Computational perspectivalism has been recently proposed as an alternative to mainstream accounts of physical computation, and especially to the teleologically-based mechanistic view. It takes physical computation to be partly dependent on explanatory perspectives and eschews appeal to teleology in helping individuate computational systems. I assess several varieties of computational perspectivalism, showing that they either collapse into existing non-perspectival views or end up with unsatisfactory or implausible accounts of physical computation. Computational perspectivalism fails, therefore, to be a compelling alternative to perspective-independent (...) theories of computation in physical systems. I conclude that a teleologically-based, non-perspectival mechanistic account of physical computation is to be preferred. 1Introduction 2The Mechanistic View of Computation 2.1Teleological functions and meeting the desiderata 3Varieties of Perspectivalism 4Computational Perspectivalism 4.1Computational perspectivalism and pancomputationalism 4.2Computational perspectivalism and miscomputation 5The Varieties of Computational Perspectivalism Assessed 5.1Innocuous computational perspectivalism 5.2Non-innocuous computational perspectivalism 5.2.1Computational instrumentalism 5.2.2Ontic perspectivalism 6Concluding Remarks. (shrink)

The mainstream view in cognitive science is that computation lies at the basis of and explains cognition. Our analysis reveals that there is no compelling evidence or argument for thinking that brains compute. It makes the case for inverting the explanatory order proposed by the computational basis of cognition thesis. We give reasons to reverse the polarity of standard thinking on this topic, and ask how it is possible that computation, natural and artificial, might be based on cognition and not (...) the other way around. (shrink)

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...) of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe. (shrink)

The traditional view of symbol grounding seeks to connect an a priori internal representation or ‘form’ to its external referent. But such a ‘form’ is usually itself systematically composed out of more primitive parts, so this view ignores its grounding in the physics of the world. Some previous work simulating multiple talking/listening agents has effectively taken this stance, and shown how a shared discrete speech code can emerge. Taking the earlier work of Oudeyer, we have extended his model to include (...) a dispersive force intended to account broadly for a speaker’s motivation to increase auditory distinctiveness. New simulations show that vowel systems result that are more representative of the range seen in human languages. These simulations make many profound abstractions and assumptions. Relaxing these by including more physically and physiologically realistic mechanisms for talking and listening is seen as the key to replicating more complex and dynamic aspects of speech, such as consonant-vowel patterning. (shrink)

The paper questions the expert system paradigm, both in terms of its range of application, and as a significant contribution to the understanding of artificial intelligence. The viewpoint is that of the systems designer who must judge the applicability of these methods in imminent and future systems. The expert system paradigm, (ESP for short), is criticised not because it is ubiquitously wrong, but because its range of application appears to be very limited, and much promise is made of its application (...) in areas where its success is likely to be little more than a matter of luck. The paper considers the success in both academic and commercial settings. It is suggested that the contribution of the ESP to the wider ambitions of AI is modest, and to the practical user is still a considerable and largely unquantifiable risk. (shrink)

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Distributed cognition (d-cog) claims that many cognitive processes are distributed across groups and the surrounding material and cultural environment. Recently, Nancy Nersessian, Ronald Giere, and others have suggested that a d-cog approach might allow us to bring together cognitive and social theories of science. I explore this idea by focusing on the specific interpretation of d-cog found in Edwin Hutchins' canonical text Cognition in the wild. First, I examine the scope of a d-cog approach to science, showing that there are (...) important disputes between cognitive and social theorists on which d-cog remains silent. Second, I suggest that, where social explanations can be recast in d-cog terms, this reformulation will not be acceptable to all social theorists. Finally, I ask how we should make sense of the claim that, on a d-cog analysis, social factors are cognitive factors. (shrink)

This paper serves as an introduction to the special issue on “Reconciling Nature and Nurture in Behavior and Cognition Research” and sets its agenda to resolve the 'interactionist' dichotomy of nature as the genetic, and stable, factors of development, and nurture as the environmental, and plastic influences. In contrast to this received view it promotes the idea that all traits, no matter how developmentally fixed or universal they seem, contingently develop out of a single-cell state through the interaction of a (...) multitude of developmental resources that defies any easy, dichotomous separation. It goes on to analyze the necessary ingredients for such a radical, epigenetic account of development, heredity and evolution: 1. A detailed understanding of the epigenetic nature of the regulatory mechanisms of gene expression; 2. The systematical questioning of preconceptions of 'explanatory' categories of behavior, such as 'innate' or 'programmed'; 3. Especially in psychological research the integration of the concepts of 'development' and 'learning', and a richer classification of the concept of 'environment' in the production of behavior; 4. A fuller understanding of the nature of inheritance that transcends the restriction to the genetic material as the sole hereditary unit, and the study of the process of developmental niche construction; and last 5. Taking serious the role of ecology in development and evolution. I hope that an accomplishment of the above task will then lead to a 'postgenomic' synthesis of nature and nurture that conceptualizes 'nature' as the natural phenotypic outcome 'nurtured' by the natural developmental process leading to it. (shrink)

