December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Sampling-based decision making
Author Affiliations
  • Cristina Savin
    Center for Neural Science, Center for Data Science, New York University, New York, NY
Journal of Vision December 2022, Vol.22, 3183. doi:
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      Cristina Savin; Sampling-based decision making. Journal of Vision 2022;22(14):3183.

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      © ARVO (1962-2015); The Authors (2016-present)

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There is substantial debate about the neural correlates of probabilistic computation (as evidenced in a Computational Cognitive Neuroscience - GAC 2020 workshop). Among competing theories, neural sampling provides a compact account of how variability in neuron responses can be used to flexibly represent probability distributions, which accounts for a range of V1 response properties. As samples encode uncertainty implicitly, distributed across time and neurons, it remains unclear how such representations can be used for decision making. Here we present a simple model for how a spiking neural network can integrate posterior samples to support Bayes-optimal decision making. We use this model to study behavioral and neural consequences of sampling based decision making. As the integration of posterior samples in the decision circuit is continuous in time, it leads to systematic biases after abrupt changes in the stimulus. This is reflected in behavioral biases towards recent history, similar to documented sequential effects in human decision making, and stimulus-specific neural transients. Overall, our work provides a first mechanistic model for decision making using sampling-based codes. It is also a stepping stone towards unifying sampling and parametric perspectives of Bayesian inference.


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