Evidence map›Paper›PMID 40133401›Full record

ArticleCommunications psychology2025

Social avoidance can be quantified as navigation in abstract social space.

Matthew Schafer, Daniela Schiller

Abstract read
In one paragraph

Article in Communications psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Matthew SchaferDepartment of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ms6883@columbia.edu.ORCID http://orcid.org/0000-0002-8881-1603
Daniela SchillerDepartment of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. daniela.schiller@mssm.edu.ORCID http://orcid.org/0000-0002-0357-7724

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
Research Training in Mood and Anxiety Disorders: From Animal Models to PatientsT32MH015144 · NIMH · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI Eduardo David Leonardo · 1985 to 2026
$11.1M
Neurocomputational mechanisms of proactive social behavior deficits in autism spectrum disorderR01MH122611 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FOSS-FEIG, JENNIFER, GU, XIAOSI · 2020 to 2024
$3.9M
Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorderR01MH123069 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI, KOENIGSBERG, HAROLD W · 2021 to 2025
$3.1M
COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
Delineating proactive social behaviors in dynamic and multidimensional social spaceR21MH120789 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI, SCHILLER, DANIELA · 2019 to 2020
$466k
Neural Mapping of the Social LandscapeF31MH123123 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SCHAFER, MATTHEW · 2021 to 2023
$135k
NCATS NIH HHS UL1 TR004419NIH HHS S10 OD026880NIH HHS S10 OD030463NIMH NIH HHS F31 MH123123NIMH NIH HHS R01 MH122611NIMH NIH HHS R01 MH123069NIMH NIH HHS R21 MH120789NIMH NIH HHS T32 MH015144U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) F31MH123123U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH122611U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH123069U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R21MH120789U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) T32MH015144
6 · The paper itself

Abstract

We navigate social relationships daily, making decisions that can change our affiliation and power relations with others. People high in social avoidance report perceiving little affiliation and power in their social lives. Do they also make low affiliation and low power interaction choices in actual social interactions? We hypothesized that social avoidance can be quantified as navigation in an abstract social space framed by power and affiliation. To test this, we recruited two large samples of online participants (Initial sample n = 579, Validation sample n = 255) to complete a naturalistic social interaction game where they form relationships with fictional characters, and a battery of questionnaires. Factor analysis of the questionnaires revealed a social avoidance factor that related to a low affiliation and low power interaction style, which was reflected in large social distance between the participants and characters. This distance, in turn, was related to smaller and less complex real-world social networks-suggesting that this abstract behavioral geometry reflects real-life behavioral tendencies. Language analysis of post-task character descriptions found semantic representations that mirrored the relationships formed in the task, with social avoidance relating to more negative impressions. This approach suggests that social avoidance can be thought of as an abstract, two-dimensional navigational strategy, potentially leading to effective strategies for social skills training and therapy.

Identifiers

PMID40133401
PMCPMC11936828

What OpenQuestion holds

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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.