What is required for one thing to be a reason for another? Must the reason, more precisely, be or involve a principle? In this essay I target the idea that justification via reasons of one's beliefs (e.g., epistemic or moral) requires that the 'justifying reasons' be or involve (substantive and significant) principles. I identify and explore some potential sources of a principles requirement, and conclude that none of them (i.e., the normative function of reasons, the abstract structure of reasons, the (...) universalizability constraint on [moral] reasons, and even the pragmatic considerations that attenuate 'real-world' reason-giving) mandate that reasons be principles. I then explore implications of this conclusion, and note especially the resultant and paradoxical permissibility of justifying reasons that manage to be lawlike (a sine qua non for justifying reasons) even while consisting of highly-detailed and situation-specific proposition sets, e.g., novel-length narratives. (shrink)

There is an enduring tension in thinking about the architecture of systems that select behaviours, including evolved organisms. One line of reasoning supports convergence in control systems and conversion of the values of all options into a common currency, in part because this seems the best or only way of trading off costs and benefits associated with outcomes of varying types. A competing consideration supports parallelism or other forms of fragmentation, because of inefficiencies associated with integration, and suspicion towards general-purpose (...) cognitive systems. In addition, recent neuroscience has given particular attention to the question of how partly independent ‘habit based’ and ‘planning based’ decision-systems might interact. The issues here are, at least in part, a special case of the more general integration versus parallelism tension. This tension provides a useful framework for thinking about the implementation of different kinds of sensitivity to risks of various types, the variety of mechanisms by means of which risk sensitivity might be modulated, and how risk might be traded off against other components of value or disincentive. I describe the tension, briefly survey some of the relevant empirical evidence, and conclude with tentative observations on the topic of risk. (shrink)

The Language of Thought: A New Philosophical Direction, by SchneiderSusan. Cambridge, MA: MIT Press, 2011. Pp. xii + 259.

In this essay we try an answer to the question has intentionality to be reduced to anything? We propose that it is possible to reduce any variety of intentionality to a specification of mechanisms (internal organization of the items involved in a given intentional phenomenon) and a historical pattern of interaction (structure of mutual significant relations historically acquired by different items involved in the same intentional phenomenon). We first clarify the meaning of this proposal having recourse to the Ruth Millikan’s (...) teleosemantics. Then, we assess the relevance and feasibility of our proposal, considering, in a succession, the case of animals and humans, of machines, and of sophisticated human collectives. We conclude arguing the heuristic nature of the proposed reduction. DOI:10.5007/1808-1711.2010v14n2p255. (shrink)

Summary Searle claims that for a machine to have intentional states it is not sufficient that a formal programme be instantiated. Various types of objections to this claim have been brought up by Searle's critics. Searle's replies to some of these objections are analysed. It turns out that it is more to these objections than Searle wants to make us believe. What is crucial, however, is that Searle's „Gedankenexperiment results in a dilemma. At the outset of the dilemma there are (...) two ways of not understanding. According to one of these ways a person (Searle's homunculus) does not understand something without knowing that s/he does not understand. While in the other mode the person knows that s/he doesn't understand. In the first case the inference from facts about the homunculus to facts about the computer is not valid whereas in the second case one would attribute mental states to the computer. Thereby Searle's claim turns out to be unfounded. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.

Cognition is not, and could not possibly be, entirely representational in character. There is also a phenomenal form of cognitive expression that registers the intrinsic properties of mental states themselves. Arguments against the reality of this intrinsic phenomenal dimension to mental experience have focused either on its supposed impossibility, or secondly, the non-appearance of any such qualities to introspection. This paper argues to the contrary, that the registration of cognitive state properties does take place independently of representational content; and necessarily (...) so, since it supports an independent function—self-management. Given the fact that unlike a computer, the brain must, of necessity, operate itself, it needs to be in touch with itself to do so. Intrinsic phenomenal forms of cognitive expression allow the brain to do just that by registering characteristics of the cognitive states themselves. Self-management might well prove to constitute the prime reason for the emergence of subjectivity itself. (shrink)

