Evidence map›Paper›PMID 41169785›Full record

ArticleInternational journal of social robotics2025

Sonic Sleight of Hand: Sound Induces Illusory Distortions in the Perception and Prediction of Robot Action.

Joel Currie, Maria Elena Giannaccini, Patric Bach

Abstract read
In one paragraph

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

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0citing papers in PubMed
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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

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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

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

3 authors.

Joel CurrieSchool of Psychology, University of Aberdeen, St Machar Drive, Aberdeen, AB24 3FX UK.ORCID 0000-0002-3367-7056
Maria Elena GiannacciniSchool of Engineering, University of Aberdeen, Elphinstone Rd, Aberdeen, AB24 3UE UK.ORCID 0000-0002-0871-4804
Patric BachSchool of Psychology, University of Aberdeen, St Machar Drive, Aberdeen, AB24 3FX UK.ORCID 0000-0003-4493-2080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For efficient human-robot interaction, human operators need to be able to efficiently represent the robot's movements in space and predict its next steps. However, according to frameworks of Bayesian multisensory integration, features outside the motion itself-like the sounds a robot makes while it moves-should affect how otherwise identical motions are perceived. Here, we translate an established psychophysical task from experimental psychology to a human-robot interaction context, which can measure these distortions to motion perception. In two series of preregistered studies, participants watched a humanoid robot make forward and backward reaching movements. When the robot hand suddenly disappeared, they reported its last seen location, either with the mouse cursor (Experiment 1a and 1b) or by matching it to probe stimuli in different locations (Experiment 2a and 2b). The results revealed that even small changes to the robot's sound robustly affect participants' visuospatial representation of its motions, so that the motion appeared to extend further in space when accompanied by slightly (100 ms) longer sounds compared to slightly shorter sounds (100 ms shorter). Moreover, these sound changes do not only affect where people currently locate the robot's motion, but where they anticipate its future steps. These findings show that sound design is an effective medium for manipulating how people represent otherwise identical robot actions and coordinate its interactions with it. The study acts as proof of concept that psychophysical tasks provide a promising tool to measure how design parameters influence the perception and prediction of robot motion. Supplementary Information: The online version contains supplementary material available at 10.1007/s12369-024-01105-5.

Indexed as

Cue-integrationHuman–robot interactionMotion perceptionMovement sonificationRepresentational momentumSocial robotics

Identifiers

PMID41169785
PMCPMC12568858

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.