___________________________________________________________________________________________________________________
*Reverse Correlation 2FC*
*Image Presentation Template Script ONLY*
___________________________________________________________________________________________________________________
Script Author: Katja Borchert, Ph.D. (katjab@millisecond.com) for Millisecond Software, LLC
Date: 05-20-2020
last updated: 02-28-2022 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC
Script Copyright © 02-28-2022 Millisecond Software
___________________________________________________________________________________________________________________
BACKGROUND INFO
___________________________________________________________________________________________________________________
This script implements a template procedure to present stimuli for a 2 Forced-Choice (2FC) Reverse Correlation Task.
The template script investigates people's mental representation of a 'biracial' face but can be easily adapted
to investigate a different target concept.
The Reverse Correlation Paradigm is a data-driven method that helps generate visual proxies of people's
mental representations of social categories. It is based on Signal Detection Theory, but instead of a-priori
deciding what is the the signal and what is the noise, the Reverse procedure attempts to find what the participant
perceives as signal and what as noise (see Brinkman, Todorov & Dotsch, 2017, for an in depth discussion).
2 FC Correlation procedure:
For a 2FC Reverse Correlation procedure, a large amount of image pairs are prepared (see step 1 below).
Each image pair is based on the same base image but each image pair has been created with a slightly
different noise pattern (noise pattern = overlay of random black/white pixel noise).
The individual images of a pair have been created with the same noise pattern, but in one case, the random
noise pattern was the inverse of the other image.
Participants are presented all image pairs and have to decide which of the two images of an image pair
best represents the target category (e.g. biracial face).
The generated data can then be analyzed to generate the proxy visual image of the mental represention
of the target category.
3 Steps:
(1) Stimuli Preparation:
The stimuli for a Reversed Correlation procedure can be generated in 'R' (free software program).
Follow instructions provided at:
http://www.rondotsch.nl/rcicr/
Example: the stimuli for this script could be generated by using the following R code:
___________________________________________________________________________________________
# Load reverse correlation package ("rcicr")
library(rcisr)
# assign the base image to 'base_face_files'
base_face_files <- list('base'='biracial base image.jpeg')
#generate the stimuli: R will generate a folder of stimuli
generateStimuli2IFC(base_face_files, n_trials = 200)
___________________________________________________________________________________________
(2) Stimuli Presentation
This script provides a procedure to present the generated stimuli in a 2FC Reverse Correlation
Procedure.
By default this script runs 200 images. The images are stored in item.stims_orig and item.stims_inverse
and can easily be exchanged for new ones. The number of trials automatically adapts to the
the number of images stored under item.stims_orig (Note: the script works best with an even number of trials).
(3) Analyze Data
Prepare Inquisit Data (raw data files) for analysis:
- merge your RAW data files (if you want to look at the data across participants)
- convert iqdat data files to .cvs files
Data Analyses is done in 'R'.
Follow instructions provided at:
http://www.rondotsch.nl/rcicr/
This is the R-code that could be used to generate the cis image based on data collected
with this Inquisit script:
(Note: the code is based on the demo provided at: http://www.rondotsch.nl/rcicr/)
__________________________________________________________________________________________
# Load reverse correlation package ("rcicr")
library(rcicr)
# Base image name used during stimulus generation
baseimage <- 'base'
# File containing the contrast parameters (this file was created during stimulus generation)
rdata <- 'rcic_seed_1_time_may_18_2020_13_41.rdata'
# Load response data
# Note: the datafile should be a *.csv file
# Load Inquisit data file in Excel and save as *.csv file
# IMPORTANT: use the name of your data file instead of 'datafile.csv'
responsedata <- read.csv('datafile.csv')
# Batch generate classification images by subject
cis <- batchGenerateCI2IFC(responsedata, 'subject', 'index', 'responseCode', baseimage, rdata)
# Batch generate classification images by trait (here: targetCategory)
cis <- batchGenerateCI2IFC(responsedata, 'targetCategory', 'index', 'responseCode', baseimage, rdata)
###Result: a folder 'cis' will be generated by R that contains the generated image file(s)
__________________________________________________________________________________________
References
L. Brinkman, A. Todorov & R. Dotsch (2017) Visualising mental
representations: A primer on noise-based reverse correlation in social psychology, European
Review of Social Psychology, 28:1, 333-361, DOI: 10.1080/10463283.2017.1381469
http://www.rondotsch.nl/rcicr/
https://cran.r-project.org/web/packages/rcicr/rcicr.pdf
___________________________________________________________________________________________________________________
TASK DESCRIPTION
___________________________________________________________________________________________________________________
This script presents 200 (default) pairs of images to participant. For each pair, participants have to decide
which image best represents a 'biracial' face.
___________________________________________________________________________________________________________________
DURATION
___________________________________________________________________________________________________________________
the default set-up of the script takes appr. 8 minutes to complete
___________________________________________________________________________________________________________________
DATA FILE INFORMATION
___________________________________________________________________________________________________________________
The default data stored in the data files are:
(1) Raw data file: 'reversecorrelation_2fc_raw*.iqdat' (a separate file for each participant)*
build: The specific Inquisit version used (the 'build') that was run
computer.platform: the platform the script was run on (win/mac/ios/android)
date, time: date and time script was run
subject, group: with the current subject/groupnumber
session: with the current session id
blockcode, blocknum: the name and number of the current block (built-in Inquisit variable)
trialcode, trialnum: the name and number of the currently recorded trial (built-in Inquisit variable)
Note: trialnum is a built-in Inquisit variable; it counts all trials run; even those
that do not store data to the data file.
(parameter) targetCategory: the target category under investigation (change under editable parameters)
(parameter) baseImageFileName: the name of the base image image (change under editable parameters)
trialCount: custom trialcounter
index: the itemnumber of the presented image pair
sequenceNumber: the official sequence number of the presented image pair
stimulusitem.1: the presented original stimulus
stimulusitem.2: the presented inverse stimulus
response: the participant's response (the selected image)
responseCode: the translated response
1 = participant selected the orig stimulus
-1 = participant selected the inverse stimulus
latency: the response latency (in ms); measured from: onset of images
(2) Summary data file: 'reversecorrelation_2fc_summary*.iqdat' (a separate file for each participant)*
inquisit.version: Inquisit version run
computer.platform: the platform the script was run on (win/mac/ios/android)
startDate: date script was run
startTime: time script was started
subjectid: assigned subject id number
groupid: assigned group id number
sessionid: assigned session id number
elapsedTime: time it took to run script (in ms); measured from onset to offset of script
completed: 0 = script was not completed (prematurely aborted);
1 = script was completed (all conditions run)
* separate data files: to change to one data file for all participants (on Inquisit Lab only), go to section
"DATA" and follow further instructions
___________________________________________________________________________________________________________________
EXPERIMENTAL SET-UP
___________________________________________________________________________________________________________________
This script runs 1 blocks of 200 trials (default: the number of trials run adapts to the number of images).
Each trial presents 1 pair of face images, consisting of one "original" and one "inverse" image (Brinkman, 2017, p.335).
Half the trials present the original image on the left side; the other half of trials present the original images
on the right side.
Images are on screen until one of the images is selected (= 2 forced choice trial).
___________________________________________________________________________________________________________________
STIMULI
___________________________________________________________________________________________________________________
provided by Millisecond Software.
New Stimuli can be generated in R. Follow instructions at:
http://www.rondotsch.nl/rcicr/
___________________________________________________________________________________________________________________
INSTRUCTIONS
___________________________________________________________________________________________________________________
provided by Millisecond Software - can be edited under 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:
