Perception is riddled with cases of reliable misperception. These are cases in which a perceptual state is tokened inaccurately any time it is tokened under normal conditions. On the face of it, this fact causes trouble for theories that provide an analysis of perceptual content in non-semantic, non-intentional, and non-phenomenal terms, such as those found in Millikan (1984), Fodor (1990), Neander (2017), and Schellenberg (2018). I show how such theories can be extended so that they cover such cases without giving (...) up their core commitments. (shrink)

This article outlines the motivations and main findings of Favela and Machery's “Investigating the concept of representation in the neural and psychological sciences”, and discusses what to do with the concept of representation in the brain sciences moving forward.

Joint attention occurs when two (or more) individuals attend together to some object. It has been identified by psychologists as an early form of our joint engagement, and is thought to provide us with an understanding of other minds that is basic in that sophisticated conceptual resources are not involved. Accordingly, it has also attracted the interest of philosophers. Moreover, a very recent trend in the psychological and philosophical literature on joint attention consists of developing the suggestion that it holds (...) partially in virtue of communication: it is because we share our thoughts or feelings about an object that our individual attention becomes joint. This paper unpacks the communicative suggestion in a way that accounts for joint attention's basicness. (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)

Functional neuroimaging is sometimes criticized as showing only where in the brain things happen, not how they happen, and thus being unable to inform us about questions of mental and neural representation. Novel analytical methods increasingly make clear that imaging can give us access to constructs of interest to psychology. In this paper I argue that neuroimaging can give us an important, if limited, window into the large-scale structure of neural representation. I describe Representational Similarity Analysis, increasingly used in neuroimaging (...) studies, and lay out desiderata for representations in general. In that context I discuss what RSA can and cannot tell us about neural representation. I compare RSA with fMRI to a different experimental paradigm which has been embraced as being indicative of representation in psychology, and argue that it compares favorably. (shrink)

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...) the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

This book concerns the nature and the norms of inquiry. It tackles not only philosophical issues regarding what inquiry is, but also issues regarding how it should and should not be executed. Roughly put, inquiry is the activity of searching for the true answers to questions of our interest. But what is the difference between empirical and armchair inquiry? And what are the right and the wrong ways to inquire? Under what conditions should one start inquiring? Which questions are such (...) that one should not inquire into them? The book offers answers to these questions. It argues that competent armchair inquiry only makes explicit what was already implicit--the inquirer already had the answer to her question before inquiring into it, though this was not explicit to her. It also argues that we should avoid inquiring into questions whose answers are unknowable to us, in the instrumental sense of 'should', and that different modes of inquiry are called for, depending on which type of information is available to the subject. These answers are rigorously argued for, and they stem from a unified framework for modeling the activity of inquiry. (shrink)

Favela and Machery conclude from their studies that neuroscientists' and psychologists' concept of representation is both unclear and confused. Rather than advocating reform or elimination of the concept, they suggest that it can serve various theoretical purposes precisely because it is unclear and confused. I challenge their claim that the concept of representation, as used by neuroscientists and psychologists, is unclear and confused, and I propose an alternative explanation of why it might appear to be so.

To account for the explanatory role representations play in cognitive science, Egan’s deflationary account introduces a distinction between cognitive and mathematical contents. According to that account, only the latter are genuine explanatory posits of cognitive-scientific theories, as they represent the arguments and values cognitive devices need to represent to compute. Here, I argue that the deflationary account suffers from two important problems, whose roots trace back to the introduction of mathematical contents. First, I will argue that mathematical contents do not (...) satisfy important and widely accepted desiderata all theories of content are called to satisfy, such as content determinacy and naturalism. Secondly, I will claim that there are cases in which mathematical contents cannot play the explanatory role the deflationary account claims they play, proposing an empirical counterexample. Lastly, I will conclude the paper highlighting two important implications of my arguments, concerning recent theoretical proposals to naturalize representations via physical computation, and the popular predictive processing theory of cognition. (shrink)

Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts to connect the mind with computation, (...) including (a) various enterprises at modeling features of the mind using computational modeling techniques, and (b) employing some feature or features of production-model computers (such as the stored program concept, or the distinction between hardware and software) merely as a guiding metaphor for understanding some feature of the mind. This entry is therefore concerned solely with the Computational Theory of Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses of ‘computer’ and ‘computation’ employed here are technical; the main tasks of this entry will therefore be to elucidate: (a) the technical sense of ‘computation’ that is at issue, (b) the ways in which it is claimed to be applicable to the mind, (c) the philosophical problems this understanding of the mind is claimed to solve, and (d) the major criticisms that have accrued to this view. (shrink)

