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Record W2110673938 · doi:10.1037/0097-7403.31.2.142

What Do Rats Learn About the Geometry of Object Arrays? Tests With Exploratory Behavior.

2005· article· en· W2110673938 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Experimental Psychology Animal Behavior Processes · 2005
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHabituationObject (grammar)Square (algebra)Position (finance)PsychologyCommunicationCognitive neuroscience of visual object recognitionGeometryComputer visionArtificial intelligenceMathematicsComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Six experiments using habituation of exploratory behavior tested whether disoriented rats foraging in a large arena encode the shapes of arrays of objects. Rats did not respond to changes in position of a single object, but they responded to a change in object color and to a change in position of 1 object in a square array, as in previous research (e.g., C. Thinus-Blanc et al., 1987). Rats also responded to an expansion of a square array, suggesting that they encoded sets of interobject distances rather than overall shape. In Experiments 4-6, rats did not respond to changes in sense of a triangular array that maintained interobject distances and angles. Shapes of object arrays are encoded differently from shapes of enclosures.

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.

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 score0.009
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.107
GPT teacher head0.401
Teacher spread0.294 · 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