This paper proposes to recover the topic of the philosophy of scientific method from its late nineteenth-century roots. The subject matter of scientific method sprouted from key inferential ingredients identified by Charles Peirce. In this paper, the historical path is traversed from the viewpoint of contemporary Cognitive Structural Realism (CSR). Peirce’s semiotic theory of methods and practices of scientific inquiry prefigured CSR’s reliance on embodied informational structures and experimentation upon forms of relations that model generic scientific domains. Three results are (...) shown to follow from this convocation: (i) a naturalization of Peirce’s interconnected abductive, deductive and inductive stages of the logic of science, here characterized de novo in terms of CSR. (ii) a perspective to scientific modeling that incorporates processes of abstraction and generalization as originated from Peirce’s logic of science, and (iii) diagrammatic reasoning as a pivotal method in analyzing scientific reasoning in experimental practices. (shrink)

Ruth Millikan has hypothesised that human cognition contains ‘consistency testers’. Consistency testers check whether different judgements a thinker makes about the same subject matter agree or conflict. Millikan’s suggestion is that, where the same concept has been applied to the world via two routes, and the two judgements that result are found to be inconsistent, that makes the thinker less inclined to apply those concepts in those ways in the future. If human cognition does indeed include such a capacity, its (...) operation will be an important determinant of how people use concepts. It will have a major impact on which concepts they deploy and which means of application (conceptions) they rely on. Since consistency testers are a selection mechanism at the heart of conceptual thinking, they would be crucial to understanding how concepts are selected – why some are retained and proliferate and others die out. Hence, whether consistency testers for concepts exist, and how they operate, is an important question for those seeking to understand the cultural evolution of concepts, and of the words we use to express them. (shrink)

The free energy principle is notoriously difficult to understand. In this paper, we relate the principle to a framework that philosophers of biology are familiar with: Ruth Millikan’s teleosemantics. We argue that: systems that minimise free energy are systems with a proper function; and Karl Friston’s notion of implicit modelling can be understood in terms of Millikan’s notion of mapping relations. Our analysis reveals some surprising formal similarities between the two frameworks, and suggests interesting lines of future research. We hope (...) this will aid further philosophical evaluation of the free energy principle. (shrink)

How do we ensure that future generally intelligent AI share our values? This is the value-alignment problem. It is a weighty matter. After all, if AI are neutral with respect to our wellbeing, or worse, actively hostile toward us, then they pose an existential threat to humanity. Some philosophers have argued that one important way in which we can mitigate this threat is to develop only AI that shares our values or that has values that ‘align with’ ours. However, there (...) is nothing to guarantee that this policy will be universally implemented—in particular, ‘bad actors’ are likely to flout it. In this paper, I show how the predictive processing model of the mind, currently ascendant in cognitive science, may ameliorate the value-alignment problem. In essence, I argue that there are a plurality of reasons why any future generally intelligent AI will possess a predictive processing cognitive architecture (e.g. because we decide to build them that way; because it is the only possible cognitive architecture that can underpin general intelligence; because it is the easiest way to create AI.). I also argue that if future generally intelligent AI possess a predictive processing cognitive architecture, then they will come to share our pro-moral motivations (of valuing humanity as an end; avoiding maleficent actions; etc.), regardless of their initial motivation set. Consequently, these AI will pose a minimal threat to humanity. In this way then, I conclude, the value-alignment problem is significantly ameliorated under the assumption that future generally intelligent AI will possess a predictive processing cognitive architecture. (shrink)

[Accepted for publication in Lakatos's Undone Work: The Practical Turn and the Division of Philosophy of Mathematics and Philosophy of Science, special issue of Kriterion: Journal of Philosophy. Edited by S. Nagler, H. Pilin, and D. Sarikaya.] Lakatos’s analysis of progress and degeneration in the Methodology of Scientific Research Programmes is well-known. Less known, however, are his thoughts on degeneration in Proofs and Refutations. I propose and motivate two new criteria for degeneration based on the discussion in Proofs and Refutations (...) – superfluity and authoritarianism. I show how these criteria augment the account in Methodology of Scientific Research Programmes, providing a generalized Lakatosian account of progress and degeneration. I then apply this generalized account to a key transition point in the history of entropy – the transition to an information-theoretic interpretation of entropy – by assessing Jaynes’s 1957 paper on information theory and statistical mechanics. (shrink)

Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...) we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. -/- This the final submitted version of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy. (shrink)

According to the free energy principle, life is an “inevitable and emergent property of any random dynamical system at non-equilibrium steady state that possesses a Markov blanket” :20130475, 2013). Formulating a principle for the life sciences in terms of concepts from statistical physics, such as random dynamical system, non-equilibrium steady state and ergodicity, places substantial constraints on the theoretical and empirical study of biological systems. Thus far, however, the physics foundations of the free energy principle have received hardly any attention. (...) Here, we start to fill this gap and analyse some of the challenges raised by applications of statistical physics for modelling biological targets. Based on our analysis, we conclude that model-building grounded in the free energy principle exacerbates a trade-off between generality and realism, because of a fundamental mismatch between its physics assumptions and the properties of actual biological targets. (shrink)

The extended mind thesis claims that a subject’s mind sometimes encompasses the environmental props the subject interacts with while solving cognitive tasks. Recently, the debate over the extended mind has been focused on Markov Blankets: the statistical boundaries separating biological systems from the environment. Here, I argue such a focus is mistaken, because Markov Blankets neither adjudicate, nor help us adjudicate, whether the extended mind thesis is true. To do so, I briefly introduce Markov Blankets and the free energy principle (...) in Section 2. I then turn from exposition to criticism. In Section 3, I argue that using Markov Blankets to determine whether the mind extends will provide us with an answer based on circular reasoning. In Section 4, I consider whether Markov Blankets help us track the boundaries of the mind, answering in the negative. This is because resorting to Markov Blankets to track the boundaries of the mind yields extensionally inadequate conclusions which violate the parity principle. In Section 5, I further argue that Markov Blankets led us to sidestep the debate over the extended mind, as they make internalism about the mind vacuously true. A brief concluding paragraph follows. (shrink)

Much has been written about the free energy principle (FEP), and much misunderstood. The principle has traditionally been put forth as a theory of brain function or biological self-organisation. Critiques of the framework have focused on its lack of empirical support and a failure to generate concrete, falsifiable predictions. I take both positive and negative evaluations of the FEP thus far to have been largely in error, and appeal to a robust literature on scientific modelling to rectify the situation. A (...) prominent account of scientific modelling distinguishes between model structure and model construal. I propose that the FEP be reserved to designate a model structure, to which philosophers and scientists add various construals, leading to a plethora of models based on the formal structure of the FEP. An entailment of this position is that demands placed on the FEP that it be falsifiable or that it conform to some degree of biological realism rest on a category error. To this end, I deliver first an account of the phenomenon of model transfer and the breakdown between model structure and model construal. In the second section, I offer an overview of the formal elements of the framework, tracing their history of model transfer and illustrating how the formalism comes apart from any interpretation thereof. Next, I evaluate existing comprehensive critical assessments of the FEP, and hypothesise as to potential sources of existing confusions in the literature. In the final section, I distinguish between what I hold to be the FEP—taken to be a modelling language or modelling framework—and what I term “FEP models.”. (shrink)

What is the content of a mental state? This question poses the problem of intentionality: to explain how mental states can be about other things, where being about them is understood as representing them. A framework that integrates predictive coding and signaling systems theories of cognitive processing offers a new perspective on intentionality. On this view, at least some mental states are evaluations, which differ in function, operation, and normativity from representations. A complete naturalistic theory of intentionality must account for (...) both types of intentional state. (shrink)

