Mark Miller

Predictive processing has begun to offer new insights into the nature of conscious experience—but the link is not straightforward. A wide variety of systems may be described as predictive machines, raising the question: what differentiates those for which it makes sense to talk about conscious experience? One possible answer lies in the involvement of a higher-order form of prediction error, termed expected free energy. In this paper we explore under what conditions the minimization of this new quantity might underpin conscious experience. Our suggestion is that the minimisation of Expected Free Energy is not in itself sufficient for the occurrence of conscious experience. Instead, it is relevant only insofar as it helps deliver what Ward et al. ( 2011 ) have previously described as a _sense of our own poise over an action space_. Perceptual experience, we will argue, is nothing other than the process that puts current actions in contact with goals and intentions, enabling some creatures to know the space of options that their current situation makes available. This proposal fits with recent work suggesting a deep link between conscious contents and contents computed at an ‘intermediate’ level of processing, apt for controlling action.

Emotion Review, Volume 14, Issue 1, Page 15-30, January 2022. We offer an account of mental health and well-being using the predictive processing framework. According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time. Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

Emotion Review, Volume 14, Issue 1, Page 15-30, January 2022. We offer an account of mental health and well-being using the predictive processing framework. According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time. Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

Why do we seek out and enjoy uncertain success in playing games? Game designers and researchers suggest that games whose challenges match player skills afford engaging experiences of achievement, competence, or effectance—of doing well. Yet, current models struggle to explain why such balanced challenges best afford these experiences and do not straightforwardly account for the appeal of high- and low-challenge game genres like Idle and Soulslike games. In this article, we show that Predictive Processing provides a coherent formal cognitive framework which can explain the fun in tackling game challenges with uncertain success as the dynamic process of reducing uncertainty surprisingly efficiently. In gameplay as elsewhere, people enjoy doing better than expected, which can track learning progress. In different forms, balanced, Idle, and Soulslike games alike afford regular accelerations of uncertainty reduction. We argue that this model also aligns with a popular practitioner model, Raph Koster’s Theory of Fun for Game Design, and can unify currently differentially modelled gameplay motives around competence and curiosity.

How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called ‘predictive mind’, including a powerful new way to think about the mind as embodied Recently a new perspective has arguably taken the pole position in both philosophy of mind and the cognitive sciences when it comes to discussing the nature of mind. This framework takes the brain to be a probabilistic prediction engine. Such engines, so the framework proposes, are dedicated to the task of minimizing the disparity between how they expect the world to be and how the world actually is. Part of the power of the framework is the elegant suggestion that much of what we take to be central to human intelligence - perception, action, emotion, learning and language - can be understood within the framework of prediction and error reduction. In what follows I will refer to this general approach to understanding the mind and brain as 'predictive processing'. While the predictive processing framework is in many ways revolutionary, there is a tendency for researchers interested in this topic to assume a very traditional ‘neurocentric’ stance concerning the mind. I argue that this neurocentric stance is completely optional, and that a focus on emotional processing provides good reasons to think that the predictive mind is also a deeply embodied mind. The result is a way of understanding the predictive brain that allows the body and the surrounding environment to make a robust constitutive contribution to the predictive process. While it’s true that predictive models can get us a long way in making sense of what drives the neural-economy, I will argue that a complete picture of human intelligence requires us to also explore the many ways that a predictive brain is embodied in a living body and embedded in the social-cultural world in which it was born and lives.

According to the free energy principle biological agents resist a tendency to disorder in their interactions with a dynamically changing environment by keeping themselves in sensory and physiological states that are expected given their embodiment and the niche they inhabit :127–138, 2010. doi: 10.1038/nrn2787). Why would a biological agent that aims at minimising uncertainty in its encounters with the world ever be motivated to seek out novelty? Novelty for such an agent would arrive in the form of sensory and physiological states that are unexpected. Such an agent ought therefore to avoid novel and surprising interactions with the world one might think. Yet humans and many other animals find play and other forms of novelty-seeking and exploration hugely rewarding. How can this be understood in frameworks for studying the mind that emphasise prediction error minimisation? This problem has been taken up in recent research concerned with epistemic action—actions an agent engages in to reduce uncertainty. However that work leaves two questions unanswered, which it is the aim of our paper to address. First, no account has been given yet of why it should feel good to the agent to engage the world playfully and with curiosity. Second an appeal is made to precision-estimation to explain epistemic action, yet it remains unclear how precision-weighting works in action more generally, or active inference. We argue that an answer to both questions may lie in the bodily states of an agent that track the rate at which free energy is being reduced. The recent literature on the predictive brain has connected the valence of emotional experiences to the rate of change in the reduction of prediction error :e1003094, 2013. doi: 10.1371/journal.pcbi.1003094; Van de Cruys, in Metzinger and Wiese Philosophy and predictive processing, vol 24, MIND Group, Frankfurt am Main, 2017. doi: 10.15502/9783958573253). In this literature valenced emotional experiences are hypothesised to be identical with changes in the rate at which prediction error is reduced. Experiences are negatively valenced when overall prediction error increases and are positively valenced when the sum of prediction errors decrease. We offer an ecological-enactive interpretation of the concept of valence and its connection to rate of change of prediction error. We show how rate of change should be understood in terms of embodied states of affordance-related action readiness. We then go on to apply this ecological-enactive account of error dynamics to provide an answer to the first question we have raised: It may explain why it should feel good to an agent to be curious and playful. Our ecological-enactive account also allows us to show how error dynamics may provide an answer to the second question we have raised regarding how precision-weighting works in active inference. An agent that is sensitive to rates of error reduction can tune precision on the fly. We show how this ability to tune precision on the go can allow agents to develop skills for adapting better and better to the unexpected, and search out opportunities for resolving uncertainty and progressing in its learning.

Recent work in cognitive and computational neuroscience depicts the human cortex as a multi-level prediction engine. This ‘predictive processing’ framework shows great promise as a means of both understanding and integrating the core information processing strategies underlying perception, reasoning, and action. But how, if at all, do emotions and sub-cortical contributions fit into this emerging picture? The fit, we shall argue, is both profound and potentially transformative. In the picture we develop, online cognitive function cannot be assigned to either the cortical or the sub-cortical component, but instead emerges from their tight co-ordination. This tight co-ordination involves processes of continuous reciprocal causation that weave together bodily information and ‘top-down’ predictions, generating a unified sense of what’s out there and why it matters. The upshot is a more truly ‘embodied’ vision of the predictive brain in action.