Marr’s seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods from (...) computational complexity theory and focuses on optimal strategies for completing cognitive tasks. However, human cognitive capacities in mathematical problem solving are essentially characterised by processes that are computationally sub-optimal, because they initially add to the computational complexity of the solutions. Yet, these solutions can be optimal for human cognisers given the acquisition and enactment of mathematical practices. Here we present diagrams and the spatial manipulation of symbols as two examples of problem solving strategies that can be computationally sub-optimal but humanly optimal. These aspects need to be taken into account when analysing competence in mathematical problem solving. Empirically informed considerations on enculturation can help identify, explore, and model the cognitive processes involved in problem solving tasks. The enculturation account of mathematical problem solving strongly suggests that computational-level analyses need to be complemented by considerations on the algorithmic and implementational levels. The emerging research strategy can help develop algorithms that model what we call enculturated cognitive optimality in an empirically plausible and ecologically valid way. (shrink)

This paper argues that the faculty of language comes essentially for free in evolutionary terms, by grace of a capacity shared with some evolutionarily quite distantly related animals for deliberatively planning action in the world. The reason humans have language of a kind that animals do not is because of a qualitative difference in the nature of human plans rather than anything unique to language.

Neurological syndromes in which consciousness seems to malfunction, such as temporal lobe epilepsy, visual scotomas, Charles Bonnet syndrome, and synesthesia offer valuable clues about the normal functions of consciousness and ‘qualia’. An investigation into these syndromes reveals, we argue, that qualia are different from other brain states in that they possess three functional characteristics, which we state in the form of ‘three laws of qualia’. First, they are irrevocable: I cannot simply decide to start seeing the sunset as green, or (...) feel pain as if it were an itch; second, qualia do not always produce the same behaviour: given a set of qualia, we can choose from a potentially infinite set of possible behaviours to execute; and third, qualia endure in short-term memory, as opposed to non-conscious brain states involved in the on-line guidance of behaviour in real time. We suggest that qualia have evolved these and other attributes because of their role in facilitating non-automatic, decision-based action. We also suggest that the apparent epistemic barrier to knowing what qualia another person is experiencing can be overcome by using a ‘bridge’ of neurons; and we offer a hypothesis about the relation between qualia and one's sense of self. (shrink)

The paper traces some of the assumptions that have informed conservative naturalism in linguistic theory, critically examines their justification, and proposes a more liberal alternative.

Are all three of Marr's levels needed? Should they be kept distinct? We argue for the distinct contributions and methodologies of each level of analysis. It is important to maintain them because they provide three different perspectives required to understand mechanisms, especially information-processing mechanisms. The computational perspective provides an understanding of how a mechanism functions in broader environments that determines the computations it needs to perform. The representation and algorithmic perspective offers an understanding of how information about the environment is (...) encoded within the mechanism and what are the patterns of organization that enable the parts of the mechanism to produce the phenomenon. The implementation perspective yields an understanding of the neural details of the mechanism and how they constrain function and algorithms. Once we adequately characterize the distinct role of each level of analysis, it is fairly straightforward to see how they relate. (shrink)

Recent work in the methodology of connectionist explanation has I'ocrrsccl on the notion of levels of explanation. Specific issucs in conncctionisrn hcrc intersect with rvider areas of debate in the philosophy of psychology and thc philosophy of science generally. The issues I raise in this chapter, then, are not unique to cognitive science; but they arise in new and important contexts when connectionism is taken seriously as a model of cognition. The general questions are the relation between levels and the (...) status of levels which have no obvious relation to others. In speaking of levels, what is the connection, if there is one, between explanation and ontology? Which, if any, conccpt of reduction is applicable to connectionist systems? What kind of legitinrtcy can the constructs of common sense psychology, or of that vclsion ol intentional realism represented by classical symbol-systems n I, hirvc irr ir full-scale connectionist theory of mind? (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)

I enlarge on the theme that the brain mechanisms required for languageand other aspects of the human mind evolved through selective changes in the regulatory genes governing growth. Extension of the period of postnatal growth increases the role of the environment in structuring the brain, and spatiotemporal programming (heterochrony) ofgrowth might explain hierarchical representation, hemispheric specialization, and perhaps sex differences.