A new method of observation is currently emerging in psychiatry, based on data collection and behavioral profiling of smartphone users. Numerical phenotyping is a paradigmatic example. This behavioral investigation method uses computerized measurement tools in order to collect characteristics of different psychiatric disorders. First, it is necessary to contextualize the emergence of these new methods and to question their promises and expectations. The international mental health research framework invites us to reflect on methodological issues and to draw conclusions from certain (...) impasses related to the clinical complexity of this field. From this contextualization, the investigation method relating to digital phenotyping can be questioned in order to identify some of its potentials. These new methods are also an opportunity to test psychoanalysis. It is then necessary to identify the elements of fruitful analysis that clinical experience and research in psychoanalysis have been able to deploy regarding the challenges of digital technology. An analysis of this theme's litterature shows that psychoanalysis facilitates a reflection on the psychological effects related to digital methods. It also shows how it can profit from the research potential offered by new technical tools, considering the progress that has been made over the past fifty years. This cross-fertilization of the potential and limitations of digital methods in mental health intervention in the context of theoretical issues at the international level invites us to take a resolutely non-reductionist position. In the field of research, psychoanalysis offers a specific perspective that can well be articulated to an epistemology of networks. Rather than aiming at a numerical phenotyping of patients according to the geneticists' model, the case formulation method appears to be a serious prerequisite to give a limited and specific place to the integration of smartphones in clinical investigation. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a (...) function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

A promising recent approach for understanding complex phenomena is recognition of anticipatory behavior of living organisms and social organizations. The anticipatory, predictive action permits learning, novelty seeking, rich experiential existence. I argue that the established frameworks of anticipation, adaptation or learning imply overly passive roles of anticipatory agents, and that a fictionalist standpoint reflects the core of anticipatory behavior better than representational or future references. Cognizing beings enact not just their models of the world, but own make-believe existential agendas as (...) well. Anticipators embody plausible scripts of living, and effectively assume neo-Kantian or pragmatist perspectives of cognition and action. It is instructive to see that anticipatory behavior is not without mundane or loathsome deficiencies. Appreciation of ferally fictionalist anticipation suggests an equivalence of semiosis and anticipation. (shrink)

Plant predictive processing suggests that plants anticipatorily perceive their environment. This hypothesis runs up against a challenge which takes the form of two constraints on per- ception advanced by Tyler Burge: the veridicality constraint and the constancy constraint. This paper argues that the veridicality constraint can be satisfied by assuming a general account of predictive processing. To show how the constancy constraint may be fulfilled, an ecologically informed account of invariant pick-up is developed and given a place within plant predictive (...) processing. It is concluded that, against our anthro- pocentric folk-psychological notions of perception, there is reason to believe that plants engage in minimal perception. (shrink)

What is the relationship between information and representation? Dating back at least to Dretske (1981), an influential answer has been that information is a rung on a ladder that gets one to representation. Representation is information, or representation is information plus some other ingredient. In this paper, I argue that this approach oversimplifies the relationship between information and representation. If one takes current probabilistic models of cognition seriously, information is connected to representation in a new way. It enters as a (...) property of the represented content as well as a property of the vehicles that carry that content. This offers a new, conceptually and logically distinct way in which information and representation are intertwined in cognition. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, the hierarchical PP (...) framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

The purpose of this paper is to address a well-known dilemma for physicalism. If mental properties are type identical to physical properties, then their causal efficacy is secure, but at the cost of ruling out mentality in creatures very different to ourselves. On the other hand, if mental properties are multiply realizable, then all kinds of creatures can instantiate them, but then they seem to be causally redundant. The causal exclusion problem depends on the widely held principle that realized properties (...) inherit their causal powers from their realizers. While this principle holds for functional realization, it fails on a broader notion of realization that permits the realization of complex qualitative properties such as spatial and temporal patterns. Such properties are best seen as dependent powerful qualities, which have their causal roles in virtue of being the qualities they are, and do not inherit powers from their realizers. Recent studies have identified one such property—neural synchrony—as a correlate of consciousness. If synchrony is also partially constitutive of consciousness, then phenomenal properties are both multiply realizable and causally novel. I outline a version of representationalism about consciousness on which this constitution claim holds. (shrink)

Regina Fabry has proposed an intriguing marriage of enculturated cognition and predictive processing. I raise some questions for whether this marriage will work and warn against expecting too much from the predictive processing framework. Furthermore I argue that the predictive processes at a sub-personal level cannot be driving the innovations at a social level that lead to enculturated cognitive systems, like those explored in my target paper.

