In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

Computationalism about the brain is the view that the brain literally performs computations. For the view to be interesting, we need an account of computation. The most well-developed account of computation is Turing Machine computation, the account provided by theoretical computer science which provides the basis for contemporary digital computers. Some have thought that, given the seemingly-close analogy between the all-or-nothing nature of neural spikes in brains and the binary nature of digital logic, neural computation could be a species of (...) digital computation. A few recent authors have offered arguments against this idea; here, I review recent findings in neuroscience that further cement the implausibility of this view. However, I argue that we can retain the view that the brain is a computer if we expand what we mean by “computation” to include analog computation. I articulate an account of analog computation as the manipulation of analog representations based on previous work on the difference between analog and non-analog representations, extending a view originally articulated in Shagrir :271–279, 2010). Given that analog computation constitutes a significant chapter in the history of computation, this revision of computationalism to include analog computation is not an ad hoc addition. Brains may well be computers, but of the analog kind, rather than the digital kind. (shrink)

Current orthodoxy takes representation to be essential to computation. However, a philosophical account of computation that does not appeal to representation would be useful, given the difficulties involved in successfully theorizing representation. Piccinini's recent mechanistic account of computation proposes to do just that: it couches computation in terms of what certain mechanisms do without requiring the manipulation or processing of representations whatsoever (Piccinini 2015). Most crucially, mechanisms must process medium-independent vehicles. There are two ways to understand what "medium-independence" means on (...) this account; however, on either understanding, the account fails. Either too many things end up being counted as computational, or purportedly natural computations (e.g., neural computations) cannot be counted at all. In the end, illustrating this failure sheds some light on the way to revise the orthodoxy in the hope of a better account of computation. (shrink)

John Searle raised the question whether all computation is observer-relative. Indeed, all of the common views of computation, be they semantical, functional or causal rely on mapping something onto the states of a physical or abstract process. In order to effectively execute such a mapping, this process would have to be observed in some way. Thus a probably syntactical analysis by an observer seems to be essential for judging whether a given process implements some computation or not. In order to (...) be able to explore the nature of these observers in a more formal way, we look at the Computing by Observing paradigm, a theoretical model of computation that includes an observer. We argue that the observers used there, monadic transducers, are good candidates for formalizing the way in which the syntax of a process must be analysed in order to judge whether it is computational. (shrink)

In recent years, the ‘mechanistic view’ has developed as a popular alternative to the ‘semantic view’ concerning the identity of physical computation. However, semanticists have provided powerful arguments that suggest the mechanistic view fails to deliver essential distinctions between paradigmatic computational operations. This article reviews responses on behalf of the mechanist and uses this opportunity to propose a type of pluralism about computational identity. This pluralism contends that there are multiple ‘levels’ of properties and relations pertaining to computation that can (...) inform different kinds of individuation. As such, for the pluralist, there are multiple legitimate ways of classifying computation, depending on the nature of the system in question, and one’s own epistemic priorities. (shrink)

Enactivism advances an understanding of cognition rooted in the dynamic interaction between an embodied agent and their environment, whilst new mechanism suggests that cognition is explained by uncovering the organised components underlying cognitive capacities. On the face of it, the mechanistic model’s emphasis on localisable and decomposable mechanisms, often neural in nature, runs contrary to the enactivist ethos. Despite appearances, this paper argues that mechanistic explanations of cognition, being neither narrow nor reductive, and compatible with plausible iterations of ideas like (...) emergence and downward causation, are congruent with enactivism. Attention to enactivist ideas, moreover, may serve as a heuristic for mechanistic investigations of cognition. Nevertheless, I show how enactivism and approaches that prioritise mechanistic modelling may diverge in starting assumptions about the nature of cognitive phenomena, such as where the constitutive boundaries of cognition lie. (shrink)

There is substantial disagreement among philosophers of embodied cognitive science about the meaning of embodiment. In what follows, I describe three different views that can be found in the current literature. I show how this debate centers around the question of whether the science of embodied cognition can retain the computer theory of mind. One view, which I will label body functionalism, takes the body to play the functional role of linking external resources for problem solving with internal biological machinery. (...) Embodiment is thus understood in terms of the role the body plays in supporting the computational circuits that realize cognition. Body enactivism argues by contrast that no computational account of cognition can account for the role of commonsense knowledge in our everyday practical engagement with the world. I will attempt a reconciliation of these seemingly opposed views. (shrink)

This paper argues that deciding on whether the cognitive sciences need a Representational Theory of Mind matters. Far from being merely semantic or inconsequential, the answer we give to the RTM-question makes a difference to how we conceive of minds. How we answer determines which theoretical framework the sciences of mind ought to embrace. The structure of this paper is as follows. Section 1 outlines Rowlands’s argument that the RTM-question is a bad question and that attempts to answer it, one (...) way or another, have neither practical nor theoretical import. Rowlands concludes this because, on his analysis, there is no non-arbitrary fact of the matter about which properties something must possess in order to qualify as a mental representation. By way of reply, we admit that Rowlands’s analysis succeeds in revealing why attempts to answer the RTM-question simpliciter are pointless. Nevertheless, we show that if specific formulations of the RTM-question are stipulated, then it is possible, conduct su... (shrink)

Radical enactive and embodied approaches to cognitive science oppose the received view in the sciences of the mind in denying that cognition fundamentally involves contentful mental representation. This paper argues that the fate of representationalism in cognitive science matters significantly to how best to understand the extent of cognition. It seeks to establish that any move away from representationalism toward pure, empirical functionalism fails to provide a substantive “mark of the cognitive” and is bereft of other adequate means for individuating (...) cognitive activity. It also argues that giving proper attention to the way the folk use their psychological concepts requires questioning the legitimacy of commonsense functionalism. In place of extended functionalism—empirical or commonsensical—we promote the fortunes of extensive enactivism, clarifying in which ways it is distinct from notions of extended mind and distributed cognition. (shrink)

Computational properties, it is standardly assumed, are to be sharply distinguished from semantic properties. Specifically, while it is standardly assumed that the semantic properties of a cognitive system are externally or non-individualistically individuated, computational properties are supposed to be individualistic and internal. Yet some philosophers (e.g., Tyler Burge) argue that content impacts computation, and further, that environmental factors impact computation. Oron Shagrir has recently argued for these theses in a novel way, and gave them novel interpretations. In this paper I (...) present a conception of computation in cognitive science that takes Shagrir's conception as its starting point, but further develops it in various directions and strengthens it. I argue that the explanatory role of computational properties emerges from the idea that syntactical properties and the relevant external factors presented by cognitive systems compose wide computational properties. I also elaborate upon the notion of content that is in play, and argue that it is contents of the kind that are ascribed by transparent interpretations of content ascriptions that impact computation. This fact enables the thesis that external factors impact computation to rebuff the challenge which concerns the claim that psychology must be individualistic. (shrink)

This paper makes a novel linkage between the multiple-computations theorem in philosophy of mind and Landauer’s principle in physics. The multiple-computations theorem implies that certain physical systems implement simultaneously more than one computation. Landauer’s principle implies that the physical implementation of “logically irreversible” functions is accompanied by minimal entropy increase. We show that the multiple-computations theorem is incompatible with, or at least challenges, the universal validity of Landauer’s principle. To this end we provide accounts of both ideas in terms of (...) low-level fundamental concepts in statistical mechanics, thus providing a deeper understanding of these ideas than their standard formulations given in the high-level terms of thermodynamics and cognitive science. Since Landauer’s principle is pivotal in the attempts to derive the universal validity of the second law of thermodynamics in statistical mechanics, our result entails that the multiple-computations theorem has crucial implications with respect to the second law. Finally, our analysis contributes to the understanding of notions, such as “logical irreversibility,” “entropy increase,” “implementing a computation,” in terms of fundamental physics, and to resolving open questions in the literature of both fields, such as: what could it possibly mean that a certain physical process implements a certain computation. (shrink)

