Motion direction is represented as a bimodal probability distribution in the human visual cortex

Abstract

How do humans infer motion direction from noisy sensory signals? We hypothesized that motion direction is computed not only from velocity but also spatial orientation signals - a streak created by moving objects. We implemented this hypothesis in a Bayesian model, which quantifies knowledge with probability distributions, and tested its predictions using psychophysics and fMRI. Using a probabilistic pattern-based analysis, we decoded probability distributions of motion direction from trial-by-trial activity in the human visual cortex. Corroborating the predictions, the decoded distributions had a bimodal shape, with peaks that predicted the direction and magnitude of behavioral errors. Interestingly, we observed similar bimodality in the distribution of the observers behavioral responses across trials. Together, these results suggest that observers use spatial orientation signals when estimating motion direction. More broadly, our findings indicate that the cortical representation of low-level visual features, such as motion direction, can reflect a combination of several qualitatively distinct signals.