{"ID":2841834,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11480","arxiv_id":"2511.11480","title":"Inferring response times of perceptual decisions with Poisson variational autoencoders","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.","short_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 resp...","url_abs":"https://arxiv.org/abs/2511.11480","url_pdf":"https://arxiv.org/pdf/2511.11480v2","authors":"[\"Hayden R. Johnson\",\"Anastasia N. Krouglova\",\"Hadi Vafaii\",\"Jacob L. Yates\",\"Pedro J. Gonçalves\"]","published":"2025-11-14T16:58:04Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}