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Neural tuning instantiates prior expectations in the human visual system

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Harrison, William J  ORCID logo
Bays, Paul M 


jats:titleAbstract</jats:title>jats:pPerception is often modelled as a process of active inference, whereby prior expectations are combined with noisy sensory measurements to estimate the structure of the world. This mathematical framework has proven critical to understanding perception, cognition, motor control, and social interaction. While theoretical work has shown how priors can be computed from environmental statistics, their neural instantiation could be realised through multiple competing encoding schemes. Using a data-driven approach, here we extract the brain’s representation of visual orientation and compare this with simulations from different sensory coding schemes. We found that the tuning of the human visual system is highly conditional on stimulus-specific variations in a way that is not predicted by previous proposals. We further show that the adopted encoding scheme effectively embeds an environmental prior for natural image statistics within the sensory measurement, providing the functional architecture necessary for optimal inference in the earliest stages of cortical processing.</jats:p>


Acknowledgements: We thank Valentin Wyart and Jean-Remi King for sharing their data. This work was supported by an Australian Research Council Discovery Early Career Researcher Award to RR (DE210100790) and Wellcome Trust Senior Fellowship to PMB (106926).


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Nature Communications

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Springer Science and Business Media LLC
Department of Education and Training | Australian Research Council (ARC) (DE210100790)