In this philosophical exploration, we investigate the concept of robotic moral environment interaction. The common view understands moral interaction to occur between agents endowed with ethical and interactive capacities. However, recent developments in moral philosophy argue that moral interaction also occurs in relation to the environment. Here conditions and situations of the environment contribute to human moral cognition and the formation of our moral experiences. Based on this philosophical position, we imagine robots interacting as moral environments—a novel conceptualization of human–robot (...) moral interaction with an inherent capacity for moral augmentation. To explicate this idea, we first define moral environments as moral systems providing moral affordances. We then intuit and explicate two constitutive conditions of moral environments: the environment’s moral ambiance and its moral atmosphere and compare them with real-life cases of moral environments—the moral landmarks. Based on these explications, we construct several explanatory cases to illustrate robots interacting as moral environments. Lastly, we set to evaluate the ethical challenges and wider social ramifications of using moral environment robots in the public space. (shrink)

Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality—the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality—the specific way in which concepts productively combine to yield our thoughts. I consider two strategies for (...) avoiding these objections and I argue that both confront formidable challenges. (shrink)

Over the last several years, predictive processing approaches to computational neuropsychiatry have been gaining explanatory traction. According to these accounts, some of the positive symptoms of schizophrenia arise from aberrant precision-weighting during hierarchical Bayesian inference. In contrast to computational approaches, the phenomenological tradition in psychiatry holds that disruptions or alterations of the self (Ichstörungen) lie at the core of schizophrenia. In this article, we aim to integrate these approaches. We align ourselves with the phenomenological insight that self-disturbances lie at the (...) core of schizophrenia and with the hierarchical conception of the self-model that emerges from the predictive processing architecture. We suggest that disruptions of the “hard core” of the narrative self-model might be partially involved in the generation of aberrant experiences and hyperreflexivity, and in some aspects of the sense of self-alienation and ambiguity that is characteristic of the phenomenology of schizophrenia. (shrink)

Voices are a complex and rich acoustic signal processed in an extensive cortical brain network. Specialized regions within this network support voice perception and production and may be differentially affected in pathological voice processing. For example, the experience of hallucinating voices has been linked to hyperactivity in temporal and extra-temporal voice areas, possibly extending into regions associated with vocalization. Predominant self-monitoring hypotheses ascribe a primary role of voice production regions to auditory verbal hallucinations. Alternative postulations view a generalized perceptual salience (...) bias as causal to AVH. These theories are not mutually exclusive as both ascribe the emergence and phenomenology of AVH to unbalanced top-down and bottom-up signal processing. The focus of the current study was to investigate the neurocognitive mechanisms underlying predisposition brain states for emergent hallucinations, detached from the effects of inner speech. Using the temporal voice area localizer task, we explored putative hypersalient responses to passively presented sounds in relation to hallucination proneness. Furthermore, to avoid confounds commonly found in in clinical samples, we employed the Launay-Slade Hallucination Scale for the quantification of HP levels in healthy people across an experiential continuum spanning the general population. We report increased activation in the right posterior superior temporal gyrus during the perception of voice features that positively correlates with increased HP scores. In line with prior results, we propose that this right-lateralized pSTG activation might indicate early hypersensitivity to acoustic features coding speaker identity that extends beyond own voice production to perception in healthy participants prone to experience AVH. (shrink)

Delusions are usually considered as harmful and dysfunctional beliefs, one of the primary symptoms of a psychiatric illness and the mark of madness in popular culture. However, in recent times a much more positive role has been advocated for delusions. More specifically, it has been argued that delusions might be an answer to a problem rather than problems in themselves. By delivering psychological and epistemic benefits, delusions would allow people who face severe biological or psychological difficulties to survive in their (...) environment - although this has obvious epistemic costs, as the delusion is fixed and irresponsive to compelling counterevidence. In other words, it has been argued that delusions are biologically adaptive. The adaptiveness of delusions has been compared by Ryan McKay and Daniel Dennett to a shear pin, a mechanism installed in the drive engine of some machines which is designed to shear whenever the machine is about to break down. By breaking, shear pins prevent the machine from collapsing and allow it to keep functioning, although in an impaired manner. Similarly, when delusions form, they would allow a cognitive or psychological system which is about to collapse to continue its functioning, although in an impaired manner. However, this optimistic picture of delusions risks being undermined by both theoretical and empirical considerations. Using Sarah Fineberg and Philip Corlett’s recent predictive coding account as a paradigmatic model of the biological adaptiveness of delusions, I develop two objections to it: principles of parsimony and simplicity suggest that maladaptive models of delusions have an upper hand over adaptive models; and the available empirical evidence suggests that at least some delusions stand good chances of being psychologically adaptive, but it is unlikely that they also qualify as biologically adaptive. (shrink)

