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Animal Navigation: A Synthesis

2011· book-chapter· en· W16659848 on OpenAlexaff
Jan Wiener, Sara J. Shettleworth, Verner P. Bingman, Ken Cheng, Susan D. Healy, Lucia F. Jacobs, Kathryn J. Jeffery, Hanspeter A. Mallot, Randolf Menzel, Nora S. Newcombe

Bibliographic record

VenueThe MIT Press eBooks · 2011
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Navigation, the ability to organize behavior adaptively to move from one place to another, appeared early in the evolution of animals and occurs in all mobile species. At the simplest level, navigation may require only movement toward or away from a stimulus, but at a more sophisticated level, it involves the formation of complex internal representations of the environment, the subject's position within it, the location of goals, the various routes from current position to goal and possible obstacles along the way. The vast array of navigational capabilities in various species has made it challenging for students of comparative cognition to formulate unifying frameworks to describe and understand these capabilities, although the variety also confers an exciting opportunity for asking comparative questions that are hypothesis driven. A unifying framework, the navigation toolbox, is proposed to provide a way of formulating common underlying principles that operate across many different taxa. The toolbox contains a hierarchy of representations and processes, ranging in complexity from simple and phylogenetically old sensorimotor processes, through the formation of navigational primitives such as orientation or landmark recognition, up to complex cognitive constructs such as cognitive maps, and fi nally culminating in the human capacity for symbolic representation and language. Each element in the hierarchy is positioned at a given level by virtue of being constructed from elements in the lower levels and having newly synthesized spatial semantic contents in the representations that were not present in the lower levels. In studying individual species, the challenge is to determine how given elements are implemented in that species, in view of its particular behavioral and anatomical constraints. The challenge for the fi eld as a whole is to understand the semantic structure of spatial representations in general, which ultimately entails understanding the behavioral and neural mechanisms by which semantic content is synthesized from sensory inputs, stored, and used to generate behavior. © 2011 Massachusetts Institute of Technology and the Frankfurt Institute for Advanced Studies. All rights reserved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.004

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 head0.053
GPT teacher head0.214
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2011
Admission routes1
Has abstractyes

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