Evidence map›Paper›PMID 36385908›Full record

ArticleAffective science2022

Modeling Variation in Empathic Sensitivity Using Go/No-Go Social Reinforcement Learning.

Katherine O'Connell, Marissa Walsh, Brandon Padgett, Sarah Connell, Abigail A Marsh

Open access · greenAbstract read
In one paragraph

Article in Affective science, 2022. 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
0.7field-weighted citation impact, top 34% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  4. 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 at 1 institution in 1 country.

Katherine O'ConnellInterdisciplinary Program in Neuroscience, Georgetown University, Washington, DC USA.ORCID 0000-0002-9649-9007
Marissa WalshDepartment of Psychology, Georgetown University, Washington, DC USA.
Brandon PadgettDepartment of Psychology, Georgetown University, Washington, DC USA.
Sarah ConnellDepartment of Psychology, Georgetown University, Washington, DC USA.
Abigail A MarshInterdisciplinary Program in Neuroscience, Georgetown University, Washington, DC USA.ORCID https://orcid.org/0000-0001-5635-181X
Georgetown University · US

Funding

Predoctoral Training in Intergrative NeuroscienceT32NS041231 · NINDS · GEORGETOWN UNIVERSITY · PI Ludise Malkova, Ashley S VanMeter · 2001 to 2026
$6.8M
Translational Biomedical Science Training GrantTL1TR001431 · NCATS · GEORGETOWN UNIVERSITY · PI LEE, DEXTER L, SANDBERG, KATHRYN L · 2015 to 2024
$4.8M
NCATS NIH HHS TL1 TR001431NINDS NIH HHS T32 NS041231
6 · The paper itself

Abstract

Recent advances in computational behavioral modeling can help rigorously quantify differences in how individuals learn behaviors that affect both themselves and others. But social learning remains understudied in the context of understanding individual variation in social phenomena like aggression, which is defined by persistent engagement in behaviors that harm others. We adapted a go/no-go reinforcement learning task across social and non-social contexts such that monetary gains and losses explicitly impacted the subject, a study partner, or no one. We then quantified participants' ( Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00119-4.

Indexed as

AntisocialityBehavioral modelingEmpathyReinforcement learningSocial learning

Identifiers

PMID36385908
PMCPMC9537390
OpenAlexW4281637719

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.