Literature DB >> 32573384

Neural Correlates of Fixated Low- and High-level Scene Properties during Active Scene Viewing.

John M Henderson1, Jessica E Goold1, Wonil Choi2, Taylor R Hayes1.   

Abstract

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.

Entities:  

Year:  2020        PMID: 32573384     DOI: 10.1162/jocn_a_01599

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  3 in total

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

Authors:  John E Kiat; Taylor R Hayes; John M Henderson; Steven J Luck
Journal:  J Neurosci       Date:  2021-11-08       Impact factor: 6.709

3.  Deep saliency models learn low-, mid-, and high-level features to predict scene attention.

Authors:  Taylor R Hayes; John M Henderson
Journal:  Sci Rep       Date:  2021-09-16       Impact factor: 4.379

  3 in total

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