Jakob Hohwy

Appeals to the value of treating people with dignity are commonplace in certain community settings, especially in health, disability, and aged care. In contrast, dignity has been relatively neglected by philosophers, not receiving the careful scrutiny most other moral concepts have been afforded. This has recently changed, with a spate of new philosophical books and articles about the meaning and value of dignity. No consensus has emerged about what dignity means or about how it is distinct from other moral values. Here, we seek to advance the debate by integrating philosophical theory with findings from empirical studies.Specifically, we argue that when people appeal to dignity in community settings they are most likely appealing to the value of not humiliating people, where to humiliate another is to treat them as a social inferior. We review two previous empirical studies which we conducted to test how lay people understand dignity. By considering these two studies together and integrating our findings with philosophical debate about the meaning of dignity, we show in this paper that lay people indeed understand dignity in line with the view that dignity means treating others as our social equals.

Andy Clark’s exciting work on predictive processing provides the umbrella under which his hugely influential previous work on embodied and extended cognition seeks a unified home. This chapter argues that in fact predictive processing harbours internalist, inferentialist and epistemic tenets that cannot leave embodied and extended cognition unchanged. Predictive processing cannot do the work Clark requires of it without relying on rich, preconstructive internal representations of the world, nor without engaging in paradigmatically rational integration of prior knowledge and new sensory input. Hence, next to Clark’s image of fluid “uncertainty surfing” is an equally valid image of more emaciated and plodding world-modelling. Rather than underpinning orthodox embodied and extended approches, predictive processing therefore presents an opportunity for a potentially fruitful new synthesis of cognitivist and embodied approaches to cognition.

The context for our paper comes from the neurophenomenology (NPh) research programme initiated by Francisco Varela at the end of the 1990s. Varela’s working hypothesis was that, to be successful, a consciousness research programme must progress by relating first-person phenomenological accounts of the structure of experience and their third-person counterparts in neuroscience through “mutual constraints”. Leveraging Bayesian mechanics, in particular deep parametric active inference, we demonstrate the potential for epistemically advantageous mutual constraints between phenomenological, computational, behavioural, and physiological vocabularies. Specifically, the dual information geometry of Bayesian mechanics serves to establish, under certain conditions, generative passage between lived experience and its physiological instantiation. This paper argues for the epistemological necessity of such a passage and the inclusion of trained reflective awareness in neurophenomenological empirical approaches. In particular, it showcases incremental explanatory gains for the scientist that arise from incorporating the participants’ epistemic insights, shifting the focus from the contents of experience (i.e. what a subject experiences in a given experimental set-up) to the how of experience (i.e. the activities of consciousness that allow for a meaningful world to appear to us as such in lived experience). The explanatory power of the resulting ‘meta-Bayesian’ framework, deep computational NPh, arises from the disciplined circulation between first and third-person perspectives enabled by the formalism of deep parametric active inference, where parametric depth refers to a property of generative models that can form beliefs about the parameters of their own modelling process. Hence, this computational formalism contributes to understanding consciousness by bridging phenomenological descriptions and physiological instantiations, whilst also highlighting the significance of trained first-person investigation in experimental protocols.

For predictive processing, perception is tied to the upshot of probabilistic inference, which makes perception internal, affording only indirect access to the world external to the perceiver. The metaphysical implications of predictive processing however remain unresolved, which is a significant gap given the major influence of this framework across philosophy and other fields of research. Here, I present what I believe is a consistent metaphysical package of commitments for predictive processing. My starting point is a suitable challenge to predictive processing presented by Tobias Schlicht, who argues that the framework is committed to Kantian transcendental idealism, and marshals several lines of argument that this commitment undermines predictive processing’s aspirations to completeness, realism, and naturalism. I first trace Hermann von Helmholtz’s nuanced reaction to Kant, which sets out the preconditions for perception in a manner prescient of the notion of self-evidencing central to contemporary predictive processing. This position enables a fundamental structural realism, rather than idealism, which blocks Schlicht’s line of argument, allowing plausible versions of completeness, realism and naturalism. Schlicht’s challenge is nevertheless valuable because addressing it, in the specific context of Helmholtz’s response to Kant, helps bring to light the compelling structural realism at the heart of self-evidencing.

Disorders of consciousness include coma, the vegetative state and the minimally conscious state. Such patients are often regarded as unconscious. This has consequences for end of life decisions for these patients: it is much easier to justify withdrawing life support for unconscious than conscious patients. Recent brain imaging research has however suggested that some patients may in fact be conscious.We argue that these new findings should lead us to be more cautious with regard to end of life decisions for this patient group. Additionally, we argue that if their lives are to be ended, then increased caution should be exercised to avoid undue suffering.As a consequence, the already difficult ethical and clinical questions surrounding these patients are made even more difficult with regard to making and acting on end of life decisions, as well as with regard to quality of life prognoses. The best we can hope for is that research both on disorders of consciousness and on the neural correlates of consciousness will progress more and make these kinds of questions easier to address in the future.

