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Predictive processing of scenes and objects
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AI
DOI：10.1038/s44159-023-00254-0.png)
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Abstract
En 中文
Real-world visual input consists of rich scenes that are meaningfully composed of multiple objects that interact in complex but predictable ways. Despite this complexity, humans can recognize scenes and objects within scenes from a brief glance at an image. In this Review, we synthesize behavioural and neural findings that elucidate the mechanisms underlying this impressive ability. First, we review evidence that visual object and scene processing is partly implemented in parallel, enabling rapid computation of an initial gist of objects and scenes concurrently. Next, we discuss bidirectional interactions between object and scene processing, with scene information modulating the visual processing of objects and object information modulating the visual processing of scenes. Finally, we review evidence that objects also combine with each other to form object constellations, modulating the processing of individual objects within the object pathway. Altogether, these findings can be understood by conceptualizing object and scene perception as the outcome of a joint probabilistic inference in which best guesses about objects act as priors for scene perception and vice versa. Humans can rapidly and accurately recognize visual scenes and objects within them. In this Review, Peelen and colleagues discuss bidirectional interactions between object and scene processing and the role of predictive processing in visual inference.
Keywords:
DEEP NEURAL-NETWORKS
REAL-WORLD
VISUAL-CORTEX
EYE-MOVEMENTS
PLACE AREA
ATTENTION
CONTEXT
REPRESENTATIONS
ORGANIZATION
INFORMATION
Journal
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21.8
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619
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2.4K