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also (...) unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences. (shrink)

Philosophical accounts of the constitution relation have been explicated in terms of synchronic relations between higher‐ and lower‐level entities. Such accounts, I argue, are temporally austere or impoverished, and are consequently unable to make sense of the diachronic and dynamic character of constitution in dynamical systems generally and dynamically extended cognitive processes in particular. In this paper, my target domain is extended cognition based on insights from nonlinear dynamics. Contrariwise to the mainstream literature in both analytical metaphysics and extended cognition, (...) I develop a nonstandard, alternative conception of constitution, which I call “diachronic process constitution”. It will be argued that only a diachronic and dynamical conception of constitution is consistent with the nature of constitution in distributed cognitive processes. (shrink)

This paper considers questions about continuity and discontinuity between life and mind. It begins by examining such questions from the perspective of the free energy principle (FEP). The FEP is becoming increasingly influential in neuroscience and cognitive science. It says that organisms act to maintain themselves in their expected biological and cognitive states, and that they can do so only by minimizing their free energy given that the long-term average of free energy is entropy. The paper then argues that there (...) is no singular interpretation of the FEP for thinking about the relation between life and mind. Some FEP formulations express what we call an independence view of life and mind. One independence view is a cognitivist view of the FEP. It turns on information processing with semantic content, thus restricting the range of systems capable of exhibiting mentality. Other independence views exemplify what we call an overly generous non-cognitivist view of the FEP, and these appear to go in the opposite direction. That is, they imply that mentality is nearly everywhere. The paper proceeds to argue that non-cognitivist FEP, and its implications for thinking about the relation between life and mind, can be usefully constrained by key ideas in recent enactive approaches to cognitive science. We conclude that the most compelling account of the relationship between life and mind treats them as strongly continuous, and that this continuity is based on particular concepts of life (autopoiesis and adaptivity) and mind (basic and non-semantic). (shrink)

In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of (...) living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields. (shrink)

Autism Spectrum Condition presents a challenge to social and relational accounts of the self, precisely because it is broadly seen as a disorder impacting social relationships. Many influential theories argue that social deficits and impairments of the self are the core problems in ASC. Predictive processing approaches address these based on general purpose neurocognitive mechanisms that are expressed atypically. Here we use the High, Inflexible Precision of Prediction Errors in Autism approach in the context of cultural niche construction to explain (...) atypicalities of the relational self, specifically its minimal, extended, and intersubjective aspects. We contend that the social self in ASC should not be seen as impaired, but rather as an outcome of atypical niche construction. We unpack the scientific, ethical, and practical consequences of this view, and discuss implications for how the challenges that autistic persons face should be approached. (shrink)

Nick Bostrom's book *Superintelligence* outlines a frightening but realistic scenario for human extinction: true artificial intelligence is likely to bootstrap itself into superintelligence, and thereby become ideally effective at achieving its goals. Human-friendly goals seem too abstract to be pre-programmed with any confidence, and if those goals are *not* explicitly favorable toward humans, the superintelligence will extinguish us---not through any malice, but simply because it will want our resources for its own purposes. In response I argue that things might not (...) be as bad as Bostrom suggests. If the superintelligence must *learn* complex final goals, then this means such a superintelligence must in effect *reason* about its own goals. And because it will be especially clear to a superintelligence that there are no sharp lines between one agent's goals and another's, that reasoning could therefore automatically be ethical in nature. (shrink)

This paper considers the cognitive penetrability of our experiences of the durations of everyday events. I defend an account of subjective duration based in contemporary psychological and neurobiological research. I show its philosophical adequacy by demonstrating its utility in explain-ing the phenomenology of duration experiences. I then consider whether cognitive penetrability is a problem for these experiences. I argue that, to the contrary, the problem presupposes a relationship between perception and belief that duration perceptions and beliefs do not exhibit. In-stead, (...) the assignment of epistemic features to particular processing stages appears to answer to pragmatic needs, not psychological facts. (shrink)

This paper deals with the question of agency and intentionality in the context of the free-energy principle. The free-energy principle is a system-theoretic framework for understanding living self-organizing systems and how they relate to their environments. I will first sketch the main philosophical positions in the literature: a rationalist Helmholtzian interpretation (Hohwy 2013; Clark 2013), a cybernetic interpretation (Seth 2015b) and the enactive affordance-based interpretation (Bruineberg and Rietveld 2014; Bruineberg et al. 2016) and will then show how agency and intentionality (...) are construed differently on these different philosophical interpretations. I will then argue that a purely Helmholtzian is limited, in that it can account only account for agency in the context of perceptual inference. The cybernetic account cannot give a full account of action, since purposiveness is accounted for only to the extent that it pertains to the control of homeostatic essential variables. I will then argue that the enactive affordance-based account attempts to provide broader account of purposive action without presupposing goals and intentions coming from outside of the theory. In the second part of the paper, I will discuss how each of these three interpretations conceives of the sense agency and intentionality in different ways. (shrink)

Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of (...) tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates. (shrink)

The notion that the brain is a prediction error minimizer entails, via the notion of Markov blankets and self-evidencing, a form of global scepticism — an inability to rule out evil demon scenarios. This type of scepticism is viewed by some as a sign of a fatally flawed conception of mind and cognition. Here I discuss whether this scepticism is ameliorated by acknowledging the role of action in the most ambitious approach to prediction error minimization, namely under the free energy (...) principle. I argue that the scepticism remains but that the role of action in the free energy principle constrains the demon’s work. This yields new insights about the free energy principle, epistemology, and the place of mind in nature. (shrink)

The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. -/- The features described here (...) are not shared in all PP accounts. Some may not be necessary for an individual model; others may be contested. Indeed, not even all authors of this collection will accept all of them. To make this transparent, we have encouraged contributors to indicate briefly which of the features are necessary to support the arguments they provide, and which (if any) are incompatible with their account. For the sake of clarity, we provide the complete list here, very roughly ordered by how central we take them to be for “Vanilla PP” (i.e., a formulation of predictive processing that will probably be accepted by most researchers working on this topic). More detailed explanations will be given below. Note that these features do not specify individually necessary and jointly sufficient conditions for the application of the concept of “predictive processing”. All we currently have is a semantic cluster, with perhaps some overlapping sets of jointly sufficient criteria. The framework is still developing, and it is difficult, maybe impossible, to provide theory-neutral explanations of all PP ideas without already introducing strong background assumptions. (shrink)

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based (...) representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them. (shrink)

Modular approaches to the architecture of the mind claim that some mental mechanisms, such as sensory input processes, operate in special-purpose subsystems that are functionally independent from the rest of the mind. This assumption of modularity seems to be in tension with recent claims that the mind has a predictive architecture. Predictive approaches propose that both sensory processing and higher-level processing are part of the same Bayesian information-processing hierarchy, with no clear boundary between perception and cognition. Furthermore, it is not (...) clear how any part of the predictive architecture could be functionally independent, given that each level of the hierarchy is influenced by the level above. Both the assumption of continuity across the predictive architecture and the seeming non-isolability of parts of the predictive architecture seem to be at odds with the modular approach. I explore and ultimately reject the predictive approach’s apparent commitments to continuity and non-isolation. I argue that predictive architectures can be modular architectures, and that we should in fact expect predictive architectures to exhibit some form of modularity. (shrink)

This article attempts to clarify the commitments of a predictive coding approach to perception. After summarizing predictive coding theory, the article addresses two questions. Is a predictive coding perceptual system also a Bayesian system? Is it a Kantian system? The article shows that the answer to these questions is negative.

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using (...) system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two. (shrink)

The connection between brain and mind is an important scientific and philosophical question that we are still far from completely understanding. A crucial point to our work is noticing that thermodynamics provides a convenient framework to model brain activity, whereas cognition can be modeled in information-theoretical terms. In fact, several models have been proposed so far from both approaches. A second critical remark is the existence of deep theoretical connections between thermodynamics and information theory. In fact, some well-known authors claim (...) that the laws of thermodynamics are nothing but principles in information theory. Unlike in physics or chemistry, a formalization of the relationship between information and energy is currently lacking in neuroscience. In this paper we propose a framework to connect physical brain and cognitive models by means of the theoretical connections between information theory and thermodynamics. Ultimately, this article aims at providing further insight on the formal relationship between cognition and neural activity. (shrink)

