Literature DB >> 34750229

Rapid Extraction of the Spatial Distribution of Physical Saliency and Semantic Informativeness from Natural Scenes in the Human Brain.

John E Kiat1, Taylor R Hayes2, John M Henderson2, Steven J Luck2.   

Abstract

Physically salient objects are thought to attract attention in natural scenes. However, research has shown that meaning maps, which capture the spatial distribution of semantically informative scene features, trump physical saliency in predicting the pattern of eye moments in natural scene viewing. Meaning maps even predict the fastest eye movements, suggesting that the brain extracts the spatial distribution of potentially meaningful scene regions very rapidly. To test this hypothesis, we applied representational similarity analysis to ERP data. The ERPs were obtained from human participants (N = 32, male and female) who viewed a series of 50 different natural scenes while performing a modified 1-back task. For each scene, we obtained a physical saliency map from a computational model and a meaning map from crowd-sourced ratings. We then used representational similarity analysis to assess the extent to which the representational geometry of physical saliency maps and meaning maps can predict the representational geometry of the neural response (the ERP scalp distribution) at each moment in time following scene onset. We found that a link between physical saliency and the ERPs emerged first (∼78 ms after stimulus onset), with a link to semantic informativeness emerging soon afterward (∼87 ms after stimulus onset). These findings are in line with previous evidence indicating that saliency is computed rapidly, while also indicating that information related to the spatial distribution of semantically informative scene elements is computed shortly thereafter, early enough to potentially exert an influence on eye movements.SIGNIFICANCE STATEMENT Attention may be attracted by physically salient objects, such as flashing lights, but humans must also be able to direct their attention to meaningful parts of scenes. Understanding how we direct attention to meaningful scene regions will be important for developing treatments for disorders of attention and for designing roadways, cockpits, and computer user interfaces. Information about saliency appears to be extracted rapidly by the brain, but little is known about the mechanisms that determine the locations of meaningful information. To address this gap, we showed people photographs of real-world scenes and measured brain activity. We found that information related to the locations of meaningful scene elements was extracted rapidly, shortly after the emergence of saliency-related information.
Copyright © 2022 the authors.

Entities:  

Keywords:  EEG; ERP; attention; meaning map; representational similarity analysis; saliency

Mesh:

Year:  2021        PMID: 34750229      PMCID: PMC8741164          DOI: 10.1523/JNEUROSCI.0602-21.2021

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.709


  63 in total

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Authors:  Robert D Gordon
Journal:  J Exp Psychol Hum Percept Perform       Date:  2004-08       Impact factor: 3.332

2.  ERP correlates of spatially incongruent object identification during scene viewing: contextual expectancy versus simultaneous processing.

Authors:  Sükrü Barış Demiral; George L Malcolm; John M Henderson
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3.  Modeling attention to salient proto-objects.

Authors:  Dirk Walther; Christof Koch
Journal:  Neural Netw       Date:  2006-11

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Review 5.  Gaze Control as Prediction.

Authors:  John M Henderson
Journal:  Trends Cogn Sci       Date:  2016-12-05       Impact factor: 20.229

6.  Meaning-based guidance of attention in scenes as revealed by meaning maps.

Authors:  John M Henderson; Taylor R Hayes
Journal:  Nat Hum Behav       Date:  2017-09-25

Review 7.  A M/EEG-fMRI Fusion Primer: Resolving Human Brain Responses in Space and Time.

Authors:  Radoslaw M Cichy; Aude Oliva
Journal:  Neuron       Date:  2020-07-27       Impact factor: 17.173

Review 8.  Computational modelling of visual attention.

Authors:  L Itti; C Koch
Journal:  Nat Rev Neurosci       Date:  2001-03       Impact factor: 34.870

Review 9.  How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling.

Authors:  Richard Veale; Ziad M Hafed; Masatoshi Yoshida
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2017-01-02       Impact factor: 6.237

10.  Representational similarity analysis - connecting the branches of systems neuroscience.

Authors:  Nikolaus Kriegeskorte; Marieke Mur; Peter Bandettini
Journal:  Front Syst Neurosci       Date:  2008-11-24
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  2 in total

1.  Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials.

Authors:  Taiqi He; Megan A Boudewyn; John E Kiat; Kenji Sagae; Steven J Luck
Journal:  Psychophysiology       Date:  2021-11-24       Impact factor: 4.016

2.  Meaning maps detect the removal of local semantic scene content but deep saliency models do not.

Authors:  Taylor R Hayes; John M Henderson
Journal:  Atten Percept Psychophys       Date:  2022-02-09       Impact factor: 2.157

  2 in total

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