___________________________________________________________________________________________________________________ Wolf and Sheep Task ___________________________________________________________________________________________________________________ Script Author: Katja Borchert, Ph.D. (katjab@millisecond.com) for Millisecond Software, LLC Date: 12-13-2023 last updated: 01-02-2025 by K. Borchert (katjab@millisecond.com) for Millisecond Software, LLC Script Copyright © 01-02-2025 Millisecond Software ___________________________________________________________________________________________________________________ BACKGROUND INFO ___________________________________________________________________________________________________________________ This script implements Millisecond Software's version of the Wolf and Sheep Task, a visual search task used to study agency attribution. The Millisecond script is based on Lisøy (2018). Researchers can select to run the task with an absolute screen size to ensure that distances stay the same across devices. The default setup uses proportional sizing. Go to section Defaults for more information. References: Lisøy, R.S. (2018). Seeing minds: a signal detection study of agency attribution in autism and psychosis. NTNU. (Dissertation) Lisøy RS, Biegler R, Haghish EF, Veckenstedt R, Moritz S, Pfuhl G. Seeing minds - a signal detection study of agency attribution along the autism-psychosis continuum. Cogn Neuropsychiatry. 2022 Sep;27(5):356-372. doi: 10.1080/13546805.2022.2075721. Epub 2022 May 17. PMID: 35579601. Lisøy, R.S., Pfuhl, G., Hope, W.N & Biegler, R. Seeing Minds: A Signal Detection Study of Agency Attribution in Autism and Psychosis downloaded from: https://osf.io/preprints/osf/6yn89 License: CC-By Attribution 4.0 International Gao, T., Newman, G. E., & Scholl, B. J. (2009). The psychophysics of chasing: A case study 603 in the perception of animacy. Cogn Psychol, 59(2), 154-179. 604 doi:10.1016/j.cogpsych.2009.03.001 ___________________________________________________________________________________________________________________ TASK DESCRIPTION ___________________________________________________________________________________________________________________ Participants watch videos with 16 red balls (the 'wolves') and one yellow ball (the 'sheep') moving around the screen. The task is to decide whether the sheep is hunted ('stalked') by one of the wolves. If participant reply 'yes' to the question of whether 'the sheep was hunted', they are further asked to identify the hunting wolf by selecting the letter printed on the red ball that represents the 'hunting wolf'. ___________________________________________________________________________________________________________________ DURATION ___________________________________________________________________________________________________________________ the default set-up of the script takes appr. 10 minutes to complete ___________________________________________________________________________________________________________________ DATA OUTPUT DICTIONARY ___________________________________________________________________________________________________________________ The fields in the data files are: (1) Raw data file: 'wolfandsheeptask_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: with the current subject id group: with the current group id 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. phase: "practice" vs. "test" trialCounterPerBlock: trialCounter for the current practice or test block itemNumber: the itemnumber of the currently presented video video: stores the presented video wolfPresent: 0 = no wolf; 1 = wolf present sheepCaught: 0 = sheep is not caught by the wolf; 1 = sheep is caught by the wolf actualWolf: the letter assigned to the current wolf (if any) //custom DVs: agencyRsp: "yes" or "no" (response to: ) selectedID: the selected letter ID of the wolf agencyAcc: 1 = agency response was correct (answer to question "is the sheep being hunted?") 0 = agency response was incorrect agencyRT: latency (in ms) of agency response; measured from onset of question "is the sheep being hunted?" idAcc: 1 = wolf identity was correctly identified (only relevant if participant chose 'yes' for agency response) 0 = wolf was not correctly identified idRT: latency (in ms) of identity response (measure from onset of question until 'submit' button is selected) //built-in DVs: response: the response of participant for the current trial correct: correctness of response (1 = correct, 0 = error) latency: response latency (in ms); !!!Note: the final trial data is recorded by trial.end (2) Summary data file: 'wolfandsheeptask_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) propCorrectAgency: proportion correct agency responses (out of 50 trials) numberIdentityResponses: number of YES