Predictive perceptions, predictive actions, and beyond

Visual attention around a hand location localized by proprioceptive information

Facilitation of visual processing has been reported in the space near the hand. To understand the underlying mechanism of hand proximity attention, we conducted experiments that isolated hand-related effects from top–down attention, proprioceptive information from visual information, the position effect from the influence of action, and the distance effect from the peripersonal effect. The flash-lag effect was used as an index of attentional modulation. Because the results showed that the flash-lag effect was smaller at locations near the hand, we concluded that there was a facilitation effect of the visual stimuli around the hand location identified through proprioceptive information. This was confirmed by conventional reaction time measures. We also measured steady-state visual evoked potential (SSVEP) in order to investigate the spatial properties of hand proximity attention and top–down attention. The results showed that SSVEP reflects the effect of top–down attention but not that of hand proximity attention. This suggests that the site of hand proximity attention is at a later stage of visual processing, assuming that SSVEP responds to neural activities at the early stages. The results of left-handers differed from those of right-handers, and this is discussed in relation to handedness variation.

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Due to its inherent neural delays, the visual system has an outdated access to sensory information about the current position of moving objects.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.

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.

On Strong Anticipation

We examine Dubois's (2003) distinction between weak anticipation and strong anticipation. Anticipation is weak if it arises from a model of the system via internal simulations. Anticipation is strong if it arises from the system itself via lawful regularities embedded in the system's ordinary mode of functioning. The assumption of weak anticipation dominates cognitive science and neuroscience and in particular the study of perception and action. The assumption of strong anticipation, however, seems to be required by anticipation's ubiquity. It is, for example, characteristic of homeostatic processes at the level of the organism, organs, and cells. We develop the formal distinction between strong and weak anticipation by elaboration of anticipating synchronization, a phenomenon arising from time delays in appropriately coupled dynamical systems. The elaboration is conducted in respect to (a) strictly physical systems, (b) the defining features of circadian rhythms, often viewed as paradigmatic of biological behavior based in internal models, (c) Pavlovian learning, and (d) forward models in motor control. We identify the common thread of strongly anticipatory systems and argue for its significance in furthering understanding of notions such as "internal", "model" and "prediction".