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Summary At least since the rise of anti-representational dynamicism (including so-called radical dynamicism, embedded and embodied cognition as well as enactivism), cognitive science and AI research has been engaged with issues concerning the need for representation in modelling and explaining intelligence. Traditionally, AI research has been based on the tenet that representation is necessary for intelligence. The anti-representational dynamicist approach rejects representation (and computation) as being key to understanding cognition and intelligence. The AI without Representation category centers on the proposal that AI can do without the central ingredient of representing the world.
Key works Given the strong interrelation between this category and other leaf categories, the interested reader is referred to related works in sibling categories, such as Symbols and Symbol Systems, Implicit/Explicit Rules and Representations as well as Computation and Representation, Misc. More specific classical works on the present topic include the following ones. Varela et al 1991 offer an early introduction to embodied cognition without representation. Bearing a similar name to this category is Rodney Brooks' famous work based on his mobots in Brooks 1991. Bearing the same name is Hubert L. Dreyfus' analysis of Merleau-Ponty's Critique of Mental Representation in Dreyfus 2002. Clark & Toribio 1994 offer an early critique of the anti-representationalist approach to modelling and understanding intelligence. Shimon Edelman ms offers a nice comparative analysis of the anti-representational approach, on the one hand, and those who defend the need for representation Markman & Dietrich 2000, on the other hand. Garzón 2008 outlines a general account of cognition without the need of positing representational resources.
Introductions Bickhard 1993 Müller 2007 Wallis 2004
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  1. Kristin Andrews (2007). Critter Psychology: On the Possibility of Nonhuman Animal Folk Psychology. In. In Daniel D. Hutto & Matthew Ratcliffe (eds.), Folk Psychology Re-Assessed. Kluwer/Springer Press. 191--209.
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  2. Murat Aydede (2005). Computation and Functionalism: Syntactic Theory of Mind Revisited. In G. Irzik & G. Guezeldere (eds.), Turkish Studies in the History and Philosophy of Science. Springer.
    There is a thesis often aired by some philosophers of psychology that syntax is all we need and there is no need to advert to intentional/semantic properties of symbols for purposes of psychological explanation. Indeed, the worry has been present since the first explicit articulation of so-called Computational Theory of Mind (CTM). Even Fodor, who has been the most ardent defender of the Language of Thought Hypoth- esis (LOTH) (which requires the CTM), has raised worries about its apparent consequences. The (...)
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  3. John A. Barnden & Kankanahalli Srinivas (1996). Quantification Without Variables in Connectionism. Minds and Machines 6 (2):173-201.
    Connectionist attention to variables has been too restricted in two ways. First, it has not exploited certain ways of doing without variables in the symbolic arena. One variable-avoidance method, that of logical combinators, is particularly well established there. Secondly, the attention has been largely restricted to variables in long-term rules embodied in connection weight patterns. However, short-lived bodies of information, such as sentence interpretations or inference products, may involve quantification. Therefore short-lived activation patterns may need to achieve the effect of (...)
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  4. William P. Bechtel (1996). Yet Another Revolution? Defusing the Dynamical System Theorists' Attack on Mental Representations. Presidential Address to Society of Philosophy and Psychology.
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  5. Samuel Bellini-Leite (2013). The Embodied Embedded Character of System 1 Processing. Mens Sana Monographs 11 (1):239-252.
    In the last thirty years, a relatively large group of cognitive scientists have begun characterising the mind in terms of two distinct, relatively autonomous systems. To account for paradoxes in empirical results of studies mainly on reasoning, Dual Process Theories were developed. Such Dual Process Theories generally agree that System 1 is rapid, automatic, parallel, and heuristic-based and System 2 is slow, capacity-demanding, sequential, and related to consciousness. While System 2 can still be decently understood from a traditional cognitivist approach, (...)
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  6. Emiliano Boccardi (2009). Who's Driving the Syntactic Engine? Journal for General Philosophy of Science 40 (1):23 - 50.
    The property of being the implementation of a computational structure has been argued to be vacuously instantiated. This claim provides the basis for most antirealist arguments in the field of the philosophy of computation. Standard manoeuvres for combating these antirealist arguments treat the problem as endogenous to computational theories. The contrastive analysis of computational and other mathematical representations put forward here reveals that the problem should instead be treated within the more general framework of the Newman problem in structuralist accounts (...)
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  7. Rodney Brooks (1991). Intelligence Without Representation. Artificial Intelligence 47:139-159.
    Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...)
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  8. Andy Clark & Josefa Toribio (1994). Doing Without Representing. Synthese 101 (3):401-31.
    Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in armchair theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper (...)
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  9. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  10. Daniel C. Dennett (1989). Cognitive Ethology. In Goals, No-Goals and Own Goals. Unwin Hyman.
    The field of Artificial Intelligence has produced so many new concepts--or at least vivid and more structured versions of old concepts--that it would be surprising if none of them turned out to be of value to students of animal behavior. Which will be most valuable? I will resist the temptation to engage in either prophecy or salesmanship; instead of attempting to answer the question: "How might Artificial Intelligence inform the study of animal behavior?" I will concentrate on the obverse: "How (...)
