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Record W2950143659 · doi:10.1037/abn0000366

The valuation of social rewards in schizophrenia.

2018· article· en· W2950143659 on OpenAlexaff
Lauren T. Catalano, Erin A. Heerey, James M. Gold

Bibliographic record

VenueJournal of Abnormal Psychology · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsWestern University
FundersNational Institute of Mental Health
KeywordsPsychologyValuation (finance)Schizophrenia (object-oriented programming)Clinical psychologyPsychiatryCognitive psychologyPsychotherapistFinance

Abstract

fetched live from OpenAlex

Social impairment in schizophrenia is often thought to reflect poor social cognition. Here we examine responses to social rewards, an aspect of social functioning that is not featured prominently in the literature. The goal of this experiment was to explore whether people with schizophrenia (a) undervalue social rewards, and (b) whether the undervaluation of social rewards was related to motivation and pleasure deficits in schizophrenia and decreased social functioning. People with schizophrenia and healthy participants completed a game (Shore & Heerey, 2011) to explore preferences for different types of social (polite and genuine smiles) and nonsocial (monetary) rewards from computerized opponents. Preferences for reward types were quantified for each participant based on choice behavior during the game. Participants also completed a smile discrimination task to assess their ability to discriminate these types of smiles. Analyses revealed that people with schizophrenia (N = 41) treated genuine smiles as significantly less rewarding than did healthy participants (N = 29), despite showing a similar preference for monetary rewards. The undervaluation of social rewards was not related to the ability to discriminate between the smiles. The current findings provide preliminary evidence of reduced social reward valuation among individuals with schizophrenia, which may have implications for behavior in face-to-face social interactions. (PsycINFO Database Record

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 score0.877
Threshold uncertainty score0.168

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 head0.394
Teacher spread0.344 · 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

Citations46
Published2018
Admission routes1
Has abstractyes

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