Inferring response times of perceptual decisions with Poisson variational autoencoders

Hayden R Johnson, Anastasia N. Krouglova, Hadi Vafaii, Jacob L. Yates, Pedro J. Goncalves

TL;DR: We introduce P-VAE-RT, a biologically inspired, image-computable model of perceptual decision making where characteristic response time phenomena emerge from efficient coding and Bayesian decoding of neural spiking activity.

Abstract: Many properties of perceptual decision making are well-modeled by deep neural networks. However, such architectures typically treat decisions as instantaneous readouts, overlooking the temporal dynamics of the decision process. We present an image-computable model of perceptual decision making in which choices and response times arise from efficient sensory encoding and Bayesian decoding of neural spiking activity. We use a Poisson variational autoencoder to learn unsupervised representations of visual stimuli in a population of rate-coded neurons--modeled as independent homogeneous Poisson processes. A task-optimized decoder then continually infers an approximate posterior over actions conditioned on incoming spiking activity. Combining these components with an entropy-based stopping rule yields a principled and image-computable model of perceptual decisions capable of generating trial-by-trial patterns of choices and response times. Applied to MNIST digit classification, the model reproduces key empirical signatures of perceptual decision making, including stochastic variability, right-skewed response time distributions, logarithmic scaling of response times with the number of alternatives (Hick's law), and speed–accuracy trade-offs.

Length: long paper (up to 8 pages)

Domain: methods

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Submission Number: 61