Simon’s notion of bounded rationality is deeply intertwined with his activity as a cognitive psychologist and founder of so-called cognitivism, a mainstream approach in cognitive psychology until the 1980s. Cognitivism, understood as ‘symbolic information processing,’ provided the first cognitive psychology foundation to bounded rationality. Has bounded rationality since then fully followed the development of cognitive psychology beyond symbolic information processing in the post-Simonian era? To answer this question, this paper focuses on Simon’s opposition during the 1990s to a new view (...) of cognition called situated cognition, which has since put into question the entire view in cognitive psychology of humans as symbolic information processors. This paper then reads the cognitivism/situated cognition debate through the lens of current bounded rationality research in economics, in order to inquire into whether it has tackled the issues in that controversy; to envisage possible new foundations for a cognitive psychology-based bounded rationality. (shrink)

The emperor's new mind (hereafter Emperor) is an attempt to put forward a scientific alternative to the viewpoint of according to which mental activity is merely the acting out of some algorithmic procedure. John Searle and other thinkers have likewise argued that mere calculation does not, of itself, evoke conscious mental attributes, such as understanding or intentionality, but they are still prepared to accept the action the brain, like that of any other physical object, could in principle be simulated by (...) a computer. In Emperor I go further than this and suggest that the outward manifestations ofconscious mental activity cannot even be properly simulated by calculation. To support this view, I use various arguments to show that the results of mathematical insight, in particular, do not seem to be obtained algorithmically. The main thrust ofthis work, however, is to present an overview ofthe present state of physical understanding and to show that an important gap exists at the point where, quantum and classical physics meet, as well as to speculate on how the conscious brain might be taking advantage ofwhatever new physics is needed to fill this gap to achieve its nonalgorithmic effects. (shrink)

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches _vehicle_ and _process_ theories of consciousness, respectively. However, while there may be space (...) for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are _dissociable_, and on the other, by the _classical_ computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of _connectionism; _and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: _phenomenal experience consists in the explicit_ _representation of information in neurally realized PDP networks_.. (shrink)

One of the principal tasks Dennett sets himself in Consciousness Explained is to demolish the Cartesian theater model of phenomenal consciousness, which in its contemporary garb takes the form of Cartesian materialism: the idea that conscious experience is a process of presentation realized in the physical materials of the brain. The now standard response to Dennett is that, in focusing on Cartesian materialism, he attacks an impossibly naive account of consciousness held by no one currently working in cognitive science or (...) the philosophy of mind. Our response is quite different. We believe that, once properly formulated, Cartesian materialism is no straw man. Rather, it is an attractive hypothesis about the relationship between the computational architecture of the brain and phenomenal consciousness, and hence one that is worthy of further exploration. Consequently, our primary aim in this paper is to defend Cartesian materialism from Dennett’s assault. We do this by showing that Dennett’s argument against this position is founded on an implicit assumption (about the relationship between phenomenal experience and information coding in the brain), which while valid in the context of classical cognitive science, is not forced on connectionism. (shrink)

The advent of the intelligent robot has occupied a significant position in society over the past decades and has given rise to new issues in society. As we know, the primary aim of artificial intelligence or robotic research is not only to develop advanced programs to solve our problems but also to reproduce mental qualities in machines. The critical claim of artificial intelligence advocates is that there is no distinction between mind and machines and thus they argue that there are (...) possibilities for machine ethics, just as human ethics. Unlike computer ethics, which has traditionally focused on ethical issues surrounding human use of machines, AI or machine ethics is concerned with the behaviour of machines towards human users and perhaps other machines as well, and the ethicality of these interactions. The ultimate goal of machine ethics, according to the AI scientists, is to create a machine that itself follows an ideal ethical principle or a set of principles; that is to say, it is guided by this principle or these principles in decisions it makes about possible courses of action it could takea. Thus, machine ethics task of ensuring ethical behaviour of an artificial agent. Although, there are many philosophical issues related to artificial intelligence, but our attempt in this paper is to discuss, first, whether ethics is the sort of thing that can be computed. Second, if we are ascribing mind to machines, it gives rise to ethical issues regarding machines. And if we are not drawing the difference between mind and machines, we are not only redefining specifically human mind but also the society as a whole. Having a mind is, among other things, having the capacity to make voluntary decisions and actions. The notion of mind is central to our ethical thinking, and this is because the human mind is self-conscious, and this is a property that machines lack, as yet. (shrink)

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)

Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...) label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference. (shrink)

The current renewal of connectionist techniques using networks of neuron-like units has started to have an influence on cognitive modelling. However, compared with classical artificial intelligence methods, the position of connectionism is still not clear. In this article artificial intelligence and connectionism are systematically compared as cognitive models so as to bring out the advantages and shortcomings of each. The problem of structured representations appears to be particularly important, suggesting likely research directions.