To err and err, but less and less: Predictive coding and affective value in perception, art, and autism

The importance of prediction or expectation in the functioning of the mind is appreciated at least since the birth of psychology as a separate discipline. From minute moment-to-moment predictions of the movements ofa melody or a ball, to the long-term plans of our friends and foes, we continuously predict the world around us, because we learned the statistical regularities that govern it. It is often only when predictions go awry ---when the sensory input does not match with the predictions we implicitly formed--- that we become conscious of this incessant predictive activity of our brains. In the last decennia, a computational model called predictive coding emerged that attempts to formalize this matching process, hence explaining perceptual inference and learning. The predictive coding scheme describes how each level in the cortical processing hierarchy predicts inputs from levels below. In this way resources can be focused on that part of the input that was unpredicted, and therefore signals important changes in the environment that are still to be explained.In doing so the brain is guided by a single principle known as the prediction error minimization principle. Its constant effort to reduce uncertainty relative to its predictions is thought to enable adaptive behavior in a computationally manageable way. The appeal ofthis account is clear from the recent surge in studies using it to explain findings across virtually all subfields of psychology. Indeed, a generalized form of predictive coding has been proposed as Grand UnifiedTheory for psychology. Unpacking the prediction error minimization principle in these disparate fields has led to powerful, integrating insights. But is this just another short-lived fad in psychology? Or are itsroots deeper and its branches as far-reaching as its proponents foreshadow? We will only know if we examine the framework (its assumptions, implementations, implications and limitations) in full detail. The main mission of this dissertation is to do a small but, so we will argue, important part of this work. Specifically, we focus on how affective processescan have their place in this theory, since this has so far remained underexposed, given the mainly cognitive and perceptual character of previous work. To do so, we start with two elaborate 'case studies'. The firstapplies predictive coding to autism spectrum disorders, a psychiatric syndrome characterized by difficulties in social, cognitive and affectiveprocessing. We argue that the weight (or 'precision') attributed to prediction errors in autism is unduly high and inflexible, rather than determined by context uncertainty. This may explain both their peculiar behavior in cognitive and perceptual tasks, and their affective-motivationaltroubles. The second case is an application of predictive coding to visual art. Here we develop the hypothesis that making predictive progress (i.e., actively reducing prediction errors) is intrinsically pleasurable, which helps to explain affective experiences in art appreciation. These theoretical studies are followed by two empirical works, of which the first is an initial step in subjecting the hypothesis of predictive progress and pleasure to the test, using Gestalt discovery in two-tone images. The second investigates whether perception can be biased by affectiverelevance of the top-down priors or predictions applied to interpret a stimulus. It uses bistable point-light figures and finds some support that this is indeed the case. The closing chapter attempts to zoom out andbuild a coherent account of positive and negative affective value within the confines of the predictive coding framework. The core explanatory factor here is the rate of prediction error reduction and the (unexpected) changes in these rates. A host of evidence seems consistent with thisidea that affective experience reflects a form of non-conceptual metacognition about prediction error dynamics.In sum, this new application of predictive coding in issues of emotional value is worth following through, because of its parsimony, explanatory power, and the specific, testable research questions that can be derived from it. Most likely, the theory will be further refined by a joint effort of neurocomputational modelers and experimental psychologists, for which we set out the tracks in the different chapters.status: publishe