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Record W1561658484 · doi:10.1111/psyp.12174

Late electrophysiological modulations of feature‐based attention to object shapes

2014· article· en· W1561658484 on OpenAlexaff
Bobby Stojanoski, Matthias Niemeier

Bibliographic record

VenuePsychophysiology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsThe Scarborough HospitalYork UniversityUniversity of TorontoWestern University
Fundersnot available
KeywordsPsychologyPerceptionObject (grammar)Feature (linguistics)CommunicationElectrophysiologyCognitive psychologyModulation (music)Visual perceptionPattern recognition (psychology)Artificial intelligenceNeuroscienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Feature-based attention has been shown to aid object perception. Our previous ERP effects revealed temporally late feature-based modulation in response to objects relative to motion. The aim of the current study was to confirm the timing of feature-based influences on object perception while cueing within the feature dimension of shape. Participants were told to expect either "pillow" or "flower" objects embedded among random white and black lines. Participants more accurately reported the object's main color for valid compared to invalid shapes. ERPs revealed modulation from 252-502 ms, from occipital to frontal electrodes. Our results are consistent with previous findings examining the time course for processing similar stimuli (illusory contours). Our results provide novel insights into how attending to features of higher complexity aids object perception presumably via feed-forward and feedback mechanisms along the visual hierarchy.

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)0.000
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 score0.160
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.043
GPT teacher head0.334
Teacher spread0.292 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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