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. (shrink)

A recent critique of hierarchical Bayesian models of delusion argues that, contrary to a key assumption of these models, belief formation in the healthy (i.e., neurotypical) mind is manifestly non-Bayesian. Here we provide a deeper examination of the empirical evidence underlying this critique. We argue that this evidence does not convincingly refute the assumption that belief formation in the neurotypical mind approximates Bayesian inference. Our argument rests on two key points. First, evidence that purports to reveal the most damning violation (...) of Bayesian updating in human belief formation is counterweighted by substantial evidence that indicates such violations are the rare exception—not a common occurrence. Second, the remaining evidence does not demonstrate convincing violations of Bayesian inference in human belief updating; primarily because this evidence derives from study designs that produce results that are not obviously inconsistent with Bayesian principles. (shrink)

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it (...) is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of (...) principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

Is action-guiding vision cognitively penetrable? More specifically, is the visual processing that guides our goal-directed actions sensitive to semantic information from cognitive states? This paper critically examines a recent family of arguments whose aim is to challenge a widespread and influential view in philosophy and cognitive science: the view that action-guiding vision is cognitively impenetrable. I argue, in response, that while there may very well be top–down causal influences on action-guiding vision, they should not be taken to be an instance (...) of cognitive penetration. Assuming otherwise is to assign a computational role to the influencing states that they cannot perform. Although questions about cognitive penetrability are ultimately empirical, the issues addressed in this paper are largely philosophical. The discussion here highlights an important set of considerations that help better understand the relations between cognition, vision, and action. (shrink)

Predictive processing and its apparent commitment to explaining cognition in terms of Bayesian inference over hierarchical generative models seems to flatly contradict the pragmatist conception of mind and experience. Against this, I argue that this appearance results from philosophical overlays at odd with the science itself, and that the two frameworks are in fact well-poised for mutually beneficial theoretical exchange. Specifically, I argue: first, that predictive processing illuminates pragmatism’s commitment to both the primacy of pragmatic coping in accounts of the (...) mind and the profound organism-relativity of experience; second, that this pragmatic, “narcissistic” character of prediction error minimization undermines its ability to explain the distinctive normativity of intentionality; and third, that predictive processing therefore mandates an extra-neural account of intentional content of exactly the sort that pragmatism’s communitarian vision of human thought can provide. (shrink)

This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences that a PP-theory should aim to explain: feelings are conscious, they have valence, they motivate behaviour, and they are intentional states with particular and formal objects. We then review existing theories of affective experiences within Predictive Processing and delineate two families (...) of theories: Interoceptive Inference Theories and Error Dynamics Theories. We highlight the strengths and shortcomings of each family of theories and develop a synthesis: the Affective Inference Theory. Affective Inference Theory claims that valence corresponds to the expected rate of prediction error reduction. In turn, the particular object of a feeling is the object predicted to be the most likely cause of expected changes in prediction error rate, and the formal object of a feeling is a predictive model of the expected changes in prediction error rate caused by a given particular object. Finally, our theory shows how affective experiences bias action selection, directing the organism towards allostasis and towards optimal levels of uncertainty in order to minimise prediction error over time. (shrink)

