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Record W2055124574 · doi:10.1111/ejn.12518

Lesions of dorsal striatum eliminate lose‐switch responding but not mixed‐response strategies in rats

2014· article· en· W2055124574 on OpenAlexaff
Ivan Skelin, Rhys Hakstol, Jenn VanOyen, Dominic Mudiayi, Leonardo A. Molina, Victoria Holec, Nancy S. Hong, David R. Euston, Robert J. McDonald, Aaron J. Gruber

Bibliographic record

VenueEuropean Journal of Neuroscience · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsNeurosciencePsychologyStriatumStimulus (psychology)DorsumReinforcementBiologyCognitive psychologyAnatomySocial psychologyDopamine

Abstract

fetched live from OpenAlex

We used focal brain lesions in rats to examine how dorsomedial (DMS) and dorsolateral (DLS) regions of the striatum differently contribute to response adaptation driven by the delivery or omission of rewards. Rats performed a binary choice task under two modes: one in which responses were rewarded on half of the trials regardless of choice; and another 'competitive' one in which only unpredictable choices were rewarded. In both modes, control animals were more likely to use a predictable lose-switch strategy than animals with lesions of either DMS or DLS. Animals with lesions of DMS presumably relied more on DLS for behavioural control, and generated repetitive responses in the first mode. These animals then shifted to a random response strategy in the competitive mode, thereby performing better than controls or animals with DLS lesions. Analysis using computational models of reinforcement learning indicated that animals with striatal lesions, particularly of the DLS, had blunted reward sensitivity and less stochasticity in the choice mechanism. These results provide further evidence that the rodent DLS is involved in rapid response adaptation that is more sophisticated than that embodied by the classic notion of habit formation driven by gradual stimulus-response learning.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.306
Teacher spread0.249 · 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.

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

Citations46
Published2014
Admission routes1
Has abstractyes

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