Högskolan i Skövde

Robotic cognitive modeling in the real world requires a level of integration and grounding rarely seen in more abstract modeling. However, like Newell we believe this is exactly the kind of integration needed to promote scientific cumulation in the cognitive sciences. We present a neural model of learning compatible with Noë’s account of enactive perception. We highlight that accounts of enactive perception tend to oversimplify the problem of identifying contingent relationships and introduce a novel way to address the problem of marginal regularities. Finally, we describe a general (non-task specific) model and present a number of real-world robotic experiments demonstrating a wide range of integrated psychological phenomena.