The neural dynamics of hierarchical Bayesian causal inference in multisensory perception

How do we make inferences about the source of sensory signals? Here, the authors use Bayesian causal modeling and measures of neural activity to show how the brain dynamically codes for and combines sensory signals to draw causal inferences.

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Detalles Bibliográficos
Autores principales: Tim Rohe, Ann-Christine Ehlis, Uta Noppeney
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/a434c215c566478aab102f7709740109
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