MétaCan
Menu
Back to cohort
Record W2086678739 · doi:10.1073/pnas.0308528101

Visual memory task for rats reveals an essential role for hippocampus and perirhinal cortex

2004· article· en· W2086678739 on OpenAlexaff
Glen T. Prusky, Robert M. Douglas, Lars H. Nelson, Aref Shabanpoor, Robert J. Sutherland

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2004
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of British ColumbiaUniversity of Lethbridge
FundersNational Institutes of HealthNational Institute of Mental HealthFondation pour la Recherche Médicale
KeywordsPerirhinal cortexHippocampusNeuroscienceRecognition memoryVisual memoryPsychologyTemporal lobeSpatial memoryTask (project management)Cognitive psychologyWorking memoryCognition

Abstract

fetched live from OpenAlex

Visual recognition memory is subserved by a distributed set of neural circuits, which include structures of the temporal lobe. Conflicting experimental results regarding the role of the hippocampus in nonspatial forms of such memories have been attributed to species, task, and lesion discrepancies. We have overcome obstacles that have prevented a direct evaluation of the role of the hippocampus in this type of memory by developing for rats a nonspatial, picture-based, trial-unique, delayed matching-to-sample task that is a procedural analogue of standard visual recognition memory tasks used in primates. With this task, we demonstrate that rats have a visual memory profile, which is analogous to that in primates and depends on the function of perirhinal cortex. We also find that selective lesions of hippocampus impair delay-dependent visual memory with a profile different from that produced by damage to the perirhinal cortex. These data demonstrate that rats have a visual recognition memory system fundamentally similar to primates that depends on the function of the hippocampus.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.278
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.067
GPT teacher head0.359
Teacher spread0.292 · 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

Citations108
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicMemory and Neural MechanismsFrench-language works237,207