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Record W2991245191 · doi:10.1162/jocn_a_01506

Dissecting Cognitive Operations in Difficult Visual Search Using Response-locked Posterior Contralateral Negativity Event-related Potentials

2019· article· en· W2991245191 on OpenAlexaff
Brandi Lee Drisdelle, Pierre Jolicœur

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyLatency (audio)Stimulus (psychology)Event-related potentialNegativity effectVisual searchCognitionGray (unit)AmplitudeCognitive psychologyAudiologyNeuroscienceCommunicationComputer science

Abstract

fetched live from OpenAlex

We bisected the sequence of processing into operations taking place before or after the engagement of visual-spatial attention during a difficult search task using event-related potentials. We were able to assign variance in RTs associated with experimental factor effects to phases of processing by examining stimulus-locked (SLpcN) and response-locked (RLpcN) posterior contralateral negativity. Participants searched for a gray square with one gap among gray squares with two gaps. The number of displayed items (set size) and the number of response alternatives were varied. Both experimental manipulations affected the onset latency of the RLpcN, whereas the SLpcN showed small or no latency effects, suggesting they had effects after the initial deployment of attention. Moreover, amplitude effects in the RLpcN and SLpcN behaved similarly. Most importantly, different aspects of the RLpcN dissociated the experimental manipulations: Set size primarily affected processing between RLpcN onset and peak amplitude of the RLpcN, whereas the number of response alternatives affected the onset latency and the latency of peak amplitude of RLpcN. These results show how RLpcN activity can dissociate factor effects that are not separable with SLpcN activity during difficult search.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.433
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.427
Teacher spread0.325 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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