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Record W4402660621 · doi:10.1101/2024.09.12.612641

Behavioral Evidence for Two Modes of Attention

2024· preprint· en· W4402660621 on OpenAlexaff
Akanksha Gupta, Tomas E. Matthews, Virginia B. Penhune, Benjamin Morillon

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsConcordia University
FundersAgence Nationale de la RechercheFondation Pour l'AuditionAix-Marseille Université
KeywordsRhythmCategorizationPerceptionStimulus (psychology)PsychologyCognitive psychologyMode (computer interface)Sensory systemCommunicationComputer scienceNeuroscienceArtificial intelligenceHuman–computer interactionPhysicsAcoustics

Abstract

fetched live from OpenAlex

Abstract Attention modulates sensory gain to select and optimize the processing of behaviorally relevant events. It has been hypothesized that attention can operate in either a rhythmic or continuous mode, depending on the nature of sensory stimulation. Despite this conceptual framework, direct behavioral evidence has been scarce. Our study explores when attention operates in a rhythmic mode through a series of nine interrelated behavioral experiments with varying stream lengths, stimulus types, attended features, and tasks. The rhythmic mode optimally operates at approximately 1.5 Hz and is prevalent in perceptual tasks involving long (> 7 s) auditory streams. Our results are supported by a model of coupled oscillators, illustrating that variations in the system’s noise level can induce shifts between continuous and rhythmic modes. Finally, the rhythmic mode is absent in syllable categorization tasks. Overall, this study provides empirical evidence for two modes of attention and defines their conditions of operation.

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)0.007
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.311
Teacher spread0.236 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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