Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to (...) adaptive success. This target article critically examines this approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency. (shrink)

Biological brains are increasingly cast as ‘prediction machines’: evolved organs whose core operating principle is to learn about the world by trying to predict their own patterns of sensory stimulation. This, some argue, should lead us to embrace a brain-bound ‘neurocentric’ vision of the mind. The mind, such views suggest, consists entirely in the skull-bound activity of the predictive brain. In this paper I reject the inference from predictive brains to skull-bound minds. Predictive brains, I hope to show, can be (...) apt participants in larger cognitive circuits. The path is thus cleared for a new synthesis in which predictive brains act as entry-points for ‘extended minds’, and embodiment and action contribute constitutively to knowing contact with the world. (shrink)

As Ruben notes, the macrostrategy can allow that the distinction may also be drawn at some micro level, but it insists that descent to the micro level is ...

The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). (...) The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between “Pearl blankets” to refer to the original epistemic use of Markov blankets and “Friston blankets” to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programmes: “inference with a model” and “inference within a model.” Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone. (shrink)

Predictive processing approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered (...) under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of ‘body-representations’, and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal ‘representations’ arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories by which to guide discovery through the formal modelling of the embodied mind. (shrink)

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

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. (shrink)

In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. -/- In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the (...) syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module. (shrink)

Drawing on insights from causal theories of reference, teleosemantics, and state space semantics, a theory of naturalized mental representation. In A Mark of the Mental, Karen Neander considers the representational power of mental states—described by the cognitive scientist Zenon Pylyshyn as the “second hardest puzzle” of philosophy of mind. The puzzle at the heart of the book is sometimes called “the problem of mental content,” “Brentano's problem,” or “the problem of intentionality.” Its motivating mystery is how neurobiological states can have (...) semantic properties such as meaning or reference. Neander proposes a naturalistic account for sensory-perceptual representations. Neander draws on insights from state-space semantics, causal theories of reference, and teleosemantic theories. She proposes and defends an intuitive, theoretically well-motivated but highly controversial thesis: sensory-perceptual systems have the function to produce inner state changes that are the analogs of as well as caused by their referents. Neander shows that the three main elements—functions, causal-information relations, and relations of second-order similarity—complement rather than conflict with each other. After developing an argument for teleosemantics by examining the nature of explanation in the mind and brain sciences, she develops a theory of mental content and defends it against six main content-determinacy challenges to a naturalized semantics. (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)

I identify three lessons from Kenneth Craik’s landmark book “The Nature of Explanation” for contemporary debates surrounding the existence, extent, and nature of mental representation: first, an account of mental representations as neural structures that function analogously to public models; second, an appreciation of prediction as the central component of intelligence in demand of such models; and third, a metaphor for understanding the brain as an engineer, not a scientist. I then relate these insights to discussions surrounding the representational status (...) of predictive processing – which, I argue, provides a contemporary vindication of Craik’s extremely prescient “hypothesis on the nature of thought.”. (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)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

Paweł Gładziejewski has recently argued that the framework of predictive processing postulates genuine representations. His focus is on establishing that certain structures posited by PP actually play a representational role. The goal of this paper is to promote this discussion by exploring the contents of representations posited by PP. Gładziejewski already points out that structural theories of representational content can successfully be applied to PP. Here, I propose to make the treatment slightly more rigorous by invoking Francis Egan’s distinction between (...) mathematical and cognitive contents. Applying this distinction to representational contents in PP, I first show that cognitive contents in PP are determined by mathematical contents, at least in the sense that computational descriptions in PP put constraints on ascriptions of cognitive contents. After that, I explore to what extent these constraints are specific. I argue that the general mathematical contents posited by PP do not constrain ascriptions of cognitive content in a specific way. However, there are at least three aspects of PP that constrain ascriptions of cognitive contents in more specific ways: formal PP models posit specific mathematical contents that define more specific constraints; PP entails claims about how computational mechanisms underpin cognitive phenomena ; the processing hierarchy posited by PP goes along with more specific constraints. (shrink)

This paper explores a novel form of Mental Fictionalism: Fictionalism about talk of neural representations in cognitive science. This type of Fictionalism promises to (i) avoid the hard problem of naturalising representations, without (ii) incurring the high costs of eliminating useful representation talk. In this paper, I motivate and articulate this form of Fictionalism, and show that, despite its apparent advantages, it faces two serious objections. These objections are: (1) Fictionalism about talk of neural representations ultimately does not avoid the (...) problem of naturalising representations; (2) Fictional representations cannot play the explanatory role required by cognitive science. (shrink)

