Progressive multi-stage extrapolation of predictable motion in human visual cortex

Abstract

Neural processing of sensory information takes time. Consequently, to estimate the current state of the world, the brain must rely on predictive processes - for example, extrapolating the motion of a ball to determine its probable present position. Mounting evidence suggests that extrapolation occurs during early (retinal) processing, however it remains unclear whether extrapolation continues during later-stage (cortical) processing. Moreover, we currently lack a spatially precise characterisation of extrapolation effects in the human brain, with most studies relying on invasive neurophysiological techniques in animals. Here, we address these issues by demonstrating how precise probabilistic maps can be constructed from human EEG recordings. Participants (N = 18) viewed a stimulus moving along a circular trajectory while EEG was recorded. Using LDA classification, we extracted maps of stimulus location over time and found evidence of a widespread temporal shift occurring across distinct processing stages. This accelerated emergence of position representations indicates progressive extrapolation occurring at multiple stages of processing, with representations across the hierarchy shifted closer to real-time. We further show evidence of representational overshoot during early-stage processing following unexpected changes to an object's trajectory, and demonstrate that the observed dynamics can emerge spontaneously in a simulated neural network via spike-timing-dependent plasticity.