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Record W4235292190 · doi:10.31231/osf.io/pvk3u

Mountains and Valleys: Binocular Rivalry and the Flow of Experience

2017· preprint· en· W4235292190 on OpenAlexaff
Diego Cosmelli, Evan Thompson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersComisión Nacional de Investigación Científica y Tecnológica
KeywordsBinocular rivalryRivalryPerceptionConsciousnessCognitive psychologyPsychologyDynamics (music)Phenomenology (philosophy)Relation (database)Cognitive scienceVisual perceptionEpistemologyComputer scienceNeurosciencePhilosophyEconomics

Abstract

fetched live from OpenAlex

Binocular rivalry provides a useful situation for studying the relation between the temporal flow of conscious experience and the temporal dynamics of neural activity. After proposing a phenomenological framework for understanding temporal aspects of consciousness, we review experimental research on multistable perception and binocular rivalry, singling out various methodological, theoretical, and empirical aspects of this research relevant to studying the flow of experience. We then review an experimental study from our group explicitly concerned with relating the temporal dynamics of rivalrous experience to the temporal dynamics of cortical activity. Drawing attention to the importance of dealing with ongoing activity and its inherent changing nature at both phenomenological and neurodynamical levels, we argue that the notions of recurrence and variability are pertinent to understanding rivalry in particular and the flow of experience in general.

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)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.354
Teacher spread0.265 · 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
Published2017
Admission routes1
Has abstractyes

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