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Record W4414722795 · doi:10.1101/2025.09.29.679121

Concept Learning Builds Behaviourally Relevant Attentional Templates

2025· preprint· en· W4414722795 on OpenAlexaff
Melisa Gumus, Zoey Zhi Yi Lee, Michael L. Mack

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCued speechTask (project management)Concept learningSequence learningMechanism (biology)Feature (linguistics)PhenomenonPerception

Abstract

fetched live from OpenAlex

Abstract Attention optimizes learning by filtering relevant information to build conceptual knowledge. However, how learned concepts, once encoded in memory, subsequently guide attentional processes remains an intriguing question. We propose that concept learning leads to the emergence of attentional templates that store goal-relevant representations, thereby actively guiding attention allocation. Participants completed two separate learning tasks and a test, wherein each trial began with a cue, indicating which learning task should be employed. Random test trials included a probe instead of concept specific features: a small arrow appeared at a feature location that was relevant (i.e., valid) or irrelevant (i.e., invalid) for the cued task. Successful learners were faster at responding to valid probes than invalid, demonstrating the deployment of concept-specific attentional templates. Importantly, the efficiency of this attention allocation was tied to concept learning success, with higher learning performance yielding greater response time benefits at test. Thus, our results reveal that learning builds behaviourally relevant attentional templates, and subsequently, learned concepts in memory guide attention by deploying these templates, a phenomenon that we introduce as learning-guided attention. This work provides novel insights into the dynamic interplay between learning, memory, and attention. Significance Statement Extensive work shows that attention selects the most relevant information while learning new knowledge. Theoretically, the interaction between learning and attention is bidirectional; learned knowledge, in turn, guides attention to relevant information for that context. However, the mechanism by which knowledge in memory directs attention has remained largely unexplored. By developing an experimental paradigm that bridges the learning and attention literatures, we demonstrate that learning builds “attentional templates” which capture what is relevant in a specific learning context. While utilizing learned knowledge, individuals deploy these templates to allocate their attention to the most relevant information for a given situation. We introduce this phenomenon as learning-guided attention , providing novel insights into the dynamic interplay between learning, memory, and attention.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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