PriorCircuit:Circuit mechanisms for computing and exploiting statistical structures in sensory decision making

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A defining feature of animal intelligence is the ability to find, represent and update knowledge of statistical regularities in the sensory environment, in service of adaptive behavior. Extracting and exploiting statistical regularities often occurs implicitly-without instruction, and automatically-and is the bedrock for complex behaviors such as language acquisition in human babies. How does the brain achieve this? Our goal is to understand the neural computations behind implicit statistical learning by investigating decision making in auditory discrimination tasks where the statistics are finely parameterized and change over time. Our recent work has shown that humans, rats and mice can track sensory statistics to bias their behaviour accordingly. Our electrophysiological interrogation has further implicated the posterior parietal and anterior cingulate cortices in representing these biases that arise from the learnt statistics. These circuit nodes provide a clear entry point for investigating the implicit learning of statistical structures.Building on these findings, we aim to achieve a mechanistic understanding of how different brain areas integrate sensory events across time in order to construct 'prior' beliefs about the environment that can in turn influence sensory perception and/or action planning.We will achieve this by combining novel and highly quantifiable behavioural paradigms in humans, rats and mice with large-scale electrophysiological recordings and loss-of-function approaches in rodents. These experiments will inform a suite of theoretical models that will serve to explain the observed phenomena, and propose further manipulations that would allow us to rule out different hypotheses for the computations that support implicit statistical learning. The results from this project will advance understanding of how the brain generates and encodes beliefs about statistical regularities and computes appropriate decisions to optimize reward.