Andrew Clark

How should we characterize the functional role of conscious visual experience? In particular, how do the conscious contents of visual experience guide, bear upon, or otherwise inform our ongoing motor activities? According to an intuitive and philosophically influential conception, the links are often quite direct. The contents of conscious visual experience, according to this conception, are typically active in the control and guidance of our fine-tuned, real-time engagements with the surrounding three-dimensional world. But this idea is hostage to empirical fortune. It is a hostage, moreover, whose safety is in serious doubt. Thus Milner and Goodale argue for a deep and abiding dissociation between the contents of conscious seeing, on the one hand, and the resources used for the on-line guidance of visuo-motor action, on the other. This ‘dual visual systems’ hypothesis, which finds many echoes in various other bodies of cognitive scientific research, poses a prima facie challenge to the Assumption of Experience-Based Control. More importantly, it provides fuel for an alternative and philosophically suggestive account of the functional role of conscious visual experience.

  Recent work in biology and cognitive science depicts a variety of target phenomena as the products of a tangled web of causal influences. Such influences may include both internal and external factors as well as complex patterns of reciprocal causal interaction. Such twisted tales are sometimes seen as a threat to explanatory strategies that invoke notions such as inner programs, genes for and sometimes even internal representations. But the threat, I shall argue, is more apparent than real. Complex causal influence, in and of itself, provides no good reason to reject these familiar explanatory notions. To believe otherwise, I suggest, is generally to commit (at least) one of two seductive errors. The first error is to think that the general notion of a state x coding for an outcome y involves the state's constituting a full description of y. This is what I call the myth of the self-contained code. The second error is to think that the practice of treating certain factors as special (e.g., seeing genes as coding for outcomes in a way environmental factors do not) depends on the (often mistaken) belief that the singled out factor is somehow doing the most real work. Where the amounts of causal influence are evenly spread, it is assumed there can be no reason to treat one factor in a special way. This is what I term the Myth of Explanatory Equality. Avoiding these errors involves reminding ourselves of (1) the rich context-dependence of even standard, unproblematic uses of the notions of code, program and information content (all three make sense only relative to an assumed ecological backdrop) and (2) the difference between explaining why an event occurred and displaying the full workings of a complex causal system

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat behaviors to language acquisition. Not only are such problems rife – they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding biases so as to reduce specific type-2 problems to type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy – namely, the maximal exploitation of states of representation already achieved by prior, simpler learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription”. In addition, the most distinctive features of human cognition – language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale.

Abstract Cognitive Science, it is argued, comprises two distinct projects. One is an Engineering project whose goal is understanding the in?the?head computational activities which ground intelligent behaviour. The other is a Descriptive project whose goal is the mapping of relations between thoughts as ascribed using the (sentential) apparatus of the propositional attitudes. Some theorists (e.g. Fodor, 1987) insist that the two projects are (in a sense to be explained) deeply related. This view is contested, and the consequences of its abandonment examined. Such consequences are seen to include (i) the irrelevance of scientific arguments for Eliminative Materialism, (ii) a view concerning the proper roles of classical and connectionist work in Artificial Intelligence and (iii) the failure of an allegedly damning argument against connectionism (the so?called systematicity argument)

Selinger and Engstrom, A moratorium on cyborgs: Computation, cognition and commerce, 2008 (this issue) urge upon us a moratorium on ‘cyborg discourse’. But the argument underestimates the richness and complexity of our ongoing communal explorations. It leans on a somewhat outdated version of the machine metaphor (exemplified perhaps by a frozen 1970’s Cyborg). The modern cyborg, informed by an evolving computational model of mind, can play a positive role in the critical discussions that Selinger and Engstrom seek.

Where does the mind stop and the rest of the world begin? The question invites two standard replies. Some accept the demarcations of skin and skull, and say that what is outside the body is outside the mind. Others are impressed by arguments suggesting that the meaning of our words "just ain't in the head", and hold that this externalism about meaning carries over into an externalism about mind. We propose to pursue a third position. We advocate a very different sort of externalism: an _active externalism_, based on the active role of the environment in driving cognitive processes

Recent studies such as Thelen and Smith, Kelso, Van Gelder, Beer, and others have presented a forceful case for a dynamical systems approach to understanding cognition and adaptive behavior. These studies call into question some foundational assumptions concerning the nature of cognitive scientific explanation and the role of notions such as internal representation and computation. These are exciting and important challenges. But they must be handled with care. It is all to easy in this debate to lose sight of the explanatorily important issues and to talk at cross purposes, courtesy of the various ways in which different theorists often concieve key terms.

Mind, it has recently been argued1, is a thoroughly temporal phenomenon: so temporal, indeed, as to defy description and analysis using the traditional computational tools of cognitive scientific understanding. The proper explanatory tools, so the suggestion goes, are instead the geometric constructs and differential equations of Dynamical Systems Theory. I consider various aspects of the putative temporal challenge to computational understanding, and show that the root problem turns on the presence of a certain kind of causal web: a web that involves multiple components (both inner and outer) linked by chains of continuous and reciprocal causal influence. There is, however, no compelling route from such facts about causal and temporal complexity to the radical anti- computationalist conclusion. This is because, interactive complexities notwithstanding, the computational approach provides a kind of explanatory understanding that cannot (I suggest) be recreated using the alternative resources of pure Dynamical Systems Theory. In particular, it provides a means of mapping information flow onto causal structure -- a mapping that is crucial to understanding the distinctive kinds of flexibility and control characteristic of truly mindful engagements with the world. Where we confront especially complex interactive causal webs, however, it does indeed become harder to isolate the syntactic vehicles required by the computational approach. Dynamical Systems Theory, I conclude, may play a vital role in recovering such vehicles from the burgeoning mass of real-time interactive complexity

