Sensorimotor Laws, Mechanisms, and Representations

According to the sensorimotor account, vision does not imply theconstruction of internally generated representations of the environment, butit isthe skillful exercise of the sensorimotor contingencies obeying sense-specific laws. In this short study, I focus on the notion of “sensorimotor law” and characterize the kind of explanation providedby the sensorimotor theory as a form of covering law model. I then question the nature of such sensorimotor laws and describe them as mechanisms. I show that a mechanistic interpretation provides a better account of the sensorimotor invariances, which fosters us to rebalance the explanatory burden of sensorimotor action and information. Finally, I show that the question of the roleof representations within the sensorimotor theory should be reconsidered

A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synesthesia

Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of “perceptual presence” has motivated “sensorimotor theories” which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative “predictive processing” theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These “counterfactually-rich” generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception

Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects

Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally invariant structures in the sensorimotor experience of an agent. We take inspiration from the Sensorimotor Contingencies Theory to define a computational model of this mechanism through a sensorimotor, unsupervised and predictive approach. Our model is based on processing the unsupervised interaction of an artificial agent with its environment. We show how spatio-temporally invariant structures in the environment induce regularities in the sensorimotor experience of an agent, and how this agent, while building a predictive model of its sensorimotor experience, can capture them as densely connected subgraphs in a graph of sensory states connected by motor commands. Our approach is focused on elementary mechanisms, and is illustrated with a set of simple experiments in which an agent interacts with an environment. We show how the agent can build an internal model of moving but spatio-temporally invariant structures by performing a Spectral Clustering of the graph modeling its overall sensorimotor experiences. We systematically examine properties of the model, shedding light more globally on the specificities of the paradigm with respect to methods based on the supervised processing of collections of static images.Comment: 24 pages, 10 figures, published in Frontiers Robotics and A

Superior Facial Expression, But Not Identity Recognition, in Mirror-Touch Synesthesia

Simulation models of expression recognition contend that to understand another's facial expressions, individuals map the perceived expression onto the same sensorimotor representations that are active during the experience of the perceived emotion. To investigate this view, the present study examines facial expression and identity recognition abilities in a rare group of participants who show facilitated sensorimotor simulation (mirror-touch synesthetes). Mirror-touch synesthetes experience touch on their own body when observing touch to another person. These experiences have been linked to heightened sensorimotor simulation in the shared-touch network (brain regions active during the passive observation and experience of touch). Mirror-touch synesthetes outperformed nonsynesthetic participants on measures of facial expression recognition, but not on control measures of face memory or facial identity perception. These findings imply a role for sensorimotor simulation processes in the recognition of facial affect, but not facial identity

High frequency repetitive transcranial magnetic stimulation to the left dorsolateral prefrontal cortex modulates sensorimotor cortex function in the transition to sustained muscle pain

Based on reciprocal connections between the dorsolateral prefrontal cortex (DLPFC) and basal-ganglia regions associated with sensorimotor cortical excitability, it was hypothesized that repetitive transcranial magnetic stimulation (rTMS) of the left DLPFC would modulate sensorimotor cortical excitability induced by muscle pain. Muscle pain was provoked by injections of nerve growth factor (end of Day-0 and Day-2) into the right extensor carpi radialis brevis (ECRB) muscle in two groups of 15 healthy participants receiving 5 daily sessions (Day-0 to Day-4) of active or sham rTMS. Muscle pain scores and pressure pain thresholds (PPTs) were collected (Day-0, Day-3, Day-5). Assessment of motor cortical excitability using TMS (mapping cortical ECRB muscle representation) and somatosensory evoked potentials (SEPs) from electrical stimulation of the right radial nerve were recorded at Day-0 and Day-5. At Day-0 versus Day-5, the sham compared to active group showed: Higher muscle pain scores and reduced PPTs (P < 0.04); decreased frontal N30 SEP (P < 0.01); increased TMS map volume (P < 0.03). These results indicate that muscle pain exerts modulatory effects on the sensorimotor cortical excitability and left DLPFC rTMS has analgesic effects and modulates pain-induced sensorimotor cortical adaptations. These findings suggest an important role of prefrontal to basal-ganglia function in sensorimotor cortical excitability and pain processing