responses to agency question (= number of identity responses) propCorrectID: proportion correct identity responses (out of numberIdentityResponses) propCorrectIDTransformed: arcsine square root transformed propCorrectID (see ) meanCorrAgencyRT: average correct agency response time (in ms) meanCorrIdRT: average correct identity response time (in ms) hitRate: hitrate (= saying 'yes' when the sheep was hunted) missRate: missrate (=saying 'no' when the sheep was hunted) - error response faRate: false alarm rate (= saying 'yes' when the sheep was NOT hunted) - error response crRate: correct rejection rate (= saying 'no' when the sheep was NOT hunted) zHitRate: calculates the z-score for the hitrate. Adjustments are made if the hitrate = 0 (increased to 0.005) or 1 (decreased to 0.995)* zFARate: calculates the z-score for the false alarm rate. Adjustments are made if the FArate = 0 (increased to 0.005) or 1 (decreased to 0.995)* dPrime: Computes d' (parametric measure of discriminability btw. signals and noise) => Range (in this script): -5.1516586840152740479 <= dprime <= 5.1516586840152740479 (=perfect performance) => The higher the value, the better signals ('wolf present') were overall distinguished from noise ('wolf absent') (d' = 0: chance performance; negative d-primes: participant treated noise as signals and signals as noise) => Note: Lisøy et al target a d-prime around '1' to get enough false alarms for a meaningful c-value (aka avoiding ceiling/floor effects) c: c-criterion in signal detection:The absolute value of c provides an indication of the strength of the subject bias (Anderson, 2015) => Response bias c in this case 'reflects the participants’ propensity to perceive the wolf as present or absent' (Lisøy et al) => negative when participant is more likely to report that signal ('wolf present') is present (propensity towards seeing agency), positive for favoring caution ('wolf absent') *Adjustments to z-scores as recommended by: 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) __________________________________________________________________________________________________________________ EXPERIMENTAL SET-UP ___________________________________________________________________________________________________________________ (1) Practice: - 2 warm-up trials (practice trials only run 10 wolves instead of 16) - by default, the two videos are run in fixed sequence 1. video: no wolf 2. video: wolf (letter 'L') In this script, participants get to watch the video again if they make a mistake for either the 'agency' and the 'identification' questions during practice (2) Test: - 50 videos (16 wolves) - by default, the videos are presented in a fixed sequence - half the videos present a wolf Trial Sequence (Test) -> get ready (2000) -> video (until video is done running), about 5seconds for each -> agency question: 'was the sheep being hunted?' until response (yes or no) -> if YES: identification question: the 16 letters are presented in a 4x4 matrix in the center of the screen and participants can select the letter. They can change their answer until they press the 'submit' button -> if NO: start of next trial sequence Note: there is no performance feedback given during test ___________________________________________________________________________________________________________________ STIMULI ___________________________________________________________________________________________________________________ provided by Lisøy et al (2018) under the CC-By Attribution 4.0 International License: https://osf.io/qaeyu/ ___________________________________________________________________________________________________________________ INSTRUCTIONS ___________________________________________________________________________________________________________________ the instructions are not original to Lisøy et al (2018); they are 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: //color parameter / canvasColor = black //Display color of the actively used portion of the screen (the 'canvas') //Note: if set to a color other than the screencolor, the active canvas //appears 'anchored' on the screen regardless of monitor size / screenColor = black //Color of the screen not used by the canvas ('inactive screen') / defaultTextColor = white //Default color of text items presented on active canvas //CANVAS SIZING PARAMETERS //sizing Parameters in RELATIVE measurements relative to CANVAS HEIGHT //NOTE: to run the script with ABSOLUTE screen measurements, go to 'defaults' and set //canvasSize to absolute measurements / videoSize = 100% //the proportional size of the video relative to active canvas //by default: covers the entire active canvas //timing parameters / getReadyDuration = 2000 //duration (in ms) of the get ready trials / feedbackDuration = 1000 //duration (in ms) of the practice feedback stims