Why do people create extra representations to help them make sense of situations, diagrams, illustrations, instructions and problems? The obvious explanation— external representations save internal memory and com- putation—is only part of the story. I discuss seven ways external representations enhance cognitive power: they change the cost structure of the inferential landscape; they provide a structure that can serve as a shareable object of thought; they create persistent referents; they facilitate re- representation; they are often a more natural representation of (...) structure than mental representations; they facilitate the computation of more explicit encoding of information; they enable the construction of arbitrarily complex structure; and they lower the cost of controlling thought—they help coordinate thought. Jointly, these functions allow people to think more powerfully with external representations than without. They allow us to think the previously unthinkable. (shrink)

In the Fall of 1983, I offered a junior/senior-level course in Philosophy of Artificial Intelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificial intelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased in the Philosophy Department’s (...) AI-related courses, such as logic, philosophy of mind, and epistemology, and that several computer science students have added philosophy as a second major.) This article describes that course, provides material for use in such a course, and offers a bibliography of relevant articles in the AI, cognitive science, and philosophical literature. (shrink)

In this paper I defend the classical computational account of reasoning against a range of highly influential objections, sometimes called relevance problems. Such problems are closely associated with the frame problem in artificial intelligence and, to a first approximation, concern the issue of how humans are able to determine which of a range of representations are relevant to the performance of a given cognitive task. Though many critics maintain that the nature and existence of such problems provide grounds for rejecting (...) classical computationalism, I show that this is not so. Some of these putative problems are a cause for concern only on highly implausible assumptions about the extent of our cognitive capacities, whilst others are a cause for concern only on similarly implausible views about the commitments of classical computationalism. Finally, some versions of the relevance problem are not really objections but hard research issues that any satisfactory account of cognition needs to address. I conclude by considering the diagnostic issue of why accounts of cognition in general—and classical computational accounts, in particular—have faired so poorly in addressing such research issues.Keywords: Computationalism; Frame problem; Relevance. (shrink)

According to Marr, a computational-level theory consists of two elements, the what and the why . This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: ( a ) that the Why element plays an explanatory role in computational-level theories, ( b ) that its goal is to explain why the computed function (specified by the What element) is appropriate for a given visual task, and ( c ) that the (...) explanation consists in showing that the functional relations between the representing cells are similar to the “external” mathematical relations between the entities that these cells represent. *Received September 2009; revised January 2010. †To contact the author, please write to: Departments of Philosophy and Cognitive Science, The Hebrew University, Jerusalem 91905, Israel; e-mail: [email protected]. (shrink)

The three commentaries of Van Orden, Spivey and Anderson, and Dietrich (with Markman’s as a backdrop) form a tableau that reminds me of a fable by Ivan Andreevich Krylov (1769 - 1844), in which a swan, a pike, and a crawfish undertake jointly to move a cart laden with goods. What transpires then is not unexpected: the swan strives skyward, the pike pulls toward the river, and the crawfish scrambles backward. The call for papers for the present ecumenically minded special (...) issue of JETAI was designed to minimize this kind of discord, by charging the authors to examine the possibility of epistemological pluralism in cognitive science — a field whose very diversity makes fundamental disagreement more likely than in other sciences. No doubt, the road mapped out by the editor had been conceived with good intentions in mind, but where did it lead us? It has been said that no good intention must go unpunished. To celebrate this venerable academic tradition (and also because I have a reputation to maintain), the following remarks will therefore be mostly other than conciliatory; caveat lector. (shrink)

