Inferring response times of perceptual decisions with Poisson variational autoencoders

TL;DR

This paper introduces a novel Poisson variational autoencoder model that captures the temporal dynamics of perceptual decision making, accurately reproducing empirical response time patterns in visual classification tasks.

Contribution

It presents an image-computable, probabilistic model that integrates neural spiking activity with decision timing, advancing understanding of perceptual decision processes.

Findings

Reproduces stochastic variability in decisions

Models right-skewed response time distributions

Captures Hick's law and speed-accuracy trade-offs

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.