Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of observation. (...) My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)

The idea that the brain is a representational organ has roots in the nineteenth century, when neurologists began drawing conclusions about what the brain represents from clinical and experimental studies. One of the earliest controversies surrounding representation in the brain was the “muscles versus movements” debate, which concerned whether the motor cortex represents complex movements or rather fractional components of movement. Prominent thinkers weighed in on each side: neurologists John Hughlings Jackson and F.M.R. Walshe in favor of complex movements, neurophysiologist (...) Charles Sherrington and neurosurgeon Wilder Penfield in favor of movement components. This essay examines these and other brain scientists’ evolving notions of representation during the first eighty years of the muscles versus movements debate (c. 1873–1954). Although participants agreed about many of the superficial features of representation, their inferences reveal deep-seated disagreements about its inferential role. Divergent epistemological commitments stoked conflicting conceptions of what representational attributions imply and what evidence supports them. (shrink)

Some philosophers search for the mark of the cognitive: a set of individually necessary and jointly sufficient conditions identifying all instances of cognition. They claim that the mark of the cognitive is needed to steer the development of cognitive science on the right path. Here, I argue that, at least at present, it cannot be provided. First (§2), I identify some of the factors motivating the search for a mark of the cognitive, each yielding a desideratum the mark is supposed (...) to satisfy (§2.1). I then (§2.2) highlight a number of tensions in the literature on the mark of the cognitive, suggesting they’re best resolved by distinguishing two distinct programs. The first program (§3) is that of identifying a mark of the cognitive capturing our everyday notion of cognition. I argue that such a program is bound to fail for a number of reasons: it is not clear whether such an everyday notion exists; and even if it existed, it would not be able to spell out individually necessary and jointly sufficient conditions for cognition; and even if it were able to spell them out, these conditions won’t satisfy the desiderata a mark of the cognitive should satisfy. The second program is that of identifying a mark of the cognitive spelling out a genuine scientific kind. But the current state of fragmentation of cognitive science, and the fact that it is splintered in a myriad of different research traditions, prevent us from identifying such a kind. And we have no reason to think that these various research traditions will converge, allowing us to identify a single mark. Or so, at least, I will argue in (§4). I then conclude the paper (§5) deflecting an intuitive objection, and exploring some of the consequences of the thesis I have defended. (shrink)

In this paper, I develop an empirically-driven approach to the relationship between conceptual and non-conceptual representations. I begin by clarifying Wilfrid Sellars's distinction between a non-conceptual capacity to picture significant aspects of our world, and a capacity to stabilize semantic content in the form of conceptual representations that signify those aspects of the world that are relevant to our shared practices. I argue that this distinction helps to clarify the reason why cognition must be understood as embodied and situated. Drawing (...) on recent models of attention and valuation, I then argue that the human brain constructs a dynamic model of the world that it has encountered, encoding higher-level regularities in the form of linguistically structured representations. And I conclude by arguing that this approach to cognition provides a set of critical resources for understanding the situated nature of social cognition. (shrink)

Recent findings in different areas of psychology and cognitive science have brought the unconscious mind back to center stage. However, the unconscious mind worry remains: What renders unconscious phenomena mental? I suggest a new strategy for answering this question, which rests on the idea that categorizing unconscious phenomena as “mental” should be scientifically useful relative to the explanatory research goals. I argue that this is the case if by categorizing an unconscious phenomenon as “mental” one picks out explanatorily relevant similarities (...) to a corresponding paradigmatically mental conscious phenomenon. Explanatory relevance is spelled out in terms of mechanistic norms. (shrink)

Representation is typically taken to be importantly separate from its physical implementation. This is exemplified in Marr’s three-level framework, widely cited and often adopted in neuroscience. However, the separation between representation and physical implementation is not a necessary feature of information-processing systems. In particular, when it comes to analog computational systems, Marr’s representational/algorithmic level and implementational level collapse into a single level. Insofar as analog computation is a better way of understanding neural computation than other notions, Marr’s three-level framework must (...) then be amended into a two-level framework. However, far from being a problem or limitation, this sheds lights on how to understand physical media as being representational, but without a separate, medium-independent representational level. (shrink)

Double dissociations between perceivable colors and physical properties of colored objects have led many philosophers to endorse relationalist accounts of color. I argue that there are analogous double dissociations between attitudes of belief—the beliefs that people attribute to each other in everyday life—and intrinsic cognitive states of belief—the beliefs that some cognitive scientists posit as cogs in cognitive systems—pitched at every level of psychological explanation. These dissociations provide good reason to refrain from conflating attitudes of belief with intrinsic cognitive states (...) of belief. I suggest that interpretivism provides an attractive account of the former. Like colors, attitudes of belief evolved to be ecological signifiers, not cogs in cognitive systems. (shrink)

