target signal) is suggested if the dprime measure in the condition 'Physical Attractiveness-I like' (both of these categories are signals) is greater than the dprime measure in the condition 'Physical Attractiveness-I don't like' (both of these categories are signals).

This script investigates the following three categories: •PA = physical appearance •SS = social status •PE = personality by pairing them with the attribute categories 'I like' vs. 'I don't like'

The implemented procedure of the “Attraction pGNAT” is based on Eastwick et al (2011).

References

Attraction Pgnat

Adjustments To Z-Scores

Duration

20 minutes

Description

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.

Procedure

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

Scoring

Zscore Adjustments

Summary Data

File Name: attraction_pgnat_summary*.iqdat

Data Fields

Raw Data

File Name: attraction_pgnat_raw*.iqdat

Data Fields