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Record W4406114057 · doi:10.3389/fpsyg.2024.1459247

Role playing in human evolution: from life to art, and everything in between

2025· article· en· W4406114057 on OpenAlexafffund
Steven Brown

Bibliographic record

VenueFrontiers in Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCognitive scienceCognitive psychology

Abstract

fetched live from OpenAlex

Role playing is a central, but underappreciated, process in human evolution. It is a feature not only of the theatrical arts, but of everyday social interactions. While some role playing is limited to enacting various personas of the self (e.g., wife, accountant, mother), others involve an impersonation of people. The most basic form of impersonation is proto-acting, which refers to a transient engagement in character portrayal, such as when we quote a friend during a conversation. During proto-acting, we “act as” some other person. However, there are other means of acting in a similar manner to another person in which we do not impersonate them, but merely emulate their behavior. This might happen when we learn a motor skill from a teacher or conform to the consumer choices of the masses. This follower-based process of “acting like” is a critically important mechanism in cultural evolution since it leads to social conformity and the homogenization of group behavior. I argue that the evolutionary transition from “acting like” (emulation) to “acting as” (impersonation) occurred via the emergence of pantomime and its narrative depiction of the actions of other people. This was probably the first step toward impersonating someone, leading initially to proto-acting and later to theatrical performance in human cultures. Overall, the study of human evolution needs to give greater consideration to role playing and its diverse manifestations in life and art.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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