Evidence map›Paper›PMID 34151186›Full record

ArticleComputational brain & behavior2021

Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model.

Jeffrey B Inglis, Vivian V Valentin, F Gregory Ashby

Abstract read
In one paragraph

Article in Computational brain & behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Sense of agency and addiction.Frontiers in psychology · 2026
    Article
  4. Sense of agency and decision making.Frontiers in psychology · 2026
    Article
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.

Jeffrey B InglisInterdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara.
Vivian V ValentinDepartment of Psychological & Brain Sciences, University of California, Santa Barbara.
F Gregory AshbyDepartment of Psychological & Brain Sciences, University of California, Santa Barbara.

Funding

The Cognitive Neuroscience of Human Category LearningR01MH063760 · NIMH · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI ASHBY, F. GREGORY, HELIE, SEBASTIEN · 2002 to 2018
$3.6M
NIMH NIH HHS R01 MH063760
6 · The paper itself

Abstract

There have been many proposals that learning rates in the brain are adaptive, in the sense that they increase or decrease depending on environmental conditions. The majority of these models are abstract and make no attempt to describe the neural circuitry that implements the proposed computations. This article describes a biologically detailed computational model that overcomes this shortcoming. Specifically, we propose a neural circuit that implements adaptive learning rates by modulating the gain on the dopamine response to reward prediction errors, and we model activity within this circuit at the level of spiking neurons. The model generates a dopamine signal that depends on the size of the tonically active dopamine neuron population and the phasic spike rate. The model was tested successfully against results from two single-neuron recording studies and a fast-scan cyclic voltammetry study. We conclude by discussing the general applicability of the model to dopamine mediated tasks that transcend the experimental phenomena it was initially designed to address.

Indexed as

adaptive learning ratecomputational cognitive neurosciencedopamineventral subiculum

Identifiers

PMID34151186
PMCPMC8210637

What OpenQuestion holds

Textmetadata
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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.