Inferring response times of perceptual decisions with Poisson variational autoencoders
This work addresses the gap in modeling temporal aspects of perceptual decisions for cognitive science and neuroscience, though it is incremental as it builds on existing methods like variational autoencoders.
The authors tackled the problem of modeling perceptual decision making by incorporating temporal dynamics, resulting in a model that reproduces key empirical signatures like Hick's law and speed-accuracy trade-offs on MNIST digit classification.
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.