Our understanding of body ownership largely relies on the Rubber Hand Illusion (RHI) paradigm where synchronous stroking of the real and fake hands leads to an illusion of ownership of RH provided its physical, anatomical, and spatial plausibility. Self-attribution of a fake hand also occurs during visuomotor synchrony, when the visual feedback of self-initiated movements follows the trajectory of the instantiated motor command. In both cases, the experience of ownership is established through bottom-up integration and top-down prediction of multisensory (proximodistal) (...) cues within the peripersonal space. It seems, however, that depending on whether the signals are externally or self-generated, top-down expectation signals are qualitatively different. On the one hand, in RHI, the sensory correlations are modulated by empirically induced priors about the internal model of the body. On the other hand, in mRHI, body ownership is actively shaped by processes allowing for continuous comparison between the actual and predicted sensory consequences of the actions. Ample research demonstrates that the differential processing of the predicted and the actual reafferent information is addressed by the central nervous system via an internal Forward Model (FM). Indeed, results from mRHI suggest that in action-contexts mechanism underlying body ownership could be similar to the FM. Crucially FMs integrate across all sensory input including not only proximodistal (visuomotor) but also purely distal sensory signals (visuoauditory). Thus if body ownership results from a consistency of FM, it would be affected by the (in)congruency of purely distal signals provided that they inform about the action-consequences relevant to the task. Here we explicitly address this question. To test our hypothesis, we devise an embodied VR-based task where action outcomes were signaled by distinct auditory cues. By manipulating the cues with respect to their spatial, temporal and semantic congruency, we show that purely distal feedback violating predictions about action outcomes compromises both body ownership and performance. These results demonstrate that body ownership is influenced by cues which not only pertain to the body itself but also those arising outside of the body, and suggest that in goal-oriented tasks body ownership might result from the consistency of forward models. (shrink)

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established (...) robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging. (shrink)

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established (...) robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging. (shrink)

This paper addresses how impairments in prediction in young adults with autism spectrum disorder relate to their behavior during a collaboration. To assess it, we developed a task where participants play in collaboration with a synthetic agent to maximize their score. The agent’s behavior changes during the different phases of the game, requiring participants to model their sensorimotor contingencies to play collaboratively. Our results (n = 30) show differences between autistic and neurotypical individuals in their behavioral adaptation to the other (...) partner. Contrarily, there are no differences in the self-reports of that collaboration. (shrink)

Integrated information theory is an example of “ironic science” and obstructs the scientific study of consciousness. By confusing the ontological status of a method to quantify network complexity with that of a theory of consciousness, IIT has to square the circle and spirals toward its panpsychism conclusion. I analyze the consequences of this fallacy and suggest how the study of consciousness can be brought back into the realm of rational, empirical science.

The Newell Test is an important step in advancing our understanding of cognition. One critical constraint is missing from this test : A cognitive architecture must be self-contained. ACT-R and connectionism fail on this account. I present an alternative proposal, called Distributed Adaptive Control, and expose it to the Newell Test with the goal of achieving a clearer specification of the different constraints and their relationships, as proposed by Anderson & Lebiere.