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  11. Daniel C. Dennett (1989). Goals, No-Goals and Own Goals. Unwin Hyman.
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  12. Eric Dietrich (1989). Semantics and the Computational Paradigm in Computational Psychology. Synthese 79 (April):119-41.
    There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics (...)
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  13. Hubert L. Dreyfus (2002). Refocusing the Question: Can There Be Skillful Coping Without Propositional Representations or Brain Representations? [REVIEW] Phenomenology and the Cognitive Sciences 1 (4):413-25.
  14. Hubert L. Dreyfus (2002). Intelligence Without Representation: Merleau-Ponty's Critique of Mental Representation. Phenomenology and the Cognitive Sciences 1:367-83.
  15. Mirko Farina (forthcoming). Beyond the Brain - How Body and Environment Shape Animal and Human Minds. [REVIEW] Phenomenology and The Cognitive Sciences.
    Beyond the Brain: How Body and Environment Shape Animal and Human Minds is an eye-opening and thought- provoking book that sets out a much-needed contribution to the study of the relationship between animals, cognition and the environment. The volume provides remarkable new insights into how to understand animal (including human) behavior, raises interesting questions about the role of environmental affordances in the emergence of complex cognitive processes and provides the reader with a refreshing break from the wearisome excess of brain-centric (...)
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  16. James H. Fetzer (1997). Thinking and Computing: Computers as Special Kinds of Signs. [REVIEW] Minds and Machines 7 (3):345-364.
    Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...)
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  17. Nir Fresco (2010). Explaining Computation Without Semantics: Keeping It Simple. [REVIEW] Minds and Machines 20 (2):165-181.
    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 (...)
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  18. Terence E. Horgan & John L. Tienson (1989). Representation Without Rules. Philosophical Perspectives 17 (1):147-74.
  19. Fred A. Keijzer (1998). Doing Without Representations Which Specify What to Do. Philosophical Psychology 11 (3):269-302.
    A discussion is going on in cognitive science about the use of representations to explain how intelligent behavior is generated. In the traditional view, an organism is thought to incorporate representations. These provide an internal model that is used by the organism to instruct the motor apparatus so that the adaptive and anticipatory characteristics of behavior come about. So-called interactionists claim that this representational specification of behavior raises more problems than it solves. In their view, the notion of internal representational (...)
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  20. David Kirsh (1991). Today the Earwig, Tomorrow Man? Artificial Intelligence 47:161-184.
    A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for (...)
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  21. Hengwei Li & Huaxin Huang (2007). Representation and Development of Cognition. Frontiers of Philosophy in China 2 (4):583-600.
    One of the major divergences between dynamical systems theory and symbolism lies in their views on the role of representation in cognition. From the perspective of development, the cognitive development could be divided into three levels: sensorimotor, imagery representation and linguistic representation. It is claimed that representation is not a sufficient condition though it is necessary for cognition. However, it does not mean that the authors agree with the notion of strong coupling in dynamicism that completely rejects representation.
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  22. Arthur B. Markman & Eric Dietrich (2000). Extending the Classical View of Representation. Trends in Cognitive Sciences 4 (12):470-475.
    Representation is a central part of models in cognitive science, but recently this idea has come under attack. Researchers advocating perceptual symbol systems, situated action, embodied cognition, and dynamical systems have argued against central assumptions of the classical representational approach to mind. We review the core assumptions of the dominant view of representation and the four suggested alternatives. We argue that representation should remain a core part of cognitive science, but that the insights from these alternative approaches must be incorporated (...)
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  23. Arthur B. Markman & Eric Dietrich (2000). In Defense of Representation. Cognitive Psychology 40 (2):138--171.
    The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological theories. Our analysis (...)
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  24. Ruth G. Millikan, On Reading Signs.
    On Reading Signs; Some Differences between Us and The Others If there are certain kinds of signs that an animal cannot learn to interpret, that might be for any of a number of reasons. It might be, first, because the animal cannot discriminate the signs from one another. For example, although human babies learn to discriminate human speech sounds according to the phonological structures of their native languages very easily, it may be that few if any other animals are capable (...)
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  25. Marcin Miłkowski (2013). Explaining the Computational Mind. MIT Press.
    In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.
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  26. Vincent C. Müller (2007). Is There a Future for AI Without Representation? Minds and Machines 17 (1):101-115.
    This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...)
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  27. Keld Stehr Nielsen (2010). Representation and Dynamics. Philosophical Psychology 23 (6):759-773.
    In the last decade several prominent critics have charged that invocation of representations is not only not essential for cognitive science, but should be avoided. These claims have been followed by counterarguments demonstrating that the notion certainly is important in explanations of cognitive phenomena. Analyzing some important contributions to the debate, Anthony Chemero has argued that representationalists still need to explain the significance of the notion once there is an available formal account of a system and has, accordingly, challenged representationalists (...)
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  28. Gualtiero Piccinini (2008). Computation Without Representation. Philosophical Studies 137 (2):205-241.
