Evidence map›Paper›PMID 35199640›Full record

ArticleeLife2022

Humans perseverate on punishment avoidance goals in multigoal reinforcement learning.

Paul B Sharp, Evan M Russek, Quentin J M Huys, Raymond J Dolan, Eran Eldar

Erratum issuedAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Computational models of subjective feelings in psychiatry.Neuroscience and biobehavioral reviews · 2023
    Review
  10. Computational perspectives on human fear and anxiety.Neuroscience and biobehavioral reviews · 2023
    Review
  11. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Paul B Sharp *The Hebrew University of Jerusalem, Jerusalem, Israel.ORCID https://orcid.org/0000-0003-4949-1501
Evan M Russek *Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
Quentin J M HuysMax Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
Raymond J DolanMax Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-9356-761X
Eran EldarThe Hebrew University of Jerusalem, Jerusalem, Israel.

Funding

Neurocomputational studies of mood-related momentum dynamics linking reward learning, valuation and responsivityR01MH124092 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DOMBROVSKI, ALEXANDRE Y., ELDAR, ERAN PLAYFAIR · 2020 to 2024
$5.3M
CRCNS US-Israel Research Proposal: Computational Phenotyping of Decision Making in Adolescent PsychopathologyR01MH125564 · NIMH · PRINCETON UNIVERSITY · PI NIV, YAEL · 2020 to 2024
$1.3M
NIMH NIH HHS R01 MH124092NIMH NIH HHS R01 MH125564Wellcome TrustWellcome Trust 098362/Z/12/Z
6 · The paper itself

Abstract

Managing multiple goals is essential to adaptation, yet we are only beginning to understand computations by which we navigate the resource demands entailed in so doing. Here, we sought to elucidate how humans balance reward seeking and punishment avoidance goals, and relate this to variation in its expression within anxious individuals. To do so, we developed a novel multigoal pursuit task that includes trial-specific instructed goals to either pursue reward (without risk of punishment) or avoid punishment (without the opportunity for reward). We constructed a computational model of multigoal pursuit to quantify the degree to which participants could disengage from the pursuit goals when instructed to, as well as devote less model-based resources toward goals that were less abundant. In general, participants (

Indexed as

GoalsPunishmentAvoidance LearningHumansReinforcement, PsychologyRewardanxietyhumanmultitaskneurosciencereinforcement learning

Identifiers

PMID35199640
PMCPMC8912924

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.