We show that the so-called Multiple-Computations Theorem in cognitive science and philosophy of mind challenges Landauer’s Principle in physics. Since the orthodox wisdom in statistical physics is that Landauer’s Principle is implied by, or is the mechanical equivalent of, the Second Law of thermodynamics, our argument shows that the Multiple-Computations Theorem challenges the universal validity of the Second Law of thermodynamics itself. We construct two examples of computations carried out by one and the same dynamical process with respect to which (...) Landauer’s principle implies contradictory predictions concerning the entropy increase. Our two examples are based on a weak version of the Multiple-Computations Theorem, which is quite uncontroversial, and therefore they amount to a clear refutation of the universal validity of Landauer’s Principle. We consider some responses to this argument that do not attempt to single out one computation over the others, and we show that they do not work. We further consider ways out of the argument by externalist approaches supporting the computational theory of the mind who propose that the interaction of a computing system with the environment is enough to select a single computation over the others. We show on physical grounds that this approach fails too. We then reverse the direction of our challenge and formulate a dilemma for supporters of the computational theory of the mind: they must reject the causal closure of physic; or else they must accept on a priori grounds that Landauer’s Principle and the Second Law of thermodynamics are not universally valid. Finally, we present our version of a type–type mind-brain identity theory called Flat Physicalism, which is based on the paradigm case of statistical mechanics, and we show that it circumvents the challenge from Landauer’s Principle and the Multiple-Computations Theorem and does not fall prey to our dilemma. (shrink)

Phyllis Illari and Jon Williamson propose a formulation for a general mechanistic account, the purpose of which is to capture the similarities across mechanistic accounts in the sciences. Illari and Williamson extract insight from mechanisms in astrophysics—which are notably different from the typical biological mechanisms discussed in the literature on mechanisms—to show how their general mechanistic account accommodates mechanisms across various sciences. We present argumentation that demonstrates why an amendment is necessary to the ontology referred to by the general mechanistic (...) account provided by Illari and Williamson. The amendment is required due to the variability of some components in computing mechanisms: the very same component serves as either entity or activity, both between levels and within the same level of the explanatory hierarchy. We argue that the proper ontological account of these mechanistic components involves disambiguation via explicitly indexing them as entities or activities. (shrink)

Single cell recordings in monkeys provide strong evidence for an important role of the motor system in action understanding. This evidence is backed up by data from studies of the (human) mirror neuron system using neuroimaging or TMS techniques, and behavioral experiments. Although the data acquired from single cell recordings are generally considered to be robust, several debates have shown that the interpretation of these data is far from straightforward. We will show that research based on single-cell recordings allows for (...) unlimited content attribution to mirror neurons. We will argue that a theoretical analysis of the mirroring process, combined with behavioral and brain studies, can provide the necessary limitations. A complexity analysis of the type of processing attributed to the mirror neuron system can help formulate restrictions on what mirroring is and what cognitive functions could, in principle, be explained by a mirror mechanism. We argue that processing at higher levels of abstraction needs assistance of non-mirroring processes to such an extent that subsuming the processes needed to infer goals from actions under the label ?mirroring? is not warranted. (shrink)

The mechanistic view of computation contends that computational explanations are mechanistic explanations. Mechanists, however, disagree about the precise role that the environment – or the so-called “contextual level” – plays for computational explanations. We advance here two claims: Contextual factors essentially determine the computational identity of a computing system ; this means that specifying the “intrinsic” mechanism is not sufficient to fix the computational identity of the system. It is not necessary to specify the causal-mechanistic interaction between the system and (...) its context in order to offer a complete and adequate computational explanation. While the first claim has been discussed before, the second has been practically ignored. After supporting these claims, we discuss the implications of our contextualist view for the mechanistic view of computational explanation. Our aim is to show that some versions of the mechanistic view are consistent with the contextualist view, whilst others are not. (shrink)

The mechanistic view of computation contends that computational explanations are mechanistic explanations. Mechanists, however, disagree about the precise role that the environment – or the so-called “contextual level” – plays for computational explanations. We advance here two claims: Contextual factors essentially determine the computational identity of a computing system ; this means that specifying the “intrinsic” mechanism is not sufficient to fix the computational identity of the system. It is not necessary to specify the causal-mechanistic interaction between the system and (...) its context in order to offer a complete and adequate computational explanation. While the first claim has been discussed before, the second has been practically ignored. After supporting these claims, we discuss the implications of our contextualist view for the mechanistic view of computational explanation. Our aim is to show that some versions of the mechanistic view are consistent with the contextualist view, whilst others are not. (shrink)

This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is contrasted with two other (...) accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed. (shrink)

