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Record W4387476016 · doi:10.1145/3609468.3609471

Emergence of Maps in the Memories of Blind Navigation Agents

2023· article· en· W4387476016 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

VenueAI Matters · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLeverage (statistics)ForageForagingArtificial intelligenceTRIPS architectureComputer scienceCommunicationEcologyPsychologyZoologyBiology

Abstract

fetched live from OpenAlex

Decades of research into intelligent animal navigation posits that organisms build and maintain internal spatial representations (or maps) 1 of their environment, that enables the organism to determine and follow task-appropriate paths (Epstein, Patai, Julian, & Spiers, 2017; O'keefe & Nadel, 1978; Tollman, 1948). Hamsters, wolves, chimpanzees, and bats leverage prior exploration to determine and follow shortcuts they may never have taken before (Chapuis & Scardigli, 1993; Harten, Katz, Goldshtein, Handel, & Yovel, 2020; Menzel, 1973; Peters, 1976; Toledo et al., 2020). Even blind mole rats and animals rendered situationally-blind in dark environments demonstrate shortcut behaviors (Avni, Tzvaigrach, & Eilam, 2008; Kimchi, Etienne, & Terkel, 2004; Maaswinkel & Whishaw, 1999). Ants forage for food along meandering paths but take near-optimal return trips (Müller & Wehner, 1988), though there is some controversy about whether insects like ants and bees are capable of forming maps (Cheung et al., 2014; Cruse & Wehner, 2011).

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 score0.012
Threshold uncertainty score0.141

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 head0.134
GPT teacher head0.361
Teacher spread0.227 · 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