Evidence map›Paper›PMID 27596541›Full record

ArticleBrain and cognition2016

Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach.

Vivian V Valentin, W Todd Maddox, F Gregory Ashby

Open access · greenAbstract read
In one paragraph

Article in Brain and cognition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.3field-weighted citation impact, top 38% of its field
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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Frontiers in aging neuroscience · 2022
    Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.Neural networks : the official journal of the International Neural Network Society · 2017
    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 at 2 institutions in 1 country.

Vivian V ValentinDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, United States. Electronic address: valentin@psych.ucsb.edu.
W Todd MaddoxDepartment of Psychology, University of Texas, 108 E. Dean Keeton, Stop A8000, Austin, TX 78712-1043, United States. Electronic address: wtoddmaddox@gmail.com.
F Gregory AshbyDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, United States. Electronic address: ashby@psych.ucsb.edu.
University of California, Santa Barbara · USThe University of Texas at Austin · US

Funding

Genetic Influences on Dual Processing Modes of Reward and Punishment LearningR01DA032457 · NIDA · UNIVERSITY OF TEXAS AT AUSTIN · PI BEEVERS, CHRISTOPHER G · 2012 to 2016
$2.2M
NIDA NIH HHS R01 DA032457
6 · The paper itself

Abstract

Procedural learning of skills depends on dopamine-mediated striatal plasticity. Most prior work investigated single stimulus-response procedural learning followed by feedback. However, many skills include several actions that must be performed before feedback is available. A new procedural-learning task is developed in which three independent and successive unsupervised categorization responses receive aggregate feedback indicating either that all three responses were correct, or at least one response was incorrect. Experiment 1 showed superior learning of stimuli in position 3, and that learning in the first two positions was initially compromised, and then recovered. An extensive theoretical analysis that used parameter space partitioning found that a large class of procedural-learning models, which predict propagation of dopamine release from feedback to stimuli, and/or an eligibility trace, fail to fully account for these data. The analysis also suggested that any dopamine released to the second or third stimulus impaired categorization learning in the first and second positions. A second experiment tested and confirmed a novel prediction of this large class of procedural-learning models that if the to-be-learned actions are introduced one-by-one in succession then learning is much better if training begins with the first action (and works forwards) than if it begins with the last action (and works backwards).

Indexed as

Models, TheoreticalAdultCognitive NeuroscienceConcept FormationDopamineFeedback, PsychologicalHumansLearningYoung AdultDopamineComputational cognitive neuroscienceDopamineParameter space partitioningSkill learningStriatal plasticity

Identifiers

PMID27596541
PMCPMC5077633
OpenAlexW2510002805

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.