Representations seem to play a major role in many neuroscientific explanations. Philosophers have long attempted to properly define what it means for a neural state to be a representation of a specific content. Teleosemantic theories of content which characterize representations, in part, by appealing to a historical notion of function, are often regarded as our best path towards an account of neural representations. This paper points to the anti-representationalist consequences of these accounts. I argue that assuming such teleosemantic views will (...) deprive representations of their explanatory role in computational explanations. My argument rests on the claim that many explanations in cognitive neuroscience are entirely independent of any historical considerations. In making this claim, I will also offer an adapted version of the famous Swampperson thought experiment, which is better suited to discussions of subpersonal neural representations. (shrink)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...) dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation. (shrink)

This paper deals with the question: how is computation best individuated? -/- 1. The semantic view of computation: computation is best individuated by its semantic properties. 2. The causal view of computation: computation is best individuated by its causal properties. 3. The functional view of computation: computation is best individuated by its functional properties. -/- Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for that is usually that computation is individuated (...) semantically. I criticize the reasons in support of this view and its presupposition of representation and semantics. Furthermore, I argue that the only justified appeal to a representational individuation of computation might be that it is partly individuated by implicit intrinsic representations. (shrink)

This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring to digital computation simply adhere to (...) Turing’s notion of computability . Classical computability theory studies what functions on the natural numbers are computable and what mathematical problems are undecidable. Whilst a mathematical formalism of computability may perform a methodological function of evaluating computational theories of certain cognitive capacities, concrete computation in physical systems seems to be required for explaining cognition as an embodied phenomenon . There are many non-equivalent accounts of digital computation in physical systems. I examine only a handful of those in this paper: (1) Turing’s account ; (2) The triviality “account”; (3) Reconstructing Smith’s account of participatory computation ; (4) The Algorithm Execution account . My goal in this paper is twofold. First, it is to identify and clarify some of the underlying key requirements mandated by these accounts. I argue that these differing requirements justify a demand that one commits to a particular account when employing the notion of computation in regard to physical systems. Second, it is to argue that despite the informative role that mathematical formalisms of computability may play in cognitive science, they do not specify the relationship between abstract and concrete computation. (shrink)

There has been an ongoing conflict regarding whether reality is fundamentally digital or analogue. Recently, Floridi has argued that this dichotomy is misapplied. For any attempt to analyse noumenal reality independently of any level of abstraction at which the analysis is conducted is mistaken. In the pars destruens of this paper, we argue that Floridi does not establish that it is only levels of abstraction that are analogue or digital, rather than noumenal reality. In the pars construens of this paper, (...) we reject a classification of noumenal reality as a deterministic discrete computational system. We show, based on considerations from classical physics, why a deterministic computational view of the universe faces problems (e.g., a reversible computational universe cannot be strictly deterministic). (shrink)

I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this possibility.

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument (...) offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

The computational theory of mind construes the mind as an information-processor and cognitive capacities as essentially representational capacities. Proponents of the view claim a central role for representational content in computational models of these capacities. In this paper I argue that the standard view of the role of representational content in computational models is mistaken; I argue that representational content is to be understood as a gloss on the computational characterization of a cognitive process.Keywords: Computation; Representational content; Cognitive capacities; Explanation.

The neural vehicles of mental representation play an explanatory role in cognitive psychology that their realizers do not. In this paper, I argue that the individuation of realizers as vehicles of representation restricts the sorts of explanations in which they can participate. I illustrate this with reference to Rupert’s (2011) claim that representational vehicles can play an explanatory role in psychology in virtue of their quantity or proportion. I propose that such quantity-based explanatory claims can apply only to realizers and (...) not to vehicles, in virtue of the particular causal role that vehicles play in psychological explanations. (shrink)

ABSTRACT Shagrir and Sprevak explore the apparent necessity of representation for the individuation of digits in computational systems.1 1 I will first offer a response to Sprevak’s argument that does not mention Shagrir’s original formulation, which was more complex. I then extend my initial response to cover Shagrir’s argument, thus demonstrating that it is possible to individuate digits in non-representational computing mechanisms. I also consider the implications that the non-representational individuation of digits would have for the broader theory of computing (...) mechanisms. 1 The Received View: No Computation without Representation 2 Computing Mechanisms and Functional Individuation 3 Against Computational Externalism 4 Implications for the Mechanistic Account. (shrink)

