Nevin Manimala Statistics

A general framework for inferring Bayesian ideal observer models from psychophysical data

eNeuro. 2022 Oct 31:ENEURO.0144-22.2022. doi: 10.1523/ENEURO.0144-22.2022. Online ahead of print.


A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a key link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well-approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.Significance statementModels in neuroscience provide an essential tool for developing and testing hypotheses about how the brain works. Here, we review the canonical application of Bayesian ideal observer models for understanding sensory processing. We present a new mathematical generalization that will allow these models to be used for deeper investigations into how prior knowledge influences perception. We also provide a software toolkit for implementing the described models.

PMID:36316119 | DOI:10.1523/ENEURO.0144-22.2022

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