The Flash-Lag Effect as a Motion-Based Predictive Shift

Due to its inherent neural delays, the visual system has an outdated access to sensory information about the current position of moving objects. In contrast, living organisms are remarkably able to track and intercept moving objects under a large range of challenging environmental conditions. Physiological, behavioral and psychophysical evidences strongly suggest that position coding is extrapolated using an explicit and reliable representation of object’s motion but it is still unclear how these two representations interact. For instance, the so-called flash-lag effect supports the idea of a differential processing of position between moving and static objects. Although elucidating such mechanisms is crucial in our understanding of the dynamics of visual processing, a theory is still missing to explain the different facets of this visual illusion. Here, we reconsider several of the key aspects of the flash-lag effect in order to explore the role of motion upon neural coding of objects’ position. First, we formalize the problem using a Bayesian modeling framework which includes a graded representation of the degree of belief about visual motion. We introduce a motion-based prediction model as a candidate explanation for the perception of coherent motion. By including the knowledge of a fixed delay, we can model the dynamics of sensory information integration by extrapolating the information acquired at previous instants in time. Next, we simulate the optimal estimation of object position with and without delay compensation and compared it with human perception under a broad range of different psychophysical conditions. Our computational study suggests that the explicit, probabilistic representation of velocity information is crucial in explaining position coding, and therefore the flash-lag effect. We discuss these theoretical results in light of the putative corrective mechanisms that can be used to cancel out the detrimental effects of neural delays and illuminate the more general question of the dynamical representation at the present time of spatial information in the visual pathways.Author Summary: Visual illusions are powerful tools to explore the limits and constraints of human perception. One of them has received considerable empirical and theoretical interests: the so-called “flash-lag effect”. When a visual stimulus moves along a continuous trajectory, it may be seen ahead of its veridical position with respect to an unpredictable event such as a punctuate flash. This illusion tells us something important about the visual system: contrary to classical computers, neural activity travels at a relatively slow speed. It is largely accepted that the resulting delays cause this perceived spatial lag of the flash. Still, after three decades of debates, there is no consensus regarding the underlying mechanisms. Herein, we re-examine the original hypothesis that this effect may be caused by the extrapolation of the stimulus’ motion that is naturally generated in order to compensate for neural delays. Contrary to classical models, we propose a novel theoretical framework, called parodiction, that optimizes this process by explicitly using the precision of both sensory and predicted motion. Using numerical simulations, we show that the parodiction theory subsumes many of the previously proposed models and empirical studies. More generally, the parodiction hypothesis proposes that neural systems implement generic neural computations that can systematically compensate the existing neural delays in order to represent the predicted visual scene at the present time. It calls for new experimental approaches to directly explore the relationships between neural delays and predictive coding.