(Penultimate draft) Embodied Cognition holds that bodily (e.g. sensorimotor) states and processes are directly involved in some higher-level cognitive functions (e.g. reasoning). This challenges traditional views of cognition according to which bodily states and processes are, at most, indirectly involved in higher-level cognition. Although some elements of Embodied Cognition have been integrated into mainstream cognitive science, others still face adamant resistance. In this paper, rather than straightforwardly defend Embodied Cognition against specific objections I will do the following. First, I will (...) present a concise account of embodied conceptual processing and highlight some of its advantages over non-embodied accounts, specifically focusing on the role of metaphors. Second, I will detail the influence of computational metaphors on theories of cognition and evaluation of these theories. Third, I will argue that embodied cognitive mechanisms, specifically those operating through computational metaphors, may drive some of the resistance to Embodied Cognition - and that Embodied Cognition is able to offer a uniquely compelling account of this. Ultimately, this will contribute to an improved understanding of Embodied Cognition, its explanatory power, and how it ought to be evaluated. Additionally, it will shed light on the role of metaphors in shaping philosophical thought and highlight the importance of these influences. (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)

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

I show that the indeterminacy problem for computational structuralists is in fact far more problematic than even the harshest critic of structuralism has realised; it is not a bullet which can be bitten by structuralists as previously thought. Roughly, this is because the structural indeterminacy of logic-gates such as AND/OR is caused by the structural identity of the binary computational digits 0/1 themselves. I provide a proof that pure computational structuralism is untenable because structural indeterminacy entails absurd consequences - namely, (...) that there is only one binary computational digit. I conclude that accounting for individuation is a more important desiderata for a theory of computation than even that of triviality. -/- . (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)

In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may be complemented (...) with semantic considerations, and in many cases, it actually should. (shrink)

Traditionally, computational theory (CT) and dynamical systems theory (DST) have presented themselves as opposed and incompatible paradigms in cognitive science. There have been some efforts to reconcile these paradigms, mainly, by assimilating DST to CT at the expenses of its anti-representationalist commitments. In this paper, building on Piccinini’s mechanistic account of computation and the notion of functional closure, we explore an alternative conciliatory strategy. We try to assimilate CT to DST by dropping its representationalist commitments, and by inviting CT to (...) recognize the functionally closed nature of some computational systems. (shrink)

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is (...) extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

The dialogue develops arguments for and against a broad new world system - info-computationalist naturalism - that is supposed to overcome the traditional mechanistic view. It would make the older mechanistic view into a special case of the new general info-computationalist framework (rather like Euclidian geometry remains valid inside a broader notion of geometry). We primarily discuss what the info-computational paradigm would mean, especially its pancomputationalist component. This includes the requirements for a the new generalized notion of computing that would (...) include sub-symbolic information processing. We investigate whether pancomputationalism can provide the basic causal structure to the world and whether the overall research program of info-computationalist naturalism appears productive, especially when it comes to new approaches to the living world, including computationalism in the philosophy of mind. (shrink)

We have a conundrum. The physical basis of information is clearly a highly active research area. Yet the power of information theory comes precisely from separating it from the detailed problems of building physical systems to perform information processing tasks. Developments in quantum information over the last two decades seem to have undermined this separation, leading to suggestions that information is itself a physical entity and must be part of our physical theories, with resource-cost implications. We will consider a variety (...) of ways in which physics seems to a affect computation, but will ultimately argue to the contrary: rejecting the claims that information is physical provides a better basis for understanding the fertile relationship between information theory and physics. instead, we will argue that the physical resource costs of information processing are to be understood through the need to consider physically embodied agents for whom information processing tasks are performed. Doing so sheds light on what it takes for something to be implementing a computational or information processing task of a given kind. (shrink)

I defend Piccinini’s mechanistic account of computation against three related criticisms adapted from Sprevak’s critique of non-representational computation. I then argue that this defence highlights a major problem with what Sprevak calls the received view; namely, that representation introduces observer-relativity into our account of computation. I conclude that if we want to retain an objective account of computation, we should reject the received view.