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am (...) skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

Many debates in philosophy focus on whether folk or scientific psychological notions pick out cognitive natural kinds. Examples include memory, emotions and concepts. A potentially interesting type of kind is: kinds of mental representations (as opposed, for example, to kinds of psychological faculties). In this chapter we outline a proposal for a theory of representational kinds in cognitive science. We argue that the explanatory role of representational kinds in scientific theories, in conjunction with a mainstream approach to explanation in cognitive (...) science, suggest that representational kinds are multi-level. This is to say that representational kinds’ properties cluster at different levels of explanation and allow for intra- and inter-level projections. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

The concept of mental representation has long been considered to be central concept of philosophy of mind and cognitive science. But not everyone agrees. Neo-behaviorists aim to explain the mind without positing any representations. My aim here is not to assess the merits and demerits of neo-behaviorism, but to take their challenge seriously and ask the question: What justifies the attribution of representations to an agent? Both representationalists and neo-behaviorists tend to take it for granted that the real question about (...) representations is whether we should be realist about the theory of representationalism. This paper is an attempt to shift the emphasis from the debate concerning realism about theories to the one concerning realism about entities. My claim is that regardless of whether we are realist about representational theories of the mind, we have compelling reasons to endorse entity realism about mental representations. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates (...) the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

A leading theoretical framework for naturalistic explanation of mind holds that we explain the mind by positing progressively "stupider" capacities ("homunculi") until the mind is "discharged" by means of capacities that are not intelligent at all. The so-called homuncular fallacy involves violating this procedure by positing the same capacities at subpersonal levels. I argue that the homuncular fallacy is not a fallacy, and that modern-day homunculi are idle posits. I propose an alternative view of what naturalism requires that reflects how (...) the cognitive sciences are actually integrating mind and matter. (shrink)

Teresa believes in God. Maggie’s wife believes that the Earth is flat, and also that Maggie should be home from work by now. Anouk—a cat—believes it is dinner time. This dissertation is about what believing is: it concerns what, exactly, ordinary people are attributing to Teresa, Maggie’s wife, and Anouk when affirming that they are believers. Part I distinguishes the attitudes of belief that people attribute to each other (and other animals) in ordinary life from the cognitive states of belief (...) theoretically posited by (some) cognitive scientists. Part II defends the view that to have an attitude of belief is to live—to be disposed to act, react, think, and feel—in a pattern that an actual belief attributor identifies with taking the world to be some way. Drawing on scientific, scholarly, and literary sources of evidence, How Beliefs are like Colors provides a framework for research on belief across the humanities and sciences of the mind. (shrink)

This paper introduces the Cummins Functions Approach to neural representations, which aims to capture the notion of representation that is relevant to contemporary neuroscientific practice. CFA shares the common view that “to be a representation of X” amounts to “having the function of tracking X,” but maintains that the relevant notion of function is defined by Robert Cummins’s account. Thus, CFA offers a notion of neural representation that is dependent on explanatory context. I argue that CFA can account for the (...) normativity of neural representations, and defend its dependence on explanations. (shrink)

Do perceptual experiences always inherit the content of their neural correlates? Most scientists and philosophers working on perception say ‘yes’. They hold the view that an experience’s content just is (i.e. is identical to) the content of its neural correlate. This paper presses back against this view, while trying to retain as much of its spirit as possible. The paper argues that type-2 blindsight experiences are plausible cases of experiences which lack the content of their neural correlates. They are not (...) experiences of the stimuli or stimulus properties prompting them, but their neural correlates represent these stimulus properties. The argument doesn’t depend on any special view of what it is for an experience to be of a stimulus or stimulus property. The upshot is that, even assuming there is a deep relationship between experiential content and neural content, that relationship is more complex than simple identity. (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 teleosemantic theory of representational content is held by some philosophers to imply that genes carry semantic information about whole-organism phenotypes. In this paper, I argue that this position is not supported by empirical findings. I focus on one of the most elaborate defenses of this position: Shea’s view that genes represent whole-organism phenotypes. I distinguish between two ways of individuating genes in contemporary biological science as possible vehicles of representational content—as molecular genes and as difference-maker genes. I show that (...) given either of these ways of individuating genes, genes fail to meet conditions which the teleosemantic theory requires an entity to meet if that entity is to qualify as a representational vehicle that represents a whole-organism phenotype. The considerations I present against Shea’s view generalize to other attempts to use the teleosemantic theory in support of the claim that genes represent whole-organism phenotypes. (shrink)

