Evidence map›Paper›PMID 34231854›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2021

Executive Function Assigns Value to Novel Goal-Congruent Outcomes.

Samuel D McDougle, Ian C Ballard, Beth Baribault, Sonia J Bishop, Anne G E Collins

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Rewards transiently and automatically enhance sustained attention.Journal of experimental psychology. General · 2025
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Advances in modeling learning and decision-making in neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022
    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

5 authors.

Samuel D McDougleDepartment of Psychology, Yale University, New Haven, CT 06520, USA.
Ian C BallardHelen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.
Beth BaribaultDepartment of Psychology, University of California, Berkeley, CA 94704, USA.
Sonia J BishopHelen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.
Anne G E CollinsHelen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.

Funding

Computationally modeling individual differences in probabilistic decision-making across positive and negative valence domainsR01MH124108 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI BISHOP, SONIA JANE · 2020 to 2024
$2.8M
The neural computations supporting hierarchical reinforcement learningR01MH119383 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI COLLINS, ANNE G.E. · 2019 to 2023
$1.9M
Causal mechanisms of habitual rule selectionF32MH119796 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI BALLARD, IAN CONNORS · 2020 to 2022
$201k
Modeling and mapping multiple computational processes in human reinforcement learningF32MH119797 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI MCDOUGLE, SAMUEL DAVID · 2019 to 2020
$82k
NIMH NIH HHS F32 MH119796NIMH NIH HHS F32 MH119797NIMH NIH HHS R01 MH119383NIMH NIH HHS R01 MH124108
6 · The paper itself

Abstract

People often learn from the outcomes of their actions, even when these outcomes do not involve material rewards or punishments. How does our brain provide this flexibility? We combined behavior, computational modeling, and functional neuroimaging to probe whether learning from abstract novel outcomes harnesses the same circuitry that supports learning from familiar secondary reinforcers. Behavior and neuroimaging revealed that novel images can act as a substitute for rewards during instrumental learning, producing reliable reward-like signals in dopaminergic circuits. Moreover, we found evidence that prefrontal correlates of executive control may play a role in shaping flexible responses in reward circuits. These results suggest that learning from novel outcomes is supported by an interplay between high-level representations in prefrontal cortex and low-level responses in subcortical reward circuits. This interaction may allow for human reinforcement learning over arbitrarily abstract reward functions.

Indexed as

Executive FunctionGoalsHumansMotivationPrefrontal CortexReinforcement, PsychologyRewardprefrontal cortexreinforcement learningrewardstriatumvalue

Identifiers

PMID34231854
PMCPMC8634563

What OpenQuestion holds

Textmetadata
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.