The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain of (...) human functioning that can be addressed exclusively in terms of a formal symbolic or algorithmic vocabulary or level of analysis. Much of the paper elaborates various conditions that need to be met if a literal view of mental activity as computation is to serve as the basis for explanatory theories. The coherence of such a view depends on there being a principled distinction between functions whose explanation requires that we posit internal representations and those that we can appropriately describe as merely instantiating causal physical or biological laws. In this paper the distinction is empirically grounded in a methodological criterion called the " cognitive impenetrability condition." Functions are said to be cognitively impenetrable if they cannot be influenced by such purely cognitive factors as goals, beliefs, inferences, tacit knowledge, and so on. Such a criterion makes it possible to empirically separate the fixed capacities of mind from the particular representations and algorithms used on specific occasions. In order for computational theories to avoid being ad hoc, they must deal effectively with the "degrees of freedom" problem by constraining the extent to which they can be arbitrarily adjusted post hoc to fit some particular set of observations. This in turn requires that the fixed architectural function and the algorithms be independently validated. It is argued that the architectural assumptions implicit in many contemporary models run afoul of the cognitive impenetrability condition, since the required fixed functions are demonstrably sensitive to tacit knowledge and goals. The paper concludes with some tactical suggestions for the development of computational cognitive theories. (shrink)

In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological.

High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...) BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. (shrink)

This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999).

Central to contemporary cognitive science is the notion that mental processes involve computations defined over internal representations. This view stands in sharp contrast to the to visual perception and cognition, whose most prominent proponent has been J.J. Gibson. In the direct theory, perception does not involve computations of any sort; it is the result of the direct pickup of available information. The publication of Gibson's recent book (Gibson 1979) offers an opportunity to examine his approach, and, more generally, to contrast (...) the theory of direct perception with the computational/representational view. In the first part of the present article (Sections 2direct perceptioncase study”: the problem of perceiving the three-dimensional shape of moving objects is examined. This problem, which has been extensively studied within the immediate perception framework, serves to illustrate some of the inherent shortcomings of that approach. Finally, in Section 5, an attempt is made to place the theory of direct perception in perspective by embedding it in a more comprehensive framework. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...) ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

Philipp Koralus' "Reason and Inquiry" presents a questioning or erotetic theory of reasoning. This review connects ideas from the book to the broader philosophical literature on inquiry and questions, as well as providing a simplified overview of the theory.

Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration. -/- Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science. (shrink)

At the beginning of the 20th century, Gestalt psychologists put forward the concept of psychoneural isomorphism, which was meant to replace Fechner’s obscure notion of psychophysical parallelism and provide a heuristics that may facilitate the search for the neural correlates of the mind. However, the concept has generated much confusion in the debate, and today its role is still unclear. In this contribution, I will attempt a little conceptual spadework in clarifying the concept of psychoneural isomorphism, focusing exclusively on conscious (...) visual perceptual experience and its neural correlates. Firstly, I will outline the history of our concept, and its alleged metaphysical and epistemic roles. Then, I will clarify the nature of isomorphism and rule out its metaphysical role. Finally, I will review some epistemic roles of our concept, zooming in on the work of Jean Petitot, and suggest that it does not play a relevant heuristic role. I conclude suggesting that psychoneural isomorphism might be an indicator of robustness for certain mathematical descriptions of perceptual content. (shrink)

One can disagree with Müller that it is neurobiologically questionable to suppose that human language is innate, specialized, and species-specific, yet agree that the precise brain mechanisms controlling language in any individual will be influenced by epigenesis and genetic variability, and that the interplay between inherited and acquired aspects of linguistic capacity deserves to be investigated.

Müller provides a valuable synthesis of neurobiological evidence on the epigenetic development of neural structures involved in language acquisition. The pluripotentiality of developing neural tissue crucially constrains linguistic/cognitive theorizing about supposedly innate neural mechanisms and contributes significantly to our understanding of experience–dependent processes involved in language acquisition. Without this understanding, any proposed explanation of language acquisition is suspect.

Neurobiological models of language need a level of analysis that can account for the typical range of language phenomena. Because linguistically motivated models have been successful in explaining numerous language properties, it is premature to dismiss them as biologically irrelevant. Models attempting to unify neurobiology and linguistics need to be sensitive to both sources of evidence.

Contrary to Müller's claims, and in support of modular theories, genetic factors play a substantial and significant role in language. The finding that some children with specific language impairment (SLI) have nonlinguistic impairments may reflect improper diagnosis of SLI or impairments that are secondary to linguistic impairments. Thus, such findings do not argue against the modularity thesis. The lexical/functional distinction appears to be innate and specifically linguistic and could be instantiated in either symbolic or connectionist systems.