The paper addresses the issue of theory-ladenness of observation/experimentation. Motivated by a naturalistic reading of Thomas Kuhn's insights into the same topic, I draw on cognitive neuroscience (predictive coding under Free Energy Principle) to scrutinise theory-ladenness. I equate theory-ladenness with the cognitive penetrability of perceptual inferences and argue that strong theory-ladenness prevails only under uncertain circumstances. This understanding of theory-ladenness is in line with Thomas Kuhn's view on the same subject as well as a cognitive version of modest realism rather (...) than downright antirealism. (shrink)

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. (shrink)

Shaun Gallagher calls for a radical rethinking of the concept of nature and he resists reduction of phenomenology to computational-neural science. However, classic, reductionist science, at least in contemporary computational guise, has the resources to accommodate insights from transcendental phenomenology. Reductionism should be embraced, not feared.

A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters. To make progress in this debate, I (...) argue that it is fruitful to focus not on specific experimental results but rather on what I call the ‘sources of epistemic irrationality’ in human cognition. I identify four such sources and I explore whether and, if so, how Bayesian models can be reconciled with them: processing costs, evolutionary suboptimality, motivated cognition, and error management. 1 Introduction 2 The Bayesian Brain 3 The Problem of Epistemic Irrationality 3.1 Bayesian inference and rationality 3.2 Intuitive Bayesian inference 4 Sources of Epistemic Irrationality 4.1 Processing costs 4.2 Evolutionary suboptimality 4.3 Motivational influences 4.4 Error management 5 Conclusion. (shrink)

Recent research in cognitive and computational neuroscience portrays the neocortex as a hierarchically structured prediction machine. Several theorists have drawn on this research to challenge the traditional distinction between perception and cognition – specifically, to challenge the very idea that perception and cognition constitute useful kinds from the perspective of cognitive neuroscience. In place of this traditional taxonomy, such theorists advocate a unified inferential hierarchy subject to substantial bi-directional message passing. I outline the nature of this challenge and then raise (...) two objections to the cognitive architecture it proposes as a replacement: first, standard ways of characterising this inferential hierarchy are in tension with the representational reach of conceptual thought; second, there is compelling evidence that commonsense reasoning is structured around highly domain-specific intuitive theories that are difficult to situate within a single hierarchy. (shrink)

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. (shrink)

The problem of the cognitive penetrability of perception pertains to whether perceptual processing may be impacted by higher-order cognitive processes. It may be understood in a twofold sense: 1) whether what a perceptual system computes may be altered in a way that is semantically coherent to one’s cognitive states; 2) whether perceptual experience may be influenced by cognitive processes. It has been argued that the cognitive penetrability problem is not scientifically tractable since we have no direct access to other persons’ (...) subjective experiences and, therefore, we have to rely on their reports, which are mediated by higher-order processes (e.g., judgments). In this paper, I analyze the scope of methods harnessed in contemporary work on the penetrability of perception, focusing particularly on methods from experimental psychology and neuroscience. Among them, I indicate the most promising techniques and paradigms, as well as those that are inadequate to successfully tackling the problem. I also discuss experimental results which unequivocally suggest direct influences on perception which have not been addressed to date by supporters of the view that perception is impenetrable. I also describe the predictive processing theory of cognition, focusing on how it contributes to our understanding of cognitive penetrability, and discuss scientific results validating the theory. In conclusion, converging empirical evidence seems to suggest that perception is cognitively penetrable and we seem to be at the dawn of the ultimate solution of the problem. (shrink)

Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will (...) expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms. (shrink)

In this paper, I use the predictive processing theory of perception to tackle the question of how perceptual states can be rationally involved in cognition by justifying other mental states. I put forward two claims regarding the epistemological implications of PP. First, perceptual states can confer justification on other mental states because the perceptual states are themselves rationally acquired. Second, despite being inferentially justified rather than epistemically basic, perceptual states can still be epistemically responsive to the mind-independent world. My main (...) goal is to elucidate the epistemology of perception already implicit in PP. But I also hope to show how it is possible to peacefully combine central tenets of foundationalist and coherentist accounts of the rational powers of perception while avoiding the well-recognized pitfalls of either. (shrink)

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. (shrink)