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Record W3049030381 · doi:10.1037/xge0000842

It’s all relative: Reward-induced cognitive control modulation depends on context.

2020· article· en· W3049030381 on OpenAlexafffund
A. Ross Otto, Eliana Vassena

Bibliographic record

VenueJournal of Experimental Psychology General · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsCognitionPsychologyNeuroeconomicsCognitive psychologyIncentiveContext (archaeology)Value (mathematics)Relative valueSocial psychologyMicroeconomicsEconomicsComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Although people seek to avoid expenditure of cognitive effort, reward incentives can increase investment of processing resources in challenging situations that require cognitive control, resulting in improved performance. At the same time, subjective value is relative, rather than absolute: The value of a reward is increased if the local context is reward-poor versus reward-rich. Although this notion is supported by work in economics and psychology, we propose that reward relativity should also play a critical role in the cost-benefit computations that inform cognitive effort allocation. Here we demonstrate that reward-induced cognitive effort allocation in a task-switching paradigm is sensitive to reward context, consistent with the notion of relative value. Informed by predictions of a computational model of divisive reward normalization, we demonstrate that reward-induced switch cost reductions depend critically upon reward context, such that the same reward amount engenders greater control allocation in impoverished versus rich reward context. Succinctly, these results confirm that reward relativity factors into the value computation driving effort allocation, revealing that motivated cognitive control, like choice, is all relative. (PsycInfo Database Record (c) 2021 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.893

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 integrity0.0000.001
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 head0.271
GPT teacher head0.459
Teacher spread0.188 · 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 designBench or experimental
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

Citations58
Published2020
Admission routes2
Has abstractyes

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