Johan Kwisthout (Radboud)
Zoom link: https://unimelb.zoom.us/j/89480041197?pwd=VW55U0xrVncxTXRXZEtkTzFGclBDdz09
There is a growing body of evidence that the human brain may be organized according to principles of predictive processing. An important conjecture in neuroscience is that a brain organized in this way can effectively and efficiently approximate Bayesian inferences. Given that many forms of cognition seem to be well characterized as a form of Bayesian inference, this conjecture has great import for cognitive science. It suggests that predictive processing may provide a neurally plausible account of how forms of cognition that are modeled as Bayesian inference may be physically implemented in the brain. Yet, as I will show in this talk, the jury is still out on whether or not the conjecture is really true. Specifically, I will demonstrate that each key subcomputation invoked in predictive processing potentially hides a computationally intractable problem. I will discuss the implications of these sobering results for the predictive processing account and propose a way to move forward.