The ‘received view’ about computation is that all computations must involve representational content. Egan and Piccinini argue against the received view. In this paper, I focus on Egan’s arguments, claiming that they fall short of establishing that computations do not involve representational content. I provide positive arguments explaining why computation has to involve representational content, and how that representational content may be of any type. I also argue that there is no need for computational psychology to be individualistic. Finally, I (...) draw out a number of consequences for computational individuation, proposing necessary conditions on computational identity and necessary and sufficient conditions on computational I/O equivalence of physical systems.Keywords: Computation; Representation; Computational identity; Explanation; Narrow content; Physical computation. (shrink)

Although predictive coding may offer a computational principle that unifies perception and action, states with different directions of fit are involved (with indicative and imperative contents, respectively). Predictive states are adjusted to fit the world in the course of perception, but in the case of action, the corresponding states act as a fixed target towards which the agent adjusts the world. This well-recognised distinction helps side-step some problems discussed in the target article.

An interesting feature of some sets of representations is that their structure mirrors the structure of the items they represent. Founding an account of representational content on isomorphism, homomorphism or structural resemblance has proven elusive, however, largely because these relations are too liberal when the candidate structure over representational vehicles is unconstrained. Furthermore, in many cases where there is a clear isomorphism, it is not relied on in the way the representations are used. That points to a potential resolution: that (...) an isomorphism must be used, hence usable, if it is to be an ingredient in a theory of content. This paper argues that the class of exploitable isomorphisms can indeed play a content-constituting role. (shrink)

The paper presents an extended argument for the claim that mental content impacts the computational individuation of a cognitive system (section 2). The argument starts with the observation that a cognitive system may simultaneously implement a variety of different syntactic structures, but that the computational identity of a cognitive system is given by only one of these implemented syntactic structures. It is then asked what are the features that determine which of implemented syntactic structures is the computational structure of the (...) system, and it is contended that these features are certain aspects of mental content. The argument helps (section 3) to reassess the thesis known as computational externalism, namely, the thesis that computational theories of cognition make essential reference to features in the individual's environment. It is suggested that the familiar arguments for computational externalism?which rest on thought experiments and on exegesis of Marr's theories of vision?are unconvincing, but that they can be improved. A reconstruction of the visex/audex thought experiment is offered in section 3.1. An outline of a novel interpretation of Marr's theories of vision is presented in section 3.2. The corrected arguments support the claim that computational theories of cognition are intentional. Computational externalism is still pending, however, upon the thesis that psychological content is extrinsic. (shrink)

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

The concept of “representation” is used broadly and uncontroversially throughout neuroscience, in contrast to its highly controversial status within the philosophy of mind and cognitive science. In this paper I first discuss the way that the term is used within neuroscience, in particular describing the strategies by which representations are characterized empirically. I then relate the concept of representation within neuroscience to one that has developed within the field of machine learning. I argue that the recent success of artificial neural (...) networks on certain tasks such as visual object recognition reflects the degree to which those systems exhibit inherent inductive biases that reflect the structure of the physical world. I further argue that any system that is going to behave intelligently in the world must contain representations that reflect the structure of the world; otherwise, the system must perform unconstrained function approximation which is destined to fail due to the curse of dimensionality, in which the number of possible states of the world grows exponentially with the number of dimensions in the space of possible inputs. An analysis of these concepts in light of philosophical debates regarding the ontological status of representations suggests that the representations identified within both biological and artificial neural networks qualify as legitimate representations in the philosophical sense. (shrink)

The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational (...) states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective. (shrink)

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the (...) ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future. (shrink)

In Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena, Jun Tani sets out to answer an essential and tantalizing question: How do our minds work? By providing an overview of his "synthetic neurorobotics" project, Tani reveals how symbols and concepts that represent the world can emerge in a neurodynamic structure--iterative interactions between the top-down subjective view, which proactively acts on the world, and the bottom-up recognition of the resultant perceptual reality. He argues that nontrivial problems of consciousness (...) and free will could be addressed through structural understanding of such iterative, conflicting interactions between the top-down and the bottom-up pathways. A wide range of readers will enjoy this wonderful journey of the mind and will follow the author on interdisciplinary discussions that span neuroscience, dynamical systems theories, robotics, and phenomenology. The book also includes many figures, as well as a link to videos of Tani's exciting robotic experiments. (shrink)

There is a certain excitement in vision science concerning the idea of applying the tools of bayesian decision theory to explain our perceptual capacities. Bayesian models are thought to be needed to explain how the inverse problem of perception is solved, and to rescue a certain constructivist and Kantian way of understanding the perceptual process. Anticlimactically, I argue both that bayesian outlooks do not constitute good solutions to the inverse problem, and that they are not constructivist in nature. In explaining (...) how visual systems derive a single percept from underdetermined stimulation, orthodox versions of bayesian accounts encounter a problem. The problem shows that such accounts need to be grounded in Natural Scene Statistics (NSS), an approach that takes seriously the Gibsonian insight that studying perception involves studying the statistical regularities of the environment in which we are situated. Additionally, I argue that bayesian frameworks postulate structures that hardly rescue a constructivist way of understanding perception. Except for percepts, the posits of bayesian theory are not representational in nature. bayesian perceptual inferences are not genuine inferences. They are biased processes that operate over nonrepresentational states. (shrink)

