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Long-Lasting Working Memories of Obstacles Established by Foreleg Stepping in Walking Cats Require Area 5 of the Posterior Parietal Cortex

2009· article· en· W2002141099 on OpenAlexafffund
David A. McVea, Andrew J. Taylor, K. G. Pearson

Bibliographic record

VenueJournal of Neuroscience · 2009
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsObstacleWorking memoryPosterior parietal cortexNeuroscienceCortex (anatomy)PsychologyComputer scienceCognition

Abstract

fetched live from OpenAlex

Walking animals rely on working memory to avoid obstacles. One example is the stepping of the hindlegs of quadrupeds over an obstacle. In this case, the obstacle is not visible at the time of hindleg stepping, because of its position between the fore and hindlegs, and working memory must be used to avoid it. We have previously shown that this memory is very precise and surprisingly long-lasting and that it depends on the stepping of the forelegs over the obstacle for its initiation. In this study, we test the hypothesis that area 5 in the posterior parietal cortex of cats is necessary for the maintenance of this long-lasting working memory. We report that small bilateral lesions to area 5 do not affect the amplitude of normal stepping of the hindlegs over obstacles, but they profoundly reduce the long-lasting working memory of obstacles. We propose that inputs to area 5 associated with foreleg stepping initiate long-lasting activity that maintains the memory of obstacle height in another brain region to guide the hindlegs over obstacles.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.295
Teacher spread0.218 · 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 designBench or experimental
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

Citations48
Published2009
Admission routes2
Has abstractyes

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