This paper proposes a model for implementation of intrinsic natural language sentence meaning in a physical language understanding system, where 'intrinsic' is understood as 'independent of meaning ascription by system-external observers'. The proposal is that intrinsic meaning can be implemented as a point attractor in the state space of a nonlinear dynamical system with feedback which is generated by temporally sequenced inputs. It is motivated by John Searle's well known (Behavioral and Brain Sciences, 3: 417–57, 1980) critique of the then-standard (...) and currently still influential computational theory of mind (CTM), the essence of which was that CTM representations lack intrinsic meaning because that meaning is dependent on ascription by an observer_._ The proposed dynamical model comprises a collection of interacting artificial neural networks, and constitutes a radical simplification of the principle of compositional phrase structure which is at the heart of the current standard view of sentence semantics because it is computationally interpretable as a finite state machine. (shrink)

The fundamental cognitive problem for active organisms is to decide what to do next in a changing environment. In this article, we analyze motor and action control in computational models that utilize reinforcement learning (RL) algorithms. In reinforcement learning, action control is governed by an action selection policy that maximizes the expected future reward in light of a predictive world model. In this paper we argue that RL provides a way to explicate the so-called action-oriented views of cognitive systems in (...) representational terms. (shrink)

A novel account of semantic information is proposed. The gist is that structural correspondence, analysed in terms of similarity, underlies an important kind of semantic information. In contrast to extant accounts of semantic information, it does not rely on correlation, covariation, causation, natural laws, or logical inference. Instead, it relies on structural similarity, defined in terms of correspondence between classifications of tokens into types. This account elucidates many existing uses of the notion of information, for example, in the context of (...) scientific models and structural representations in cognitive science. It is poised to open a new research program concerned with various kinds of semantic information, its functions, and its measurement. (shrink)

Here, I take alethic views of understanding to be all views that hold that whether an explanation is true or false matters for whether that explanation provides understanding. I then argue that there is (as yet) no naturalistic defence of alethic views of understanding in cognitive science, because there is no agreement about the correct descriptions of the content of cognitive scientific explanations. I use this claim to argue for the provisional acceptance of afactivism in cognitive science, which is the (...) view that the truth or falsity of an explanation of cognition is irrelevant to whether that explanation provides understanding. I conclude by discussing the relation between understanding in cognitive science and understanding in other domains. (shrink)

Representationalists and anti-representationalists disagree about whether a naturalisation of mental content is possible and, hence, whether positing mental representations in cognitive science is justified. Here, I develop a novel way to think about mental representations based on a philosophical description of (cognitive) science inspired by cognitive instrumentalism. On this view, our acceptance of theories positing mental representations and our beliefs in (something like) mental representations do not depend on the naturalisation of content. Thus, I conclude that if we endorse cognitive (...) instrumentalism about mental representations, then we can finally leave the dispute between representationalism and anti-representationalism behind. (shrink)

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to (...) neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience. (shrink)

Recent developments in the literature suggest cognitive representation can be conceived of as a kind of mechanism that meets the functional profile set out by the S-representation account. However, this approach is threatened by worries that the S-representation account cannot tell a satisfactory story about content determination at the subpersonal level. One solution is to complement the S-representation account with a traditional etiological theory of content determination. This paper argues such a move is unwarranted and threatens the broader project of (...) understanding representation in mechanism-friendly terms. Once properly situated within a mechanistic framework, the S-representation account can address the issue of content determination without supplementing it with an etiological theory. In turn, this strengthens the notion that combing a mechanistic framework with the S-representation account provides a robust and comprehensive defense of representation’s positive role in cognitive science. (shrink)

The concept of mental representation has long been considered to be central concept of philosophy of mind and cognitive science. But not everyone agrees. Neo-behaviorists aim to explain the mind (or some subset thereof) without positing any representations. My aim here is not to assess the merits and demerits of neo-behaviorism, but to take their challenge seriously and ask the question: What justifies the attribution of representations to an agent? Both representationalists and neo-behaviorists tend to take it for granted that (...) the real question about representations is whether we should be realist about the theory of representationalism. This paper is an attempt to shift the emphasis from the debate concerning realism about theories to the one concerning realism about entities. My claim is that regardless of whether we are realist about representational theories of the mind, we have compelling reasons to endorse entity realism about mental representations. (shrink)

Volume 33, Issue 8, November 2020, Page 1096-1120.

In the course of investigating the living world, biologists regularly attribute semantic content to the phenomena they study. In this paper, I examine the case of a contemporary research program studying the navigation behaviors of ants and develop an account of the norms governing researchers’ ascriptions of semantic content in their research practices. The account holds that researchers assign semantic content to behaviors that reliably achieve a difficult goal-directed function, and it also suggests a productive role for attributions of semantic (...) content in the process of animal behavior research. (shrink)