Some philosophers have offered structural representations as an alternative to indicator-based representations. Motivating these philosophers is the belief that an indication-based analysis of representation exhibits two fatal inadequacies from which structural representations are spared: such an analysis cannot account for the causal role of representational content and cannot explain how representational content can be made determinate. In fact, we argue, indicator and structural representations are on a par with respect to these two problems. This should not be surprising, we contend, (...) given that the distinction between indicator and structural representations is better conceived as one involving degree rather than kind. (shrink)

Many philosophers and psychologists have attempted to elucidate the nature of mental representation by appealing to notions like isomorphism or abstract structural resemblance. The ‘structural representations’ that these theorists champion are said to count as representations by virtue of functioning as internal models of distal systems. In his 2007 book, Representation Reconsidered, William Ramsey endorses the structural conception of mental representation, but uses it to develop a novel argument against representationalism, the widespread view that cognition essentially involves the manipulation of (...) mental representations. Ramsey argues that although theories within the ‘classical’ tradition of cognitive science once posited structural representations, these theories are being superseded by newer theories, within the tradition of connectionism and cognitive neuroscience, which rarely if ever appeal to structural representations. Instead, these theories seem to be explaining cognition by invoking so-called ‘receptor representations’, which, Ramsey claims, aren’t genuine representations at all—despite being called representations, these mechanisms function more as triggers or causal relays than as genuine stand-ins for distal systems. I argue that when the notions of structural and receptor representation are properly explicated, there turns out to be no distinction between them. There only appears to be a distinction between receptor and structural representations because the latter are tacitly conflated with the ‘mental models’ ostensibly involved in offline cognitive processes such as episodic memory and mental imagery. While structural representations might count as genuine representations, they aren’t distinctively mental representations, for they can be found in all sorts of non-intentional systems such as plants. Thus to explain the kinds of offline cognitive capacities that have motivated talk of mental models, we must develop richer conceptions of mental representation than those provided by the notions of structural and receptor representation. (shrink)

Correctly understood, teleosemantics is the claim that “representation” is a function term. Things are called “representations” if they have a certain kind of function or telos and perform it in a certain kind of way. This claim is supported with a discussion and proposals about the function of a representation and of how representations perform that function. These proposals have been retrieved by putting together current descriptions from the literature on neural representations with earlier explorations of the features common to (...) most things we are inclined to call representations as these were assessed in Millikan. Of interest is the degree to which these independent sources converge. I conclude that there is no need to employ any new or technical sense of the term “representation” for it to play an important role in neuroscience. (shrink)

This is Part A of an article that defends non-eliminative structuralism about mathematics by means of a concrete case study: a theory of unlabeled graphs. Part A summarizes the general attractions of non-eliminative structuralism. Afterwards, it motivates an understanding of unlabeled graphs as structures sui generis and develops a corresponding axiomatic theory of unlabeled graphs. As the theory demonstrates, graph theory can be developed consistently without eliminating unlabeled graphs in favour of sets; and the usual structuralist criterion of identity can (...) be applied successfully in graph-theoretic proofs. Part B will turn to the philosophical interpretation and assessment of the theory. (shrink)

A promising strategy for defending the role that representation plays in explanations of cognition frames the concept in terms of internal models or map‐like mechanisms. “Structural representation” offers an account of representation that is grounded in well‐specified, empirical criteria. However, anti‐representationalists continue to press the issue of how to account for the paradigmatic semantic properties of representation at the subpersonal level. In this paper, I offer an account of how the proponent of structural representation should think about content. There are (...) really two problems of content any account of representation must overcome: the “hard problem of content” and the “content determination problem.” I argue that the structural representation account has the resources to provide a naturalistically respectable notion of content that overcomes both problems. (shrink)

This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the Kullback–Leibler (KL)-divergence provides an accessible measure of misrepresentation, and therefore, a measure of representational content in hierarchical Bayesian inference. The paper then argues that while the KL-divergence is a measure of information, it does not establish a sufficient measure of representational content. We argue that this follows from the fact that the KL-divergence is a measure of relative entropy, which can (...) be shown to be the same as covariance (through a set of additional steps). It is well known that facts about covariance do not entail facts about representational content. So there is no reason to think that the KL-divergence is a measure of (mis-)representational content. This paper thus provides an enactive, non-representational account of Bayesian belief optimisation in hierarchical PP. (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)

