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Record W2727428593 · doi:10.1073/pnas.1700574114

Endogenous orienting in the archer fish

2017· article· en· W2727428593 on OpenAlexaff
William Saban, Liora Sekely, Raymond M. Klein, Shai Gabay

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsDalhousie University
FundersIsrael Science Foundation
KeywordsInhibition of returnFacilitationNeocortexPsychologyStimulus (psychology)Orienting responseCognitive psychologyFish <Actinopterygii>NeuroscienceCognitionBiologyHabituationVisual attention

Abstract

fetched live from OpenAlex

The literature has long emphasized the neocortex's role in volitional processes. In this work, we examined endogenous orienting in an evolutionarily older species, the archer fish, which lacks neocortex-like cells. We used Posner's classic endogenous cuing task, in which a centrally presented, spatially informative cue is followed by a target. The fish responded to the target by shooting a stream of water at it. Interestingly, the fish demonstrated a human-like "volitional" facilitation effect: their reaction times to targets that appeared on the side indicated by the precue were faster than their reaction times to targets on the opposite side. The fish also exhibited inhibition of return, an aftermath of orienting that commonly emerges only in reflexive orienting tasks in human participants. We believe that this pattern demonstrates the acquisition of an arbitrary connection between spatial orienting and a nonspatial feature of a centrally presented stimulus in nonprimate species. In the literature on human attention, orienting in response to such contingencies has been strongly associated with volitional control. We discuss the implications of these results for the evolution of orienting, and for the study of volitional processes in all species, including humans.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 science0.0010.000
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 head0.220
GPT teacher head0.370
Teacher spread0.151 · 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 designTheoretical or conceptual
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

Citations46
Published2017
Admission routes1
Has abstractyes

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