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How Do Expectations Shape Perception?
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DOI：10.1016/j.tics.2018.06.002.png)
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摘要
En 中文
Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.
Keyword:
PRIMARY VISUAL-CORTEX
DEEP NEURAL-NETWORKS
BAYESIAN-INFERENCE
REPETITION SUPPRESSION
POPULATION RESPONSES
PREDICTION ERRORS
REPRESENTATIONS
FEEDBACK
MEMORY
MODEL
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期刊
IF:
17.2
论文数:
3.6K
被引数:
3.5W
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