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Record W2063022343 · doi:10.1371/journal.pone.0093653

Comparisons of an Open-Ended vs. Forced-Choice ‘Mind Reading’ Task: Implications for Measuring Perspective-Taking and Emotion Recognition

2014· article· en· W2063022343 on OpenAlexafffund
Tracy G. Cassels, Susan Birch

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTwo-alternative forced choiceCognitive psychologyPerspective (graphical)Task (project management)RecallPsychologyReading (process)EmpathyTheory of mindTypically developingDevelopmental psychologyCognitionComputer scienceSocial psychologyArtificial intelligenceAutism

Abstract

fetched live from OpenAlex

Perspective-taking and emotion recognition are essential for successful social development and have been the focus of developmental research for many years. Although the two abilities often overlap, they are distinct and our understanding of these abilities critically rests upon the efficacy of existing measures. Lessons from the literature differentiating recall versus recognition memory tasks led us to hypothesize that an open-ended emotion recognition measure would be less reliant on compensatory strategies and hence a more specific measure of emotion recognition abilities than a forced-choice task. To this end, we compared an open-ended version of the Reading the Mind in the Eyes Task with the original forced-choice version in two studies: 118 typically-developing 4- to 8-year-olds (Study 1) and 139 5- to 12-year-olds; 85 typically-developing and 54 with learning disorders (Study 2). We found that the open-ended version of the task was a better predictor of empathy and more reliably discriminated typically-developing children from those with learning disorders. As a whole, the results suggest that the open-ended version is a more sensitive measure of emotion recognition specifically.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.159
GPT teacher head0.340
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2014
Admission routes2
Has abstractyes

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