In order to understand cognition, we often recruit analogies as building blocks of theories to aid us in this quest. One such attempt, originating in folklore and alchemy, is the homunculus: a miniature human who resides in the skull and performs cognition. Perhaps surprisingly, this appears indistinguishable from the implicit proposal of many neurocognitive theories, including that of the 'cognitive map,' which proposes a representational substrate for episodic memories and navigational capacities. In such 'small cakes' cases, neurocognitive representations are assumed (...) to be meaningful and about the world, though it is wholly unclear who is reading them, how they are interpreted, and how they come to mean what they do. We analyze the 'small cakes' problem in neurocognitive theories (including, but not limited to, the cognitive map) and find that such an approach a) causes infinite regress in the explanatory chain, requiring a human-in-the-loop to resolve, and b) results in a computationally inert account of representation, providing neither a function nor a mechanism. We caution against a 'small cakes' theoretical practice across computational cognitive modelling, neuroscience, and artificial intelligence, wherein the scientist inserts their (or other humans') cognition into models because otherwise the models neither perform as advertised, nor mean what they are purported to, without said 'cake insertion.' We argue that the solution is to tease apart explanandum and explanans for a given scientific investigation, with an eye towards avoiding van Rooij's (formal) or Ryle's (informal) infinite regresses. (shrink)

Structural representations are likely the most talked about representational posits in the contemporary debate over cognitive representations. Indeed, the debate surrounding them is so vast virtually every claim about them has been made. Some, for instance, claimed structural representations are different from indicators. Others argued they are the same. Some claimed structural representations mesh perfectly with mechanistic explanations, others argued they can’t in principle mash. Some claimed structural representations are central to predictive processing accounts of cognition, others rebuked predictive processing (...) networks are blissfully structural representation free. And so forth. Here, I suggest this confusing state of affairs is due to the fact that the term “structural representations” is applied to a number of distinct conceptions of representations. In this paper, I distinguish four such conceptions, argue that these four conceptions are actually distinct, and then show that such a fourfold distinction can be used to clarify some of the most pressing questions concerning structural representations and their role in cognitive theorizing, making these questions more easily answerable. (shrink)

I present considerations surrounding pre-reflective self-consciousness, arising in work I am conducting on a new physicalist, process-based account of [phenomenal] consciousness. The account is called the meta-causal account because it identifies consciousness with a certain type of arrangement of meta-causation. Meta-causation is causation where a cause or effect is itself an instance of causation. The proposed type of arrangement involves a sort of time-spanning, internal reflexivity of the overall meta-causation. I argue that, as a result of the account, any conscious (...) process has PRSC. Hence, PRSC does not need to be taken as a stipulation or argued for on purely phenomenological grounds or as a necessary support for reflective consciousness. I also show how it is natural to the account that PRSC is not an additional, peripheral, sort of consciousness, but is intrinsic to all consciousness, thereby fitting claims about self-intimation and co-constitution by various authors, and also being amenable to an adverbial account. As part of this, consciousness of an external object is just the form that current self-consciousness takes, the meta-causal constitution of it being inextricably modulated by the causal relationship with the object. The article also discusses how MCA helps explore issues of for-me-ness, transparency of perceptual consciousness, and possible immediacy and non-relationality of self-consciousness. (shrink)

Teleosemantics has an indeterminacy problem. In an earlier publication, I argued that teleosemanticists may afford to be realists about indeterminacy, pointing to the phenomenon of vagueness as a case of really-existing semantic indeterminacy. Here, I continue that project by proposing two criteria of adequacy that a semantically indeterminate theory should meet: a criterion of theoretical adequacy and a criterion of extensional adequacy. I present reasons to think that indeterminate versions of teleosemantics can meet these criteria. I end by discussing vagueness, (...) concluding that it most likely is not the same kind of phenomenon as the semantic indeterminacy afflicting teleosemantics. (shrink)

This chapter argues that the only tenable unconscious inferences theories of cognitive achievement are ones that employ a theory internal technical notion of representation, but that once we give cash-value definitions of the relevant notions of representation and inference, there is little left of the ordinary notion of representation. We suggest that the real value of talk of unconscious inferences lies in (a) their heuristic utility in helping us to make fruitful predictions, such as about illusions, and (b) their providing (...) a high-level description of the functional organization of subpersonal faculties that makes clear how they equip an agent to navigate its environment and pursue its goals. (shrink)

According to the semantic view of computation, computations cannot be individuated without invoking semantic properties. A traditional argument for the semantic view is what we shall refer to as the argument from the cognitive science practice. In its general form, this argument rests on the idea that, since cognitive scientists describe computations (in explanations and theories) in semantic terms, computations are individuated semantically. Although commonly invoked in the computational literature, the argument from the cognitive science practice has never been discussed (...) in detail. In this paper, we shall provide a critical reconstruction of this argument and an extensive analysis of its prospects, taking into account some ways of defending it that have never been explored so far. We shall argue that explanatory considerations support at best a weak version of the argument from the cognitive science practice, according to which semantic properties concur with formal syntactic properties in individuating computations in cognitive science, but not a strong version, according to which computation individuation in cognitive science is semantic as opposed to formal syntactic. (shrink)

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 predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

