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Record W2118307493 · doi:10.1037/0735-7036.118.4.384

Reorientation in a Two-Dimensional Environment: II. Do Pigeons (Columba livia) Encode the Featural and Geometric Properties of a Two-Dimensional Schematic of a Room?

2004· article· en· W2118307493 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 comparative psychology · 2004
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Alberta
FundersNational Institute of Mental Health
KeywordsSchematicENCODEArtificial intelligenceComputer visionFeature (linguistics)CommunicationOrientation (vector space)MathematicsGeometryPsychologyComputer sciencePattern recognition (psychology)Biology

Abstract

fetched live from OpenAlex

Pigeons (Columba livia) searched for a hidden target area in images showing a schematic rectangular environment. The absolute position of the goal varied across trials but was constant relative to distinctive featural cues and geometric properties of the environment. Pigeons learned to use both of these properties to locate the goal. Transformation tests showed that pigeons could use either the color or shape of the features, but performance was better with color cues present. Pigeons could also use a single featural cue at an incorrect corner to distinguish between the correct corner and the geometrically equivalent corner; this indicates that they did not simply use the feature at the correct corner as a beacon. Interestingly, pigeons that were trained with features spontaneously encoded geometry. The encoded geometric information withstood vertical translations but not orientation transformations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.345

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 head0.038
GPT teacher head0.313
Teacher spread0.275 · 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