Dignity is prominently endorsed in healthcare, organizations, and law. However, humanities research casts doubt over the utility of this concept, disputing that “dignity” captures any unique ethical value distinguishable from “respect”. Here, we test a recent proposal that dignity entails special regard for humiliation. We created a set of vignettes describing a ‘perpetrator’ making offensive remarks or gestures to a ‘victim’, and asked lay participants to rate how well each offense corresponds to a dignity violation as opposed to a respect violation. We manipulated the victims’ resulting emotions within-subject, across ‘humiliation’, ‘anger’, and ‘baseline’ conditions, while controlling for other factors (e.g. victim’s gender, vulnerability). We found substantial evidence (BF=4.11) for our preregistered hypothesis that humiliation increased dignity-relatedness ratings as compared to the baseline condition, and very strong evidence when compared to the anger condition (BF=84.75). These findings provide empirical backing to dignitarian ethics with applications in healthcare, organizations, and law.

The frame or relevance problem is a classic problem in cognitive science and philosophy. We attempt to resolve this problem by appealing to predictive processing, a growing theory of cognition. As such, it ought to explain one of the central processes of cognition, that is, how an agent context-sensitively determines relevance. Our solution begins by appealing to Bayesian prior probabilities, which intuitively reflect relevance for a predictive agent. However, prior probabilities are necessary but insufficient for solving the problem with predictive processing. We then turn to the broader predictive processing toolbox, leveraging the concepts of prediction, prediction error, and precision in order to explain relevance. This move reveals that the processes that optimize for prediction error minimization are crucial for realizing relevance. Although, they do not yet solve the entire problem, which also demands an agent select relevant actions, based on considerations about their consequences. By appealing to active inference, decision-making, and planning can be brought to bear on relevance, in addition to perceptual inference. With this final inclusion of action (as inference), we suggest predictive processing has the tools to comprehensively solve the problem of relevance.

The Self-Evidencing Agent offers a unique method for addressing difficult philosophical questions. Self-evidencing occurs when an agent uses their model of the world and of themselves to explain what they observe in the world and in themselves, such that those observations become evidence for their model – the more agents explain, the more they self-evidence. This book argues that there is good reason to cast an agent’s existence itself in terms of self-evidencing, and that if we begin from this as a first principle, then we can make better sense of who and what we are, of how we engage in diverse ways with the world, and what matters to us. Self-evidencing can help situate attention, action, and perception. It also furnishes compelling approaches to decision-making and rationality, the self, of preferences and values. It can build bridges to consciousness, and even on to free will, and to meaning and wisdom.

This book brings together perspectives on predictive processing and expected experience. It features contributions from an interdisciplinary group of authors specializing in philosophy, psychology, cognitive science, and neuroscience. Predictive processing, or predictive coding, is the theory that the brain constantly minimizes the error of its predictions based on the sensory input it receives from the world. This process of prediction error minimization has numerous implications for different forms of conscious and perceptual experience. The chapters in this volume explore these implications and various phenomenon related to them. The contributors tackle issues related to precision estimation, sensory prediction, probabilistic perception, and attention, as well as the role predictive processing plays in emotion, action, psychotic experience, anosognosia, and gut complex. Expected Experiences will be of interest to scholars and advanced students in philosophy, psychology, and cognitive science working on issues related to predictive processing and coding.

We introduce the predictive processing account of body representation, according to which body representation emerges via a domain-general scheme of (long-term) prediction error minimisation. We contrast this account against one where body representation is underpinned by domain-specific systems, whose exclusive function is to track the body. We illustrate how the predictive processing account offers considerable advantages in explaining various empirical findings, and we draw out some implications for body representation research.

Self-evidencing describes the purported predictive processing of all self-organising systems, whether conscious or not. Self-evidencing in itself is therefore not sufficient for consciousness. Different systems may however be capable of self-evidencing in different, specific and distinct ways. Some of these ways of self-evidencing can be matched up with, and explain, several properties of consciousness. This carves out a distinction in nature between those systems that are conscious, as described by these properties, and those that are not. This approach throws new light on phenomenology, and suggests that some self-evidencing may be characteristic of consciousness.

Among all their sensations, agents need to distinguish between those caused by themselves and those caused by external causes. The ability to infer agency is particularly challenging under conditions of uncertainty. Within the predictive processing framework, this should happen through active control of prediction error that closes the action-perception loop. Here we use a novel, temporally-sensitive, behavioural proxy for prediction error to show that it is minimised most quickly when volatility is high and when participants report agency, regardless of the accuracy of the judgement. We demonstrate broad effects of uncertainty on accuracy of agency judgements, movement, policy selection, and hypothesis switching. Measuring autism traits, we find differences in policy selection, sensitivity to uncertainty and hypothesis switching despite no difference in overall accuracy.

The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.