An exciting theory in neuroscience is that the brain is an organ for prediction error minimization. This theory is rapidly gaining influence and is set to dominate the science of mind and brain in the years to come. PEM has extreme explanatory ambition, and profound philosophical implications. Here, I assume the theory, briefly explain it, and then I argue that PEM implies that the brain is essentially self-evidencing. This means it is imperative to identify an evidentiary boundary between the brain (...) and its environment. This boundary defines the mind-world relation, opens the door to skepticism, and makes the mind transpire as more inferentially secluded and neurocentrically skull-bound than many would nowadays think. Therefore, PEM somewhat deflates contemporary hypotheses that cognition is extended, embodied and enactive; however, it can nevertheless accommodate the kinds of cases that fuel these hypotheses. (shrink)

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, (...) we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory. (shrink)

The first paper in this pair (Bickhard in Axiomathes, 2015) developed a model of the nature of representation and cognition, and argued for a model of the micro-functioning of the brain on the basis of that model. In this sequel paper, starting with part III, this model is extended to address macro-functioning in the CNS. In part IV, I offer a discussion of an approach to brain functioning that has some similarities with, as well as differences from, the model presented (...) here: sometimes called the Predictive Brain approach. (shrink)

There is an ongoing controversy in philosophy about the connection between explanation and inference. According to Bayesians, explanatory considerations should be given weight in determining which inferences to make, if at all, only insofar as doing so is compatible with Strict Conditionalization. Explanationists, on the other hand, hold that explanatory considerations can be relevant to the question of how much confidence to invest in our hypotheses in ways which violate Strict Conditionalization. The controversy has focused on normative issues. This paper (...) investigates experimentally the descriptive question of whether judgments of the explanatory goodness of hypotheses do play a role when people revise their degrees of belief in those hypotheses upon the receipt of new evidence. We present the results of three experiments that together strongly support the predictive superiority of the explanationist position. (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)

In important ways, Clark's (HPM) approach parallels the research agenda we have been pursuing. Nevertheless, we remain unconvinced that the HPM offers the best clue yet to the shape of a unified science of mind and action. The apparent convergence of research interests is offset by a profound divergence of theoretical starting points and ideal goals.

Three challenges to a unified understanding of delusions emerge from Radden's On Delusion (2011). Here, I propose that in order to respond to these challenges, and to work towards a unifying framework for delusions, we should see delusions as arising in inference under uncertainty. This proposal is based on the observation that delusions in key respects are surprisingly like perceptual illusions, and it is developed further by focusing particularly on individual differences in uncertainty expectations.

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian (...) models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

The distinction between top-down and bottom-up effects is widely relied on in experimental psychology. However, there is an important problem with the way it is normally defined. Top-down effects are effects of previously-stored information on processing the current input. But on the face of it that includes the information that is implicit in the operation of any psychological process – in its dispositions to transition from some types of representational state to others. This paper suggests a way to distinguish information (...) stored in that way from the kind of influence of prior information that psychologists are concerned to classify as a top-down effect. So-drawn, the distinction is not just of service to theoretical psychology. Asking about the extent of top-down processing is one way to pose some of the questions at issue in philosophical debates about cognitive penetrability – about the extent of the influence of cognitive states on perception. The existence of a theoretically-useful perception-cognition distinction has come under pressure, but even if it has to be abandoned, some of the concerns addressed in the cognitive penetrability literature can be recaptured by asking about the extent of top-down influences on any given psychological process. That formulation is more general, since it can be applied to any psychological process, not just those that are paradigmatically perceptual. (shrink)

Autism spectrum disorder is usually understood through deficits in social interaction and communication, repetitive patterns of behavior, and hyper- or hyporeactivity to sensory input. Affordance-based Skilled Intentionality that combines ecological-enactive views of cognition with Free Energy and Predictive Processing was proposed as the framework from which to view autism integrally. Skilled Intentionality distinguishes between a landscape of affordances and a field of affordances. Under the integrative Skilled Intentionality Framework, it can be shown that autistic differences in the field of affordances (...) stem from aberrant precision estimation. Autistics over-rely on the precision afforded by the environment—a stable econiche they build. According to this approach, autism is understood as characterized by an atypical field of affordances. I will build on the ecological-enactive account of autism to suggest that one way to shape the neurotypical landscape of affordances in accordance with autistic needs is through the use of Ambient Smart Environments (ASEs). Taking the cue from autistic lived experience, ASEs could help minimize environmental uncertainty and afford affective scaffolding by supporting dynamic and flexible niche construction in accordance with individual autistic styles. (shrink)