Evidence map›Paper›PMID 41055415›Full record

ArticleJournal of vision2025

Detecting unnaturalness in biological motion with altered playback speeds.

Kan Misumi, Hiroshi Ueda, Yuichiro Nishiura, Katsumi Watanabe

Abstract read
In one paragraph

Article in Journal of vision, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Kan MisumiFaculty of Science and Engineering, Waseda University, Tokyo, Japan.
Hiroshi UedaNTT Communication Science Laboratories, Kanagawa, Japan.
Yuichiro NishiuraFaculty of Science and Engineering, Waseda University, Tokyo, Japan.
Katsumi WatanabeFaculty of Science and Engineering, Waseda University, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prior research showed that observers can tolerate large speed alterations in real-world videos. The present study examined how sensitive the human visual system is to the change of kinematic information of human actions with altered playback speeds. We recorded four persons walking at various speeds and produced point-light walker stimuli (standard stimuli), from which we also created test stimuli either by speeding up or slowing down the playback speed. In the experiments, two point-light walkers were presented sequentially: one standard and the other test stimuli. Importantly, in each trial, the expected speed of translation was kept constant (e.g., a pair of one walking at 5.40 km/h and the other walking at 2.70 km/h but played with double speed), differing only in gait kinematic information. Participants reported which stimulus was played at a normal speed. We also included the manipulations of orientation (upright vs. inverted) and spatial scrambling of the point-light dots. The results showed that the unnaturalness detection was performed at above chance levels, confirming that kinematic inconsistencies provided a discernible cue. However, detection was only reliable when the speed alteration in a test stimulus was fairly large. Interestingly, we found little differences in performance among upright-intact, inverted, and scrambled conditions. The lack of the large detriments from inversion or scrambling suggests that the participants did not rely strongly on global form or orientation cues to perform the unnaturalness detection and points to greater contributions of local motion signals.

Indexed as

Motion PerceptionWalkingAdultBiomechanical PhenomenaCuesFemaleGaitHumansMalePhotic StimulationYoung Adult

Identifiers

PMID41055415
PMCPMC12524866

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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.