According to Marr, a computational-level theory consists of two elements, the what and the why. This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: that the Why element plays an explanatory role in computational-level theories, that its goal is to explain why the computed function is appropriate for a given visual task, and that the explanation consists in showing that the functional relations between the representing cells are similar to (...) the “external” mathematical relations between the entities that these cells represent. (shrink)

This paper addresses the relationship between psychological capacities, as they are understood within cognitive psychology, and neurobiological activities. First, Lycan’s (1987) account of this relationship is examined and certain problems with his account are explained. According to Lycan, psychological capacities occupy a higher level than neurobiological activities in a hierarchy of levels of nature, and psychological entities can be decomposed into neurobiological entities. After discussing some problems with Lycan’s account, a similar, more recent account built around levels of mechanisms is (...) examined (Craver 2007). In the second half of this paper, an alternative is laid out. This new account uses levels of organization and levels of explanation to create a two-dimensional model. Psychological capacities occupy a high level of explanation relative to the cellular and molecular levels of organization. As a result, according to this model, psychological capacities are a particular way of describing the activities that occur at the cellular and molecular levels of organization. (shrink)

Cybernetics promoted machine-supported investigations of adaptive sensorimotor behaviours observed in biological systems. This methodological approach receives renewed attention in contemporary robotics, cognitive ethology, and the cognitive neurosciences. Its distinctive features concern machine experiments, and their role in testing behavioural models and explanations flowing from them. Cybernetic explanations of behavioural events, regularities, and capacities rely on multiply realizable mechanism schemata, and strike a sensible balance between causal and unifying constraints. The multiple realizability of cybernetic mechanism schemata paves the way to principled (...) comparisons between biological systems and machines. Various methodological issues involved in the transition from mechanism schemata to their machine instantiations are addressed here, by reference to a simple sensorimotor coordination task. These concern the proper treatment of ceteris paribus clauses in experimental settings, the significance of running experiments with correct but incomplete machine instantiations of mechanism schemata, and the advantage of operating with real machines ??? as opposed to simulated ones ??? immersed in real environments. (shrink)

Evolutionary psychology is widely understood as involving an integration of evolutionary theory and cognitive psychology, in which the former promises to revolutionise the latter. In this paper, I suggest some reasons to doubt that the assumptions of evolutionary theory and of cognitive psychology are as directly compatible as is widely assumed. These reasons relate to three different problems of specifying adaptive functions as the basis for characterising cognitive mechanisms: the disjunction problem, the grain problem and the environment problem. Each of (...) these problems can be understood as arising from incommensurate characterisations of the nature and role of 'the environment' in the two approaches. Purported solutions to the problems appear to require detailed information concerning the EEA (environment of evolutionary adaptedness), with the disjunction problem placing the lowest requirement, the environment problem placing the highest requirement, and the grain problem placing an intermediate one. In each case, such information is not likely to be forthcoming, because it may require iterating through successively more distant EEA's with no principled stopping point. This produces a dilemma for evolutionary psychology - either to solve these apparently insoluble problems, or to attempt to avoid them but in doing so forego detailed evolutionary constraints on cognition. (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)

This paper discusses some requirements on a folk-psychological, computational account of concepts. Although most psychological views take the folk-psychological stance that concept-possession requires capacities of both representation and classification, such views lack a philosophical context. In contrast, philosophically motivated views stress one of these capacities at the expense of the other. This paper seeks to provide some philosophical motivation for the (folk-) psychological stance. Philosophical and psychological constraints on a computational level account provide the context for evaluating two theses. The (...) first, the Classificatory View, is that concept-possession is constituted by the ability to classify states of the world. I argue, against this view, that to be able to classify, a thinker must also be able to represent the world. The second thesis, the Representational View, is that to possess a concept is constituted by the ability to represent the world. I argue that ascribing this ability is incoherent without ascribing an ability to classify. Hence, a detailed computational specification of concept-possession suggests that the folk-psychological stance is accurate. Philosophical views of concepts, (e.g. Fodor, 1987), adhering to one of the strong theses, whilst adverting to folk-psychological motivations, are thus both insufficiently complex and incoherent. (shrink)