A growing number of studies in both the scientific and the philosophical literature have drawn on a Bayesian predictive processing framework to account for the formation of delusions. The key here is that delusions form because of disrupted prediction error signalling. Parrott’s recent critique argues that the framework is incomplete in two respects: it leaves unclear why delusional hypotheses are selected over none at all or over more plausible alternatives; it leaves unclear how exactly it is that delusional hypotheses are (...) generated in the first place. In this paper, we defend the framework’s explanatory power by way of showing how it can go a long way in helping disentangle these concerns. We take up each of them in turn. As for the first, our argument turns on two claims: a) believing nothing, although possible in principle, may not be a viable procedure in the circumstances; b) alternative hypotheses may be rendered unavailable for selection, due to aberrant prediction errors. As for the second concern, we distinguish three ways in which delusional hypotheses may be initially acquired, all of which are based on experience broadly construed. (shrink)

Stereotypes are often described as being generally inaccurate and irrational. However, for years, a minority of social psychologists has been proclaiming that stereotype accuracy is among the most robust findings in the field. This same minority also opposes the majority by questioning the power of self-fulfilling prophecies and thereby the construction of social reality. The present paper examines this long-standing debate from the perspective of predictive processing, an increasingly influential cognitive science theory. In this theory, stereotype accuracy and self-fulfilling prophecies (...) are two sides of the same coin, namely prediction error minimisation, pointing to a new middle course between the two existing views. On the one hand, predictive processing indicates that depicting stereotypes as generally inaccurate runs counter to their actual purpose of making the social world predictable, which supports the minority view. On the other hand, predictive processing supposes that expectations, including stereotypes, permanently affect perception and behaviour and thereby co-construct social reality, which supports the majority view. Therefore, from a predictive processing perspective, stereotypes are largely rational and not per se inaccurate, and self-fulfilling prophecies are omnipresent and greatly affect social reality. This new middle course appears to fit the empirical data better than the two existing views. (shrink)

The predictive processing account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which may employ (...) various kinds of scientific representations, differing in the scope of empirical commitments they entail. Two major perspectives on PP qua cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science. (shrink)

Why do well‐functioning psychological systems sometimes give rise to absurd beliefs that are radically misaligned with reality? Drawing on signalling theory, I develop and explore the hypothesis that groups often embrace beliefs that are viewed as absurd by outsiders as a means of signalling ingroup commitment. I clarify the game‐theoretic and psychological underpinnings of this hypothesis, I contrast it with similar proposals about the signalling functions of beliefs, and I motivate several psychological and sociological predictions that could be used to (...) distinguish it from alternative explanations of irrational group beliefs. (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) 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)

In both cognitive science and philosophy, many theorists have recently appealed to a predictive processing framework to offer explanations of why certain individuals form delusional beliefs. One aim of this essay will be to illustrate how one could plausibly develop a predictive processing account in different ways to account for the onset of different kinds of delusions. However, the second aim of this essay will be to discuss two significant limitations of the predictive processing framework. First, I shall draw on (...) the structure of explanatory why-questions to argue that predictive processing theories can only partially explain the formation of delusional beliefs. Second, I shall argue that predictive processing theories cannot explain how implausible delusional hypotheses are generated. Yet understanding why an agent even generates a delusional hypothesis is a crucial step to understanding why she eventually comes to believe it. The final section of the essay presents three alternative ways the process of hypothesis generation might be functionally divergent in cases of delusional cognition. (shrink)

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)

In vice epistemology, bad epistemic outcomes, such as maintaining false beliefs, are interpreted as indicators of blameworthy irrationality. Conversely, a growing trend in philosophical psychology advocates for environmentalist explanations, suggesting these outcomes emerge because rational cognitive processes of faultless individuals falter due to polluted environmental inputs. Building on concrete examples, I first offer a systematic analysis of the relative explanatory merits of that environmentalist project. I then use this analysis to advance the rationality debate, which has recently been identified as (...) stagnating due to an observational equivalence between environmentalist and vice accounts. Although the conceptual imprecision of vice epistemology has frequently been critiqued, the framework developed here reveals that environmentalism is (also) unable to meet various theoretical desiderata. I show why this is so and argue that, to make progress, environmentalism needs a more substantive conception of epistemic rationality. To this end, I propose that a closer engagement with questions of cognitive agency – how rational creatures can “make up” their minds about what to believe – could enable the necessary progress. (shrink)

Predictive processing models of psychopathologies are not explanatorily consistent with the present account of abstract thought. These models are based on latent variables probabilistically mapping the structure of the world. As such, they cannot be informed by representational ontology based on mental objects and states. What actually is the case is merely some terminological affinity between subjective and informational uncertainty.

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)

When the costs of acquiring knowledge outweigh the benefits of possessing it, ignorance is rational. In this paper I clarify and explore a related but more neglected phenomenon: cases in which ignorance is motivated by the anticipated costs of possessing knowledge, not acquiring it. The paper has four aims. First, I describe the psychological and social factors underlying this phenomenon of motivated ignorance. Second, I describe those conditions in which it is instrumentally rational. Third, I draw on evidence from the (...) social sciences to argue that this phenomenon of rational motivated ignorance plays an important but often unappreciated role in one of the most socially harmful forms of ignorance today: voter ignorance of societal risks such as climate change. Finally, I consider how to address the high social costs associated with rational motivated ignorance. (shrink)

Central to many discussions of motivated reasoning is the idea that it runs afoul of epistemic normativity. Reasoning differently about information supporting our prior beliefs versus information contradicting those beliefs, is frequently equated with motivated irrationality. By analyzing the normative status of belief polarization, selective scrutiny, biased assimilation and the myside bias, I show this inference is often not adequately supported. Contrary to what’s often assumed, these phenomena need not indicate motivated irrationality, even though they are instances of belief-consistent information (...) processing. Second, I engage with arguments purporting to show that belief-consistent information processing does not indicate motivated irrationality because of its mere differential treatment of confirming and non-confirming evidence, but rather because it reveals the undermining presence of an irrelevant influence, such as a desire or partisan identity-driven cognition. While linking belief-consistent reasoning to a deeper source of directional motivation to make good on the claim that it indicates motivated irrationality is indeed what’s needed, two prominent such arguments fail. The non-normativity of many reasoning processes often taken to indicate motivated irrationality is not in fact well established. (shrink)

I clarify and defend the hypothesis that human belief formation is sensitive to social rewards and punishments, such that beliefs are sometimes formed based on unconscious expectations of their likely effects on other agents – agents who frequently reward us when we hold ungrounded beliefs and punish us when we hold reasonable ones. After clarifying this phenomenon and distinguishing it from other sources of bias in the psychological literature, I argue that the hypothesis is plausible on theoretical grounds and I (...) show how it illuminates and unifies a range of psychological phenomena, including confabulation and rationalisation, positive illusions, and identity-protective cognition. (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)