The cognitive sciences are increasingly coming to terms with the embodied, embedded, extended, and experiential aspects of the mind. Exemplifying this shift, the enactive approach points to an essential role of goal-directed bodily activity in the generation of meaningful perceptual experience, i.e., sense-making. Here, building on recent insights into the transformative effects of practical tool- use, we make use of the enactive approach in order to provide a definition of an enactive interface in terms of augmented sense- making. We introduce such a custom-built interface, the Enactive Torch, and present a study of its experiential effects. The results demonstrate that the user experience is not adequately captured by any standardly assumed perceptual modality; rather, it is a new feeling that is mediated by the design of the device and shaped by the overall situation of the task. Taken together these findings show that there is much to be gained by synergies between engineering and the cognitive sciences in the creation of new experience- centered technology. We suggest that the guiding principle should be the design of interfaces that serve as a transparent medium for augmenting our natural skills of interaction with the world, instead of requiring conscious attention to the interface as an opaque object in the world.

Can perceptual presence be explained by counterfactually-rich predictive models linking perception and action? Considering an unusually rich range of responses to this idea has led me to (1) re-emphasize the core conceptual commitment of “predictive processing of sensorimotor contingencies” (PPSMC) to predictive model-based perception, (2) reconsider the relationship between presence and objecthood, and (3) refine the phenomenological target by differentiating between perceptual presence and the phenomenology of absence-of-presence, or “phenomenal unreality.” It turns out that this requires blue-sky thinking.

Key words: perceptual presence, objecthood, predictive processing, active inference

The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Bayesian brain and predictive coding approaches within cognitive science. To date, this perspective has been applied primarily to exteroceptive perception (e.g., vision, audition), and action. Here, I describe a predictive, inferential perspective on interoception: ‘interoceptive inference’ conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents. The model generalizes ‘appraisal’ theories that view emotions as emerging from cognitive evaluations of physiological changes, and it sheds new light on the neurocognitive mechanisms that underlie the experience of body ownership and conscious selfhood in health and in neuropsychiatric illness.

Key words: interoception, predictive coding, emotion, experience of body ownership, rubber hand illusion, active inference

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.

Key words: Predictive coding, Presence, Sensorimotor contingencies, Veridicality, Counterfactuals, Synesthesia, Active inference, Bayesian brain.