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Record W3005613942 · doi:10.1037/pspa0000186

So difficult to smile: Why unhappy people avoid enjoyable activities.

2020· article· en· W3005613942 on OpenAlexaff
Hao Shen, Aparna A. Labroo, Robert S. Wyer

Bibliographic record

VenueJournal of Personality and Social Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPsychologyFeelingPsycINFOMoodExpression (computer science)MetacognitionSocial psychologyAffect (linguistics)Facial expressionCognitionCommunication

Abstract

fetched live from OpenAlex

Engaging in an enjoyable activity is often effective in reducing or eliminating a negative mood. However, imagining this activity before deciding whether to perform it can decrease unhappy people's willingness to engage in it. The facial expression that accompanies a negative mood (and the muscles activated by the expression) can conflict with the expression that is elicited by imagining the performance of an enjoyable activity, evoking subjective feelings of difficulty of imagining it. Unhappy people misattribute these feelings of difficulty to the enjoyable activity itself, decreasing their desire to engage in the activity. The effects of metacognitive difficulty are eliminated when (a) unhappy people attribute difficulty to something other than the enjoyable activity and (b) focus their attention on the outcome of the activity rather than the process of engaging in it. Moreover, when an irrelevant factor activates smile-related features while performing the activity, the experience of difficulty is attenuated and its effect on aversion to the activity is not apparent. In contrast, unhappy people also find it easy to imagine an unenjoyable activity and consequently evaluate it more favorably after imagining it. Seven studies demonstrated the role of these metacognitive experiences and their implications for research on affect regulation. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.120
GPT teacher head0.446
Teacher spread0.327 · 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.

Study designNot applicable
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

Citations8
Published2020
Admission routes1
Has abstractyes

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