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Record W4408907580 · doi:10.1037/xge0001745

Characterizing age-related change in learning the value of cognitive effort.

2025· article· en· W4408907580 on OpenAlexaff
Camille V. Phaneuf, Isabelle M Jacques, Catherine Insel, A. Ross Otto, Leah H. Somerville

Bibliographic record

VenueJournal of Experimental Psychology General · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsMcGill University
FundersNational Institute of Mental HealthHarvard University
KeywordsPsychologyCognitionCognitive psychologyValue (mathematics)Developmental psychologyStatisticsNeuroscience

Abstract

fetched live from OpenAlex

= 150, ages 10-20 years) performance across two task-switching paradigms that manipulated the rewards offered for, and the difficulty of, engaging cognitive effort. In the primary experiment, reward cues were instructed but difficulty cues were learnable. In the secondary experiment, the reward and difficulty cues were both instructed, eliminating learning demands and effectively making the task easier. The primary experiment revealed that although less difficult contexts promoted greater accuracy at the group level, the regulation of cognitive effort according to higher and lower incentives emerged with age. Especially early in the task, older participants achieved greater accuracy for higher incentives. Younger participants, unexpectedly, achieved greater accuracy for lower incentives and adolescents performed similarly for each reward level. Nonetheless, participants of all ages self-reported trying their hardest for higher incentives, but only adults translated this aim into action. The secondary experiment revealed that in an overall easier task environment, cognitive effort did not become increasingly economical with age. Taken together, this pattern of findings suggests that different sources and amounts of information, and the conditions they are presented in, shape learning the value of cognitive effort from late childhood to early adulthood. (PsycInfo Database Record (c) 2025 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.493

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 integrity0.0000.001
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 head0.061
GPT teacher head0.453
Teacher spread0.393 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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