At present, the science of consciousness is structured around the search for the neural correlates of consciousness (the NCCs). One of the alleged advantages of the NCCs framework is its metaphysical neutrality—the fact that it begs no contested questions with respect to debates about the fundamental nature of consciousness. Here, we argue that even if the NCC framework is metaphysically neutral, it is structurally committed, for it presupposes a certain model—what we call the Lite-Brite model—of consciousness. This, we argue, represents a serious liability for the NCC framework for the plausibility of the Lite-Brite model is very much an open question, and the science of consciousness would be better served by a framework that does not presuppose it. Drawing on interventionist ideas in the philosophy of science, we suggest that the Difference-Maker framework can provide just such an alternative. Instead of searching for the neural correlates of consciousness (NCCs), we ought to be searching for the difference makers of consciousness (DMCs). We detail how a shift to searching DMCs will change both the practice of consciousness science and the interpretation of existing results.

In previous research on the evaluation of food images, we found that appetitive food images were rated higher following a positive prediction than following a negative prediction, and vice versa for aversive food images. The findings suggested an active confirmation bias. Here, we examine whether this influence from prediction depends on the evaluative polarization of the food images. Specifically, we divided the set of food images into "strong" and "mild" images by how polarized (i.e., extreme) their average ratings were across all conditions. With respect to the influence from prediction, we raise two alternative hypotheses. According to a predictive dissonance hypothesis, the larger the discrepancy between prediction and outcome, the stronger the active inference toward accommodating the outcome with the prediction; thus, the confirmation bias should obtain particularly with strong images. Conversely, according to a nudging-in-volatility hypothesis, the active confirmation bias operates only on images within a dynamic range, where the values of images are volatile, and not on the evaluation of images that are too obviously appetitive or aversive; accordingly, the effects from prediction should occur predominately with mild images. Across the data from two experiments, we found that the evaluation of mild images tended to exhibit the confirmation bias, with ratings that followed the direction given by the prediction. For strong images, there was no confirmation bias. Our findings corroborate the nudging-in-volatility hypothesis, suggesting that predictive cues may be able to tip the balance of evaluation particularly for food images that do not have a strongly polarized value.

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity. Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual inference.

The free energy principle says that any self-organising system that is at nonequilibrium steady-state with its environment must minimize its free energy. It is proposed as a grand unifying principle for cognitive science and biology. The principle can appear cryptic, esoteric, too ambitious, and unfalsifiable—suggesting it would be best to suspend any belief in the principle, and instead focus on individual, more concrete and falsifiable ‘process theories’ for particular biological processes and phenomena like perception, decision and action. Here, I explain the free energy principle, and I argue that it is best understood as offering a conceptual and mathematical analysis of the concept of existence of self-organising systems. This analysis offers a new type of solution to long-standing problems in neurobiology, cognitive science, machine learning and philosophy concerning the possibility of normatively constrained, self-supervised learning and inference. The principle can therefore uniquely serve as a regulatory principle for process theories, to ensure that process theories conforming to it enable self-supervision. This is, at least for those who believe self-supervision is a foundational explanatory task, good reason to believe the free energy principle.

Predictive processing (PP) is now a prominent theoretical framework in the philosophy of mind and cognitive science. This review focuses on PP research with a relatively philosophical focus, taking stock of the framework and discussing new directions. The review contains an introduction that describes the full PP toolbox; an exploration of areas where PP has advanced understanding of perceptual and cognitive phenomena; a discussion of PP's impact on foundational issues in cognitive science; and a consideration of the philosophy of science of PP. The overall picture is that PP is a fruitful framework, with exciting new directions awaiting exploration.

There is a view on consciousness that has strong intuitive appeal and empirical support: the intermediate-level theory of consciousness, proposed mainly by Ray Jackendoff and by Jesse Prinz. This theory identifies a specific “intermediate” level of representation as the basis of human phenomenal consciousness, which sits between high-level non-perspectival thought processes and low-level disjointed feature-detection processes in the perceptual and cognitive processing hierarchy. In this article, we show that the claim that consciousness arises at an intermediate-level is true of some cognitive systems, but only in virtue of specific constraints on their active interactions with the environment. We provide ecological reasons for why certain processing levels in a cognitive hierarchy are privileged with respect to consciousness. We do this from the perspective of a prediction-error minimization model of perception and cognition, relying especially on the notion of active inference: the privileged level for consciousness depends on the specific dispositions of an organism concerned with inferring its policies for action. Such a level is indeed intermediate for humans, but this depends on the spatiotemporal resolution of the typical actions that a human organism can normally perform. Thus, intermediateness is not an essential feature of consciousness. In organisms with different action dispositions the privileged level or levels may differ as well.

This chapter focuses on what’s novel in the perspective that the prediction error minimization (PEM) framework affords on the cognitive-scientific project of explaining intelligence by appeal to internal representations. It shows how truth-conditional and resemblance-based approaches to representation in generative models may be integrated. The PEM framework in cognitive science is an approach to cognition and perception centered on a simple idea: organisms represent the world by constantly predicting their own internal states. PEM theories often stress the hierarchical structure of the generative models they posit. The novel explanatory power of the PEM account derives largely from the way in which pairs of generative and recognition models interact. “Predictive coding” refers to an encoding strategy in which predicted portions of an input signal are subtracted from the actual signal received, so that only the difference between the two is passed as output to the next stage of information processing.