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Record W1481188462 · doi:10.3389/fpsyg.2015.00823

Adjustment of speaker’s referential expressions to an addressee’s likely knowledge and link with theory of mind abilities

2015· article· en· W1481188462 on OpenAlexafffund
Amélie M. Achim, Marion Fossard, Sophie Couture, André Achim

Bibliographic record

VenueFrontiers in Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de QuébecUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTheory of mindPsychologyCognitionSocial cognitionTask (project management)Cognitive psychologyCommon groundTest (biology)Probabilistic logicSocial psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

To communicate cooperatively, speakers must determine what constitutes the common ground with their addressee and adapt their referential choices accordingly. Assessing another person's knowledge requires a social cognition ability termed theory of mind (ToM). This study relies on a novel referential communication task requiring probabilistic inferences of the knowledge already held by an addressee prior to the study. Forty participants were asked to present 10 movie characters and the addressee, who had the same characters in a random order, was asked to place them in order. ToM and other aspects of social cognition were also assessed. Participants used more information when presenting likely unknown than likely known movie characters. They particularly increased their use of physical descriptors, which most often accompanied movie-related information. Interestingly, a significant relationship emerged between our ToM test and the increased amount of information given for the likely unknown characters. These results suggest that speakers use ToM to infer their addressee's likely knowledge and accordingly adapt their referential expressions.

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)0.001
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 score0.610
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.049
GPT teacher head0.342
Teacher spread0.294 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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