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Consolidation and Reconsolidation of Incentive Learning in the Amygdala

2005· article· en· W2088332577 on OpenAlexaff
Szu‐Han Wang, Sean B. Ostlund, Karim Nader, Bernard W. Balleine

Bibliographic record

VenueJournal of Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsMcGill University
FundersNational Institute of Mental HealthUniversity of California, Los Angeles
KeywordsMemory consolidationAmygdalaPsychologyAnisomycinNeuroscienceConsolidation (business)IncentiveDevaluationCognitive psychologyChemistryHippocampusMicroeconomicsBusinessEconomicsProtein biosynthesisMonetary economics

Abstract

fetched live from OpenAlex

Incentive learning is the process via which animals update changes in the value of rewards. Current evidence suggests that, for food rewards in rats, this learning process involves the amygdala. However, it remains unclear whether this learning undergoes protein synthesis-dependent consolidation and "reconsolidation" processes in the lateral and basal nuclei of amygdala. Accordingly, we examined this hypothesis by local infusion of protein-synthesis inhibitor after devaluation of a food reward induced by a shift from a food-deprived to a food-sated state in an instrumental conditioning paradigm. Our results show that intra-amygdala infusions of anisomycin, whether given after the initial devaluation or after a second devaluation session, abolished the changes in the value of the food reward produced by incentive learning. This study provides direct evidence that instrumental incentive learning depends on protein synthesis within the amygdala for both consolidation and reconsolidation and extends the demonstrations of protein synthesis-dependent reconsolidation to reward-related memories.

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)0.002
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 score0.013
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.094
GPT teacher head0.331
Teacher spread0.237 · 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

Citations129
Published2005
Admission routes1
Has abstractyes

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