Predictive coding of visual motion in both monocular and binocular human visual processing

Neural processing of sensory input in the brain takes time, and for that reason our awareness of visual events lags behind their actual occurrence. One way the brain might compensate to minimize the impact of the resulting delays is through extrapolation. Extrapolation mechanisms have been argued to underlie perceptual illusions in which moving and static stimuli are mislocalised relative to one another (such as the flash-lag and related effects). However, where in the visual hierarchy such extrapolation processes take place remains unknown. Here, we address this question by identifying monocular and binocular contributions to the flash-grab illusion. In this illusion, a brief target is flashed on a moving background that reverses direction. As a result, the perceived position of the target is shifted in the direction of the reversal. We show that the illusion is attenuated, but not eliminated, when the motion reversal and the target are presented dichoptically to separate eyes. This reveals extrapolation mechanisms at both monocular and binocular processing stages contribute to the illusion. We interpret the results in a hierarchical predictive coding framework, and argue that prediction errors in this framework manifest directly as perceptual illusions

A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework