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Open AccessJournal ArticleDOI

Contributions of low- and high-level properties to neural processing of visual scenes in the human brain

TLDR
It is suggested that this problem can be resolved by questioning the utility of the classical low- to high-level framework of visual perception for scene processing, and why low- and mid-level properties may be particularly diagnostic for the behavioural goals specific to scene perception as compared to object recognition.
Abstract
Visual scene analysis in humans has been characterized by the presence of regions in extrastriate cortex that are selectively responsive to scenes compared with objects or faces. While these regions have often been interpreted as representing high-level properties of scenes (e.g. category), they also exhibit substantial sensitivity to low-level (e.g. spatial frequency) and mid-level (e.g. spatial layout) properties, and it is unclear how these disparate findings can be united in a single framework. In this opinion piece, we suggest that this problem can be resolved by questioning the utility of the classical low- to high-level framework of visual perception for scene processing, and discuss why low- and mid-level properties may be particularly diagnostic for the behavioural goals specific to scene perception as compared to object recognition. In particular, we highlight the contributions of low-level vision to scene representation by reviewing (i) retinotopic biases and receptive field properties of scene-selective regions and (ii) the temporal dynamics of scene perception that demonstrate overlap of low- and mid-level feature representations with those of scene category. We discuss the relevance of these findings for scene perception and suggest a more expansive framework for visual scene analysis.This article is part of the themed issue 'Auditory and visual scene analysis'.

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Citations
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Journal ArticleDOI

Scene Perception in the Human Brain.

TL;DR: Challenges for the future include developing computational models of information processing in scene regions, investigating how these regions support scene perception under ecologically realistic conditions, and understanding how they operate in the context of larger brain networks.
Journal ArticleDOI

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

TL;DR: The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.
Journal ArticleDOI

Making Sense of Real-World Scenes.

TL;DR: It is argued that for a complete view of scene understanding, it is necessary to account for both differing observer goals and the contribution of diverse scene properties.
Journal ArticleDOI

On the partnership between neural representations of object categories and visual features in the ventral visual pathway

TL;DR: It is suggested that addressing the issue of functional specificity requires clear coding hypotheses, about object category and visual features, which make contrasting predictions about neuroimaging results in ventral pathway regions, and a strong method for testing for residual categorical effects: effects of category selectivity that cannot be accounted for by visual features of stimuli is argued.
Journal ArticleDOI

Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.

TL;DR: A set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses are developed, which map the sensory functions of the OPA onto a fully quantitative model that provides insights into its visual computations.
References
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Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
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TL;DR: In An Introduction to the Event-Related Potential Technique, Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology.
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TL;DR: The visual processing needed to perform this highly demanding task can be achieved in under 150 ms, and ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset.
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Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

TL;DR: A new experimental and data-analytical framework called representational similarity analysis (RSA) is proposed, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs.
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Using goal-driven deep learning models to understand sensory cortex

TL;DR: It is outlined how the goal-driven HCNN approach can be used to delve even more deeply into understanding the development and organization of sensory cortical processing.
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