Advances in artificial intelligence, as well as its increased presence in everyday life, have brought the emergence of many new phenomena, including an intriguing appearance of what seems to be a variant of body dysmorphic disorder, coined “Snapchat dysmorphia”. Body dysmorphic disorder is a DSM-5 psychiatric disorder defined as a preoccupation with one or more perceived defects or flaws in physical appearance that are not observable or appear slight to others. Snapchat dysmorphia is fueled by automated selfie filters that reflect (...) unrealistic sociocultural standard. In this paper, we discuss how body dysmorphic disorder and related body image distortions could arise, using the conceptual resources provided by the active inference framework. We suggest that these disorders involve dysfunctional self-modelling which entails maladaptive internalization of sociocultural preferences during adolescent identity formation. Identity formation is hereby described as cycles of interpersonal active inference that arbitrate between identity exploration and commitment. We propose that impaired self-modelling is unable to reduce interpersonal uncertainty during identity exploration, which, over time, degenerates into uncontrollable epistemic habits that isolate the body image from corrective sensory evidence. In light of these insights, we subsequently explore some of the consequences of image-centered social media platforms on the identity formation process. We conclude that heightened interpersonal uncertainty in this novel context could precipitate the onset of body dysmorphic disorder and related body image distortions, particularly when selfie filters are involved. (shrink)

In this paper I propose minimal criteria for a successful theory of the mechanisms of motivation, and argue that extant philosophical accounts fail to meet them. Further, I argue that a predictive processing framework gives us the theoretical power to meet these criteria, and thus ought to be preferred over existing theories. The argument proceeds as follows—motivational mental states are generally understood as mental states with the power to initiate, guide, and control action, though few existing theories of motivation explicitly (...) detail how they are meant to explain these functions. I survey two contemporary theories of motivational mental states, due to Wayne Wu and Bence Nanay, and argue that they fail to satisfactorily explain one or more of these functions. Nevertheless, I argue that together, they are capable of giving a strong account of the control function, which competing theories ought to preserve. I then go on to argue that what I call the ‘predictive theory’ of motivational mental states, which makes use of the notion of active inference, is able to explain all three of the key functions and preserves the central insights of Wu and Nanay on control. It thus represents a significant step forward in the contemporary debate. (shrink)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, in (...) the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument (...) offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)