Evidence map›Paper›PMID 35705734›Full record

ArticlePsychopharmacology2022

Adolescent reinforcement-learning trajectories predict cocaine-taking behaviors in adult male and female rats.

Peroushini Villiamma, Jordan Casby, Stephanie M Groman

Abstract read
In one paragraph

Article in Psychopharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Peroushini VilliammaDepartment of Neuroscience, University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA.
Jordan CasbyMolecular Pharmacology and Therapeutics Graduate Program, University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA.
Stephanie M GromanDepartment of Neuroscience, University of Minnesota, Twin Cities, Minneapolis, MN, 55455, USA. sgroman@umn.edu.

Funding

Genomic mechanisms of decision-making and opioid use trajectories in the ratU01DA051977 · NIDA · YALE UNIVERSITY · PI DILEONE, RALPH J, GROMAN, STEPHANIE MARY · 2020 to 2024
$3.2M
Molecular Mechanisms of Susceptibility to Drug Use.K01DA051598 · NIDA · YALE UNIVERSITY · PI GROMAN, STEPHANIE MARY · 2020 to 2024
$800k
NIDA NIH HHS DA051598NIDA NIH HHS DA051977NIDA NIH HHS K01 DA051598NIDA NIH HHS U01 DA051977
6 · The paper itself

Abstract

The anatomical, structural, and functional adaptations that occur in the brain during adolescence are thought to facilitate improvements in decision-making functions that are known to occur during this stage of development. The mechanisms that underlie these neural adaptations are not known, but deviations in developmental trajectories have been proposed to contribute to the emergence of mental illness, including addiction. Direct evidence supporting this hypothesis, however, has been limited. Here, we used a recently developed reversal-learning protocol to investigate the predictive relationship between adolescent decision-making trajectories and cocaine-taking behaviors in adulthood. Decision-making functions in the reversal-learning task were assessed throughout adolescence and into adulthood in male and female Long-Evans rats. Trial-by-trial choice data was fitted with a reinforcement-learning model to quantify the degree to which choice behavior of individual rats was influenced by rewarded (e.g., ∆

Indexed as

Behavior, AddictiveCocaineAnimalsDecision MakingFemaleMaleRatsRats, Long-EvansReinforcement, PsychologyReversal LearningRewardCocaineAddiction susceptibilityComputational psychiatryDecision makingNeurodevelopmentReward learning

Identifiers

PMID35705734
PMCPMC10332493

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.