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)

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)

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One (...) of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights. (shrink)

b>: In this article I outline, apply, and defend a theory of natural representation. The main consequences of this theory are: i) representational status is a matter of how physical entities are used, and specifically is not a matter of causation, nomic relations with the intentional object, or information; ii) there are genuine (brain-)internal representations; iii) such representations are really representations, and not just farcical pseudo-representations, such as attractors, principal components, state-space partitions, or what-have-you;and iv) the theory allows us to (...) sharply distinguish those complex behaviors which are genuinely cognitive from those which are merely complex and adaptive. (shrink)

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)

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)

Despite the fact that the notion of internal representation has - at least according to some - a fundamental role to play in the sciences of the mind, not only has its explanatory utility been under attack for a while now, but it also remains unclear what criteria should an explanation of a given cognitive phenomenon meet to count as a representational explanation in the first place. The aim of this article is to propose a solution to this latter problem. (...) I will assume that representational explanations should be construed as a form of mechanistic explanations and proceed by proposing a general sketch of a functional architecture of a representational cognitive mechanism. According to the view on offer here, representational mechanisms are mechanisms that meet four conditions: the structural resemblance condition, the action-guidance condition, the decouplability condition, and the error-detection condition. (shrink)

Why do brains have so many connections? The principles exposed by Andy Clark provide answers to questions like this by appealing to the notion that brains distil causal regularities in the sensorium and embody them in models of their world. For example, connections embody the fact that causes have particular consequences. This commentary considers the imperatives for this form of embodiment.

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, (...) the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. (shrink)

In this paper I argue that, by combining eliminativist and fictionalist approaches toward the sub-personal representational posits of predictive processing, we arrive at an empirically robust and yet metaphysically innocuous cognitive scientific framework. I begin the paper by providing a non-representational account of the five key posits of predictive processing. Then, I motivate a fictionalist approach toward the remaining indispensable representational posits of predictive processing, and explain how representation can play an epistemologically indispensable role within predictive processing explanations without thereby (...) requiring that representation metaphysically exists. Finally, I outline four consequences of accepting this approach and explain why they are beneficial: we arrive at a victory for metaphysical eliminativism in the ‘representation wars’; my account fits with extant empirical practice; my account provides guidance for future research; and, my account provides the beginnings of a response to Mark Sprevak’s IBE problem for fictionalist approaches toward sub-personal representation. (shrink)

The proposal that probabilistic inference and unconscious hypothesis testing are central to information processing in the brain has been steadily gaining ground in cognitive neuroscience and associated fields. One popular version of this proposal is the new theoretical framework of predictive processing or prediction error minimization, which couples unconscious hypothesis testing with the idea of ‘active inference’ and claims to offer a unified account of perception and action. Here we will consider one outstanding issue that still looms large at the (...) core of the PEM framework: the lack of a clear criterion for distinguishing conscious states from unconscious ones. In order to fulfill the promise of becoming a unifying framework for describing and modeling cognition, PEM needs to be able to differentiate between conscious and unconscious mental states or processes. We will argue that one currently popular view, that the contents of conscious experience are determined by the ‘winning hypothesis’, falls short of fully accounting for conscious experience. It ignores the possibility that some states of a system can control that system’s behavior even though they are apparently not conscious. What follows from this is that the ‘winning hypothesis’ view does not provide a complete account of the difference between conscious and unconscious states in the probabilistic brain. We show how this problem for the received view can be resolved by augmenting PEM with Daniel Dennett’s multiple drafts model of consciousness. This move is warranted by the similar roles that attention and internal competition play in both the PEM framework and the multiple drafts model. (shrink)

How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics.In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to (...) be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation. (shrink)

In _ Plato's Camera_, eminent philosopher Paul Churchland offers a novel account of how the brain constructs a representation -- or "takes a picture" -- of the universe's timeless categorical and dynamical structure. This construction process, which begins at birth, yields the enduring background conceptual framework with which we will interpret our sensory experience for the rest of our lives. But, as even Plato knew, to make singular perceptual judgments requires that we possess an antecedent framework of abstract categories to (...) which any perceived particular can be relevantly assimilated. How that background framework is assembled in the first place is the motivating mystery, and the primary target, of Churchland's book. Unexpectedly, this neurobiologically grounded account of human cognition also provides a systematic story of how such low-level epistemological activities are integrated within an enveloping framework of linguistic structures and regulatory mechanisms at the social level. As Churchland illustrates, this integration of cognitive mechanisms at several levels has launched the human race on an epistemological adventure denied to all other terrestrial creatures. (shrink)