Evidence map›Paper›PMID 30541915›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2019

Auditory Figure-Ground Segregation Is Impaired by High Visual Load.

Katharine Molloy, Nilli Lavie, Maria Chait

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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  11. Establishing gaze markers of perceptual load during multi-target visual search.Cognitive research: principles and implications · 2023
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  19. Attention and Capacity Limits in Perception: A Cellular Metabolism Account.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020
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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

3 authors.

Katharine MolloyInstitute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom, and.
Nilli LavieInstitute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom, and.ORCID 0000-0001-5274-0535
Maria ChaitEar Institute, University College London, London WC1X 8EE, United Kingdom m.chait@ucl.ac.uk.ORCID 0000-0002-7808-3593

Funding

Wellcome Trust
6 · The paper itself

Abstract

Figure-ground segregation is fundamental to listening in complex acoustic environments. An ongoing debate pertains to whether segregation requires attention or is "automatic" and preattentive. In this magnetoencephalography study, we tested a prediction derived from load theory of attention (e.g., Lavie, 1995) that segregation requires attention but can benefit from the automatic allocation of any "leftover" capacity under low load. Complex auditory scenes were modeled with stochastic figure-ground stimuli (Teki et al., 2013), which occasionally contained repeated frequency component "figures." Naive human participants (both sexes) passively listened to these signals while performing a visual attention task of either low or high load. While clear figure-related neural responses were observed under conditions of low load, high visual load substantially reduced the neural response to the figure in auditory cortex (planum temporale, Heschl's gyrus). We conclude that fundamental figure-ground segregation in hearing is not automatic but draws on resources that are shared across vision and audition.

Indexed as

Auditory PerceptionVisual PerceptionAdultAttentionAuditory CortexFemaleHumansMagnetoencephalographyMaleattentionauditory scene analysisload theorymagnetoencephalographyMEGmultisensory

Identifiers

PMID30541915
PMCPMC6391559

What OpenQuestion holds

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Registered trials

None linked

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.