Evidence map›Paper›PMID 40178884›Full record

ArticleeLife2025

Visual routines for detecting causal interactions are tuned to motion direction.

Sven Ohl, Martin Rolfs

Abstract read
In one paragraph

Article in eLife, 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

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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

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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. Causal Perception(s).Cognitive science · 2025
    Article
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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.

Sven OhlDepartment of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee, Berlin, Germany.ORCID https://orcid.org/0000-0002-0292-2151
Martin RolfsDepartment of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee, Berlin, Germany.ORCID https://orcid.org/0000-0002-8214-8556

Funding

Deutsche Forschungsgemeinschaft OH 274/4-1Deutsche Forschungsgemeinschaft OH 274/5-1Deutsche Forschungsgemeinschaft RO3579/12-1Deutsche Forschungsgemeinschaft RO3579/8-1
6 · The paper itself

Abstract

Detecting causal relations structures our perception of events in the world. Here, we determined for visual interactions whether generalized (i.e. feature-invariant) or specialized (i.e. feature-selective) visual routines underlie the perception of causality. To this end, we applied a visual adaptation protocol to assess the adaptability of specific features in classical launching events of simple geometric shapes. We asked observers to report whether they observed a launch or a pass in ambiguous test events (i.e. the overlap between two discs varied from trial to trial). After prolonged exposure to causal launch events (the adaptor) defined by a particular set of features (i.e. a particular motion direction, motion speed, or feature conjunction), observers were less likely to see causal launches in subsequent ambiguous test events than before adaptation. Crucially, adaptation was contingent on the causal impression in launches as demonstrated by a lack of adaptation in non-causal control events. We assessed whether this negative aftereffect transfers to test events with a new set of feature values that were not presented during adaptation. Processing in specialized (as opposed to generalized) visual routines predicts that the transfer of visual adaptation depends on the feature similarity of the adaptor and the test event. We show that the negative aftereffects do not transfer to unadapted launch directions but do transfer to launch events of different speeds. Finally, we used colored discs to assign distinct feature-based identities to the launching and the launched stimulus. We found that the adaptation transferred across colors if the test event had the same motion direction as the adaptor. In summary, visual adaptation allowed us to carve out a visual feature space underlying the perception of causality and revealed specialized visual routines that are tuned to a launch's motion direction.

Indexed as

Motion PerceptionVisual PerceptionAdaptation, PhysiologicalAdultFemaleHumansMalePhotic StimulationYoung Adultadaptationcausalitydirection selectivityhumanneuroscienceperception

Identifiers

PMID40178884
PMCPMC11968102

What OpenQuestion holds

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LicenceCC BY
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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.