Evidence map›Paper›PMID 37708602›Full record

ArticleCognition2023

Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks.

Yang Xiang, Jenna Landy, Fiery A Cushman, Natalia Vélez, Samuel J Gershman

Abstract read
In one paragraph

Article in Cognition, 2023. 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
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Evidence of a social evaluation penalty for using AI.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. People reward others based on their willingness to exert effort.Journal of experimental social psychology · 2025
    Article
  5. Article
  6. 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

5 authors.

Yang XiangDepartment of Psychology, Harvard University, United States of America. Electronic address: yyx@g.harvard.edu.
Jenna LandyCollege of Human Ecology, Cornell University, United States of America.
Fiery A CushmanDepartment of Psychology, Harvard University, United States of America.
Natalia VélezDepartment of Psychology, Harvard University, United States of America.
Samuel J GershmanDepartment of Psychology, Harvard University, United States of America; Center for Brain Science, Harvard University, United States of America; Center for Brains, Minds, and Machines, MIT, United States of America.

Funding

Computational and neural underpinnings of decision-making in social contextsK00MH125856 · NIMH · HARVARD UNIVERSITY · PI VELEZ, NATALIA · 2020 to 2022
$247k
NIMH NIH HHS K00 MH125856
6 · The paper itself

Abstract

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the actual effort or force (individuals are more responsible if they put forth more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N=717), we found that participants' judgments are best described by a model that considers both actual and counterfactual effort. This finding generalized to an independent validation data set (N=99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

Indexed as

Cooperative BehaviorJudgmentAdultFemaleHumansMaleSocial PerceptionSocial ResponsibilityYoung AdultCausal reasoningMoral psychologyResponsibility attributionSocial cognition

Identifiers

PMID37708602
PMCPMC10592005

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Registered trials

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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.