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Record W2970035359 · doi:10.1037/bne0000341

Lesions of the head direction cell system impair direction discrimination.

2019· article· en· W2970035359 on OpenAlexaff
Anna E. Smith, Olivia A. Cheek, Emily L. C. Sweet, Paul A. Dudchenko, Emma R. Wood

Bibliographic record

VenueBehavioral Neuroscience · 2019
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsDiscovery Centre
FundersBiotechnology and Biological Sciences Research Council
KeywordsMammillary bodyLesionNeurosciencePsychologyHippocampal formationTask (project management)AnatomyAudiologyHippocampusBiologyMedicine

Abstract

fetched live from OpenAlex

Previous results suggest that directional information from the head direction cell circuit may inform hippocampal place cell firing when an animal is confronted with visually identical environments. To investigate whether such information might also be essential for spatial behavior, we tested adult, male Lister Hooded rats that had received either bilateral lateral mammillary nuclei (LMN) lesions or sham lesions on a four-way, conditional odor-location discrimination in compartments arranged at 60° to one another. We found that significantly fewer rats in the LMN lesion group were able to learn the task compared to the Sham group. We also found that the extent of the behavioral impairment was highly correlated with the degree of tissue loss in the LMN resulting from the lesion. Animals with LMN lesions were also impaired in a nonmatching-to-sample task in a T maze, and the extent of impairment likewise depended on the extent of the lesion. Performance in the odor-location and T-maze tasks was not affected by tissue loss in the medial mammillary nuclei. Together, these results indicate that the LMN, a key node in the head direction circuit, is critical for solving a spatial task that requires a directional discrimination. (PsycINFO Database Record (c) 2019 APA, 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 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 score0.031
Threshold uncertainty score0.470

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.121
GPT teacher head0.339
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 teacher head, 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

Citations4
Published2019
Admission routes1
Has abstractyes

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