Can philosophical theories of perception defer to perceptual science when fixing their ontological commitments regarding the objects of perception? Or in other words, can perceptual science inform us about the nature of perception? Many contemporary mainstream philosophers of perception answer affirmatively. However, in this essay I provide two arguments against this idea. On the one hand, I will argue that perceptual science is not committed to certain assumptions, relevant for determining perceptual ontology, which however are generally relied upon by philosophers (...) when interpreting such science. On the other hand, I will show how perceptual science often relies on another assumption, which I call the ‘Measuring instrument conception’ of sensory systems, which philosophers of perception should clearly reject. Given these two symmetric lines of argument, I will finally suggest that we ought to think differently about the relationship between perceptual science and the philosophy of perception. (shrink)

This work aims to contribute to the explanation of cognitive capacities that are essential to human intelligence: commonsense and situation holism. The attempt to explain them within the field of cognitive sciences raises a foundational challenge. How can human cognition distinguish what’s relevant and what’s not in an open-ended set of contexts? The challenge is characterized by a circularity. Potential solutions end up relying on the very capacity that they should be explaining, i.e. the sensitivity to what’s contextually relevant. The (...) argument here developed is an attempt to get out of this circularity. It is grounded on a bi-dimensional analysis of the problem. The first one is representational and is about how cognitive knowledge on what’s relevant can be stored. This dimension is related to the frame problem from artificial intelligence. I claim that this representational aspect of the issue can be neutralized by abandoning language-like representational schemes and sticking to a plurality of structural ones. They allow representational states to play a clear and non-trivial explanatory role while enabling a kind of representational productivity that is not bothered by the frame problem. The second dimension is the inferential one. It encompasses our relevance-sensitive inferential productivity, i.e. our capacity to efficiently infer what’s relevant in indefinitely many contexts, including ones we’ve never faced before. I argue that such relevance sensitivity cannot be neither innate, for innate mechanisms result in the aforementioned circularity, nor learned through innate mechanisms, for learning processes themselves rely on relevance sensitivity. The suggested alternative is that relevance sensitivity is a cognitive gadget. A kind of mental mechanism that is culturally, rather than genetically, inherited. This possibility is rendered plausible within Cecilia Heyes’ “cognitive gadgets” framework. She shows how developmental tweaks and biases culturally established can participate in the constitution of cognitive mechanisms. Thus, cultural elements can participate in human cognition not only as information sources, but also in the form of tweaks that bias the processing of that information towards what’s culturally established as contextually relevant. (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)

This dissertation focuses on generative models in the Predictive Processing framework. It is commonly accepted that generative models are structural representations; i.e. physical particulars representing via structural similarity. Here, I argue this widespread account is wrong: when closely scrutinized, generative models appear to be non-representational control structures realizing an agent’s sensorimotor skills. The dissertation opens (Ch.1) introducing the Predictive Processing account of perception and action, and presenting some of its connectionist implementations, thereby clarifying the role generative models play in Predictive (...) Processing. Subsequently, I introduce the conceptual framework guiding the research (ch.2). I briefly elucidate the metaphysics of representations, emphasizing the specific functional role played by representational vehicles within the systems of which they are part. I close the first half of the dissertation (Ch.3) introducing the claim that generative models are structural representations, and defending it from intuitive but inconclusive objections. I then move to the second half of the dissertation, switching from exposition to criticism. First (Ch.4), I claim that the argument allegedly establishing that generative models are structural representations is flawed beyond repair, for it fails to establish generative models are structurally similar to their targets. I then consider alternative ways to establish that structural similarity, showing they all either fail or violate some other condition individuating structural representations. I further argue (Ch.5) that the claim that generative models are structural representations would not be warranted even if the desired structural similarity were established. For, even if generative models were to satisfy the relevant definition of structural representation, it would still be wrong to consider them as representations. This is because, as currently defined,structural representations fail to play the relevant functional role of representations, and thus cannot be rightfully identified as representations in the first place. This conclusion prompts a direct examination of generative models, to determine their nature (Ch.6). I thus analyze the simplest generative model I know of: a neural network functioning as a robotic “brain” and allowing different robotic creatures to swiftly and intelligently interact with their environments. I clarify how these networks allow the robots to acquire and exert the relevant sensorimotor abilities needed to solve the various cognitive tasks the robots are faced with, and then argue that neither the entire architecture nor any of its parts can possibly qualify as representational vehicles. In this way, the structures implementing generative models are revealed to be non-representational structures that instantiate an agent’s relevant sensorimotor skills. I show that my conclusion generalizes beyond the simple example I considered, arguing that adding computational ingredients to the architecture, or considering altogether different implementations of generative models, will in no way force a revision of my verdict. I further consider and allay a number of theoretical worries that it might generate, and then briefly conclude the dissertation. (shrink)

According to radical enactivists, cognitive sciences should abandon the representational framework. Perceptuomotor cognition and action control are often provided as paradigmatic examples of nonrepresentational cognitive phenomena. In this article, we illustrate how motor and action control are studied in research that uses reinforcement learning algorithms. Crucially, this approach can be given a representational interpretation. Hence, reinforcement learning provides a way to explicate action-oriented views of cognitive systems in a representational way.