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Record W3080924586 · doi:10.1093/texcom/tgaa054

Restricting Visual Exploration Directly Impedes Neural Activity, Functional Connectivity, and Memory

2020· article· en· W3080924586 on OpenAlexafffund
Zhong‐Xu Liu, R. Shayna Rosenbaum, Jennifer D. Ryan

Bibliographic record

VenueCerebral Cortex Communications · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsBaycrest HospitalUniversity of TorontoYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada First Research Excellence Fund
KeywordsFunctional connectivityComputer scienceCognitive psychologyPsychologyNeuroscienceNeural activityCognitive scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

We move our eyes to explore the visual world, extract information, and create memories. The number of gaze fixations-the stops that the eyes make-has been shown to correlate with activity in the hippocampus, a region critical for memory, and with later recognition memory. Here, we combined eyetracking with fMRI to provide direct evidence for the relationships between gaze fixations, neural activity, and memory during scene viewing. Compared to free viewing, fixating a single location reduced: 1) subsequent memory, 2) neural activity along the ventral visual stream into the hippocampus, 3) neural similarity between effects of subsequent memory and visual exploration, and 4) functional connectivity among the hippocampus, parahippocampal place area, and other cortical regions. Gaze fixations were uniquely related to hippocampal activity, even after controlling for neural effects due to subsequent memory. Therefore, this study provides key causal evidence supporting the notion that the oculomotor and memory systems are intrinsically related at both the behavioral and neural level. Individual gaze fixations may provide the basic unit of information on which memory binding processes operate.

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)0.001
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 score0.071
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.237
GPT teacher head0.350
Teacher spread0.113 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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