    The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational (...)
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  29. Michael Rescorla (2009). Cognitive Maps and the Language of Thought. British Journal for the Philosophy of Science 60 (2):377-407.
    Fodor advocates a view of cognitive processes as computations defined over the language of thought (or Mentalese). Even among those who endorse Mentalese, considerable controversy surrounds its representational format. What semantically relevant structure should scientific psychology attribute to Mentalese symbols? Researchers commonly emphasize logical structure, akin to that displayed by predicate calculus sentences. To counteract this tendency, I discuss computational models of navigation drawn from probabilistic robotics. These models involve computations defined over cognitive maps, which have geometric rather than logical (...)
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  30. Ricardo Restrepo (2012). Computers, Persons, and the Chinese Room. Part 1: The Human Computer. Journal of Mind and Behavior 33 (1):27-48.
    Detractors of Searle’s Chinese Room Argument have arrived at a virtual consensus that the mental properties of the Man performing the computations stipulated by the argument are irrelevant to whether computational cognitive science is true. This paper challenges this virtual consensus to argue for the first of the two main theses of the persons reply, namely, that the mental properties of the Man are what matter. It does this by challenging many of the arguments and conceptions put forth by the (...)
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  31. Lynne Rudder Baker (2002). Comments on Hubert L. Dreyfus €œIntelligence Without Representation”. Phenomenology and the Cognitive Sciences 1 (4):411-412.
    My main reaction to “Intelligence without representation” is to applaud. Dreyfus’s use of Merleau-Ponty is a refreshing new breeze in philosophy of psychology. About twenty or so years ago, philosophers struck an unfortunate course dictated by a pair of dubious assumptions: (1) that ordinary psychological attributions were at risk unless vindicated by some science; and (2) that the only possible scientific vindication required that intentional content be represented in the brain. Thus did representationalism become, in Jerry Fodor’s memorable phrase, “the (...)
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  32. Nigel Stepp, Anthony Chemero & Michael T. Turvey (2011). Philosophy for the Rest of Cognitive Science. Topics in Cognitive Science 3 (2):425-437.
    Cognitive science has always included multiple methodologies and theoretical commitments. The philosophy of cognitive science should embrace, or at least acknowledge, this diversity. Bechtel’s (2009a) proposed philosophy of cognitive science, however, applies only to representationalist and mechanist cognitive science, ignoring the substantial minority of dynamically oriented cognitive scientists. As an example of nonrepresentational, dynamical cognitive science, we describe strong anticipation as a model for circadian systems (Stepp & Turvey, 2009). We then propose a philosophy of science appropriate to nonrepresentational, dynamical (...)
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  33. Robert S. Stufflebeam (1997). Why Computation Need Not Be Traded Only for Internal Representation. Behavioral and Brain Sciences 20 (1):80-81.
    Although Clark & Thornton's “trading spaces” hypothesis is supposed to require trading internal representation for computation, it is not used consistently in that fashion. Not only do some of the offered computation-saving strategies turn out to be nonrepresentational, others (e.g., cultural artifacts) are external representations. Hence, C&T's hypothesis is consistent with antirepresentationalism.
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  34. Tim van Gelder (1995). What Might Cognition Be If Not Computation? Journal of Philosophy 92 (7):345-81.
  35. Iris van Rooij (2012). Self-Organization Takes Time Too. Topics in Cognitive Science 4 (1):63-71.
    Four articles in this issue of topiCS (volume 4, issue 1) argue against a computational approach in cognitive science in favor of a dynamical approach. I concur that the computational approach faces some considerable explanatory challenges. Yet the dynamicists’ proposal that cognition is self-organized seems to only go so far in addressing these challenges. Take, for instance, the hypothesis that cognitive behavior emerges when brain and body (re-)configure to satisfy task and environmental constraints. It is known that for certain systems (...)
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  36. Wendell Wallach, Stan Franklin & Colin Allen (2010). A Conceptual and Computational Model of Moral Decision Making in Human and Artificial Agents. Topics in Cognitive Science 2 (3):454-485.
    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational (...)
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  37. Peter Wallis (2004). Intention Without Representation. Philosophical Psychology 17 (2):209-223.
    A mechanism for planning ahead would appear to be essential to any creature with more than insect level intelligence. In this paper it is shown how planning, using full means-ends analysis, can be had while avoiding the so called symbol grounding problem. The key role of knowledge representation in intelligence has been acknowledged since at least the enlightenment, but the advent of the computer has made it possible to explore the limits of alternate schemes, and to explore the nature of (...)
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  38. Jonathan Webber (2002). Doing Without Representation: Coping with Dreyfus. Philosophical Explorations 5 (1):82-88.
    Hubert Dreyfus argues that the traditional and currently dominant conception of an action, as an event initiated or governed by a mental representation of a possible state of affairs that the agent is trying to realise, is inadequate. If Dreyfus is right, then we need a new conception of action. I argue, however, that the considerations that Dreyfus adduces show only that an action need not be initiated or governed by a conceptual representation, but since a representation need not be (...)
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