Evidence map›Paper›PMID 37034619›Full record

ArticlebioRxiv : the preprint server for biology2024

Reward timescale controls the rate of behavioural and dopaminergic learning.

Dennis A Burke, Annie Taylor, Huijeong Jeong, SeulAh Lee, Brenda Wu, Joseph R Floeder, Vijay Mohan K Namboodiri

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Dennis A BurkeDepartment of Neurology, University of California, San Francisco, CA, USA.ORCID 0000-0002-7515-4093
Annie TaylorNeuroscience Graduate Program, University of California, San Francisco, CA, USA.
Huijeong JeongDepartment of Neurology, University of California, San Francisco, CA, USA.
SeulAh LeeDepartment of Neurology, University of California, San Francisco, CA, USA.
Brenda WuDepartment of Neurology, University of California, San Francisco, CA, USA.
Joseph R FloederNeuroscience Graduate Program, University of California, San Francisco, CA, USA.
Vijay Mohan K NamboodiriDepartment of Neurology, University of California, San Francisco, CA, USA.ORCID 0000-0002-6674-610X

Funding

Prospective and retrospective learning in orbitofrontal cortexR01MH129582 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Vijay Mohan K Namboodiri · 2022 to 2026
$2.2M
Orbitofrontal circuit mechanisms underlying alcohol use disorderR01AA029661 · NIAAA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Vijay Mohan K Namboodiri · 2022 to 2026
$2.0M
Neural encoding of associative learning by orbitofrontal cortex circuitsR00MH118422 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI K NAMBOODIRI, VIJAY MOHAN · 2020 to 2022
$747k
Nucleus accumbens dynamics underlying cue-reward learningF32DA060044 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Dennis Alexander Burke · 2024 to 2026
$238k
NIAAA NIH HHS R01 AA029661NIDA NIH HHS F32 DA060044NIMH NIH HHS R00 MH118422NIMH NIH HHS R01 MH129582
6 · The paper itself

Abstract

Learning the causes of rewards is necessary for survival. Thus, it is critical to understand the mechanisms of such a vital biological process. Cue-reward learning is controlled by mesolimbic dopamine and improves with spacing of cue-reward pairings. However, whether a mathematical rule governs such improvements in learning rate, and if so, whether a unifying mechanism captures this rule and dopamine dynamics during learning remain unknown. Here, we investigate the behavioral, algorithmic, and dopaminergic mechanisms governing cue-reward learning rate. Across a range of conditions in mice, we show a strong, mathematically proportional relationship between both behavioral and dopaminergic learning rates and the duration between rewards. Due to this relationship, removing up to 19 out of 20 cue-reward pairings over a fixed duration has no influence on overall learning. These findings are explained by a dopamine-based model of retrospective learning, thereby providing a unified account of the biological mechanisms of learning.

Identifiers

PMID37034619
PMCPMC10081323

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.