MétaCan
Menu
Back to cohort
Record W1973389601 · doi:10.1177/0269881112472566

Effects of post-training heroin and d-amphetamine on consolidation of win-stay learning and fear conditioning

2013· article· en· W1973389601 on OpenAlexaff
Francesco Leri, Elia Nahas, Katherine Henderson, Cheryl L. Limebeer, Linda A. Parker, Norman M. White

Bibliographic record

VenueJournal of Psychopharmacology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsMcGill UniversityUniversity of Guelph
Fundersnot available
KeywordsPsychologyConditioningAmphetamineHeroinConsolidation (business)AnesthesiaMedicineNeuroscienceDrugPsychiatryDopamine

Abstract

fetched live from OpenAlex

It has been proposed that the reinforcing properties of drugs of abuse are due, in part, to their ability to enhance memory consolidation. To test this hypothesis, heroin (0.03-3 mg/kg, SC) and d-amphetamine (0.5-2 mg/kg, SC) were administered to male Sprague-Dawley rats immediately or 4 h after training on win-stay and fear conditioning tasks. On the win-stay, immediate post-training administration of lower doses of heroin and d-amphetamine enhanced acquisition, and probe tests further revealed that these drugs enhanced different aspects of learning. Higher doses had no effect or impaired performance, particularly when administered repeatedly. On fear conditioning, the memory-enhancing effects of immediate post-training administration of lower heroin and d-amphetamine doses were revealed only when a single tone-shock pairing procedure was employed. Therefore, under appropriate experimental conditions, mildly stimulatory doses of heroin and d-amphetamine enhanced the acquisition of tasks thought to involve different types of learning. These results support the hypothesis that one of the ways in which drugs of abuse such as opiates and psychomotor stimulants reinforce behavior is by enhancing memory consolidation processes.

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 score0.005
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of PsychopharmacologySame topicMemory and Neural MechanismsFrench-language works237,207