Andy Clark

I am John's[3] brain. In the flesh, I am just a rather undistinguished looking grey/white mass of cells. My surface is heavily convoluted and I am possessed of a fairly differentiated internal structure. John and I are on rather close and intimate terms; indeed, sometimes it is hard to tell us apart. But at times, John takes this intimacy a little too far. When that happens, he gets very confused about my role and functioning. He imagines that I organize and process information in ways which echo his own perspective on the world. In short, he thinks that his thoughts are, in a rather direct sense, my thoughts. There is some truth to this of course. But things are really rather more complicated than John suspects, as I shall try to show

Some recent cognitive-scientific research suggests that a considerable amount of intelligent action is generated not by the systematic activity of internal representations, but by complex interactions involving neural, bodily, and environmental factors. Following an analysis of this threat to representational explanation, we pursue an analogy between the role of genes in the production of biological form and the role of neural states in the production of behaviour, in order to develop a notion of genic representation. In both cases an appeal to normal ecological context is used to balance multi-factoral, interactive causal determination against the intuition that certain aspects of the causal nexus play a special role in promoting adaptive success. Certain worries abut this vision help us to get a better grip on the concept of genic representation itself. We end with a puzzle concerning the relation between cognition and representation.

We seem, or so it seems to some theorists, to experience a rich stream of highly detailed information concerning an extensive part of our current visual surroundings. But this appearance, it has been suggested, is in some way illusory. Our brains do not command richly detailed internal models of the current scene. Our seeings, it seems, are not all that they seem. This, then, is the Grand Illusion. We think we see much more than we actually do. In this paper I shall (briefly) rehearse the empirical evidence for this rather startling claim, and then critically examine a variety of responses. One especially interesting response is a development of the so-called ‘skill theory’, according to which there is no illusion after all. Instead, so the theory goes, we establish the required visual contact with our world by an ongoing process of active exploration, in which the world acts as a kind of reliable, interrogable, external memory (Noe, Pessoa and Thompson (2000), Noe (2001). The most fully worked-out versions of this response ( Noe and O’Regan (2000), O’Regan and Noe 2001) tend, however, to tie the contents of conscious visual experience rather too tightly to quite low-level features of this ongoing sensorimotor engagement. This (I shall argue) undervalues the crucial links between perceptual experience, reason and intentional action, and opens the door to a problem that I will call ‘sensorimotor chauvinism’: the premature welding of experiential contents to very specific details of our embodiment and sensory apparatus. Drawing on the dual visual systems hypothesis of Milner and Goodale (1995), I sketch an alternative version of the skill theory, in which the relation between conscious visual experience and the low-level details of sensorimotor engagement is indirect and non-constitutive. The hope is thus to embrace the genuine insights of the skill theory response, while depicting conscious visual experience as most tightly geared to knowing and reasoning about our world..

The notion of folk‐psychology as a primitive speculative theory of the mental is called into question. There is cause to believe that folk‐psychology has more in common with a naive physics than with early speculative physical theorising. The distinction between these is elaborated. The conclusion drawn is that commonsense ascription of psychological content, though not a suitable finishing point for cognitive science, should still provide a more reliable source of data than some contemporary theorists are willing to admit.

Much work in economics, the social sciences, and elsewhere takes as its starting point a somewhat unrealistic conception of rationality — a conception that ignores or downplays both the temporal and the situated aspects of human reason. Biological reason, I shall argue, is better conceived as an iterated process of adaptive response made under extreme time pressure and exquisitely keyed to a variety of external structures and circumstances. These external structures and circumstances act as filters and constraints on the spaces of possible real-time responses. Paramount among such structures and circumstances, in the case of human reason, are the cultural artifacts of language and of social and economic institutions. Models of rational decision making need to situate the reasoning agent as just one element in a complex and time-sensitive feedback system in which such external structures play a major role. It is therefore crucial that we understand the complex and mutually modulatory interplay between individual cognition and the extended environmental loops in which it participates. I shall explore a few potential avenues for developing such an understanding including neural network research and multiple time scale..

The last ten years have seen an increasing interest, within cognitive science, in issues concerning the physical body, the local environment, and the complex interplay between neural systems and the wider world in which they function. “Physically embodied, environmentally embedded” approaches thus loom large on the contemporary cognitive scientific scene. Yet many unanswered questions remain, and the shape of a genuinely embodied, embedded science of the mind is still unclear. I begin by sketching a few examples of the approach, and then raise a variety of critical questions concerning its nature and scope. A distinction is drawn between two kinds of appeal to embodiment: ‘simple’ cases, in which bodily and environmental properties merely constrain accounts that retain the focus on inner organization and processing, and more radical appeals, in which attention to bodily and environmental features is meant to transform both the subject matter and the theoretical framework of cognitive science.

  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 we first present, and then critically assess, a variety of recent anti-representationalist treatments. We suggest that so far, at least, the sceptical rhetoric outpaces both evidence and argument. Some probable causes of this premature scepticism are isolated. Nonetheless, the anti-representationalist challenge is shown to be both important and progressive insofar as it forces us to see beyond the bare representational/non-representational dichotomy and to recognize instead a rich continuum of degrees and types of representationality

1. Throughout the paper, and especially in the section called "LISP vs. DST", I worried that there was not enough focus on EXPLANATION. For the real question, it seems to me, is not whether some dynamical system can implement human cognition, but whether the dynamical description of the system is more explanatorily potent than a computational/representational one. Thus we know, for example, that a purely physical specification can fix a system capable of computing any LISP function. But from this it doesn't follow that the physical description is the one we need to understand the power of the system considered as an information processing device. In the same way, I don't think your demonstration that bifurcating attractor sets can yield the same behavior as a LISP program goes any way towards showing that we should not PREFER the LISP description. To reduce symbolic stories to a subset of DST (as hinted in that section) requires MORE than showing this kind of equivalence: it requires showing that there is explanatory gain, or at the very least, no explanatory loss, at that level. I append an extract from a recent paper of mine that touches on these issues, in case it helps clarify what I am after here

This is the second of two volumes of essays in commemoration of Alan Turing; it celebrates his intellectual legacy within the philosophy of mind and cognitive science. A distinguished international cast of contributors focus on the relationship beteen a scientific, computational image of the mind and a common-sense picture of the mind as an inner arena populated by concepts, beliefs, intentions, and qualia. Topics covered include the causal potency of folk- psychological states, the connectionist reconception of learning and concept formation, the understanding of the notion of computation itself, and the relation between philosophical and psychological theories of concepts.

A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some equivalence class of algorithms/processing strategies so that the common properties highlighted by the chosen class may feature in psychologically interesting accounts. A question arises concerning the type of relation a theorist might expect to hold between such a competence model and a psychologically real processing strategy. Classical work in cognitive science expects the actual processing to depend on explicit or tacit knowledge of the competence theory. Connectionist work, for reasons to be explained, represents a departure from this norm. But the precise way in which a connectionist approach may disturb the satisfying classical symmetry of competence and processing has yet to be properly specified. A standard ?Newtonian? connectionist account, due to Paul Smolensky, is discussed and contrasted with a somewhat different ?rogue? account. A standard connectionist understanding has it that a classical competence theory describes an idealized subset of a network's behaviour. But the network's behaviour is not to be explained by its embodying explicit or tacit knowledge of the information laid out in the competence theory. A rogue model, by contrast, posits either two systems, or two aspects of a single system, such that one system does indeed embody the knowledge laid out in the competence theory