Is consciousness all in the head, or might the minimal physical substrate for some forms of conscious experience include the goings on in the (rest of the) body and the world? Such a view might be dubbed (by analogy with Clark and Chalmers’s ( 1998 ) claims concerning ‘the extended mind’) ‘the extended conscious mind’. In this article, I review a variety of arguments for the extended conscious mind, and find them flawed. Arguments for extended cognition, I conclude, do not generalize to arguments for an extended conscious mind

[Andy Clark] What is the relation between perceptual experience and the suite of sensorimotor skills that enable us to act in the very world we perceive? The relation, according to 'sensorimotor models' is tight indeed. Perceptual experience, on these accounts, is enacted via skilled sensorimotor activity, and gains its content and character courtesy of our knowledge of the relations between movement and sensory stimulation. I shall argue that this formulation is too extreme, and that it fails to accommodate the substantial firewalls, dis-integrations, and special-purpose streamings that form the massed strata of human cognition. In particular, such strong sensorimotor models threaten to obscure the computationally potent insensitivity of key information-processing events to the full subtleties of embodied cycles of sensing and moving. /// [Naomi Eilan] The strong sensorimotor account of perception gives self-induced movements two constitutive roles in explaining visual consciousness. The first says that self-induced movements are vehicles of visual awareness, and for this reason consciousness 'does not happen in the brain only'. The second says that the phenomenal nature of visual experiences is consists in the action-directing content of vision. In response I suggest, first, that the sense in which visual awareness is active should be explained by appeal to the role of attention in visual consciousness, rather than self-induced movements; and second, that the sense in which perceptual consciousness does not happen in the brain only should be explained by appeal to the relational nature of perceptual consciousness, appeal to which also shows why links with action cannot exhaust phenomenal content.

  Marinov''s critique I argue, is vitiated by its failure to recognize the distinctive role of superposition within the distributed connectionist paradigm. The use of so-called subsymbolic distributed encodings alone is not, I agree, enough to justify treating distributed connectionism as a distinctive approach. It has always been clear that microfeatural decomposition is both possible and actual within the confines of recognizably classical approaches. When such approaches also involve statistically-driven learning algorithms — as in the case of ID3 — the fundamental differences become even harder to spot. To see them, it is necessary to consider not just the nature of an acquired input-output function but the nature of the representational scheme underlying it. Differences between such schemes make themselves best felt outside the domain of immediate problem solving. It is in the more extended contexts of performance DURING learning and cognitive change as a result of SUBSEQUENT training on new tasks (or simultaneous training on several tasks) that the effects of superpositional storage techniques come to the fore. I conclude that subsymbols, distribution and statistically driven learning alone are indeed not of the essence. But connectionism is not just about subsymbols and distribution. It is about the generation of whole subsymbol SYSTEMS in which multiple distributed representations are created and superposed

O'Regan and Noe present a wonderfully detailed and comprehensive defense of a position whose broad outline we absolutely and unreservedly endorse. They are right, it seems to us, to stress the intimacy of conscious content and embodied action, and to counter the idea of a Grand Illusion with the image of an agent genuinely in touch, via active exploration, with the rich and varied visual scene. This is an enormously impressive achievement, and we hope that the comments that follow will be taken in a spirit of constructive questioning. Overall, we have two main reservations.

  The paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to miss the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we see exactly how the solution works, however, it becomes clear that it is limited to a special class of cases in which (1) statistically driven undersampling is (luckily) equivalent to task decomposition, and (2) the dangers of unlearning are somehow being minimized. The technique is suggestive nonetheless, for a variety of developmental factors may yield the functional equivalent of both statistical AND informed undersampling in early learning

Recent advances in cognitive science and cognitive neuroscience open up new vistas for human enhancement. Central to much of this work is the idea of new human-machine interfaces (in general) and new brain-machine interfaces (in particular). But despite the increasing prominence of such ideas, the very idea of such an interface remains surprisingly under-explored. In particular, the notion of human enhancement suggests an image of the embodied and reasoning agent as literally extended or augmented, rather than the more conservative image of a standard (non-enhanced) agent using a tool via some new interface. In this essay, I explore this difference, and attempt to lay out some of the conditions under which the more radical reading (positing brand new integrated agents or systemic wholes) becomes justified. I adduce some empirical evidence suggesting that the radical result is well within our scientific reach. The main reason why this is so has less to do with the advancement of our science (though that certainly helps) than with our native biological plasticity. We humans, I shall try to show, are biologically disposed towards literal (and repeated) episodes of sensory re-calibration, of bodily re-configuration and of mental extension. Such potential for literal and repeated re-configuration is the mark of what I shall call "profoundly embodied agency," contrasting it with a variety of weaker (less philosophically and scientifically interesting) understandings of the nature and importance of embodiment for minds and persons. The article ends by relating the image of profound embodiment to some questions (and fears) concerning converging technologies for improving human performance.

Mind, it is increasingly fashionable to assert, is an intrinsically embodied and environmentally embedded phenomenon. But there is a potential tension between two strands of thought prominent in this recent literature. One of those strands depicts the body as special, and the fine details of a creature’s embodiment as a major constraint on the nature of its mind: a kind of new-wave body-centrism. The other depicts the body as just one element in a kind of equal-partners dance between brain, body and world, with the nature of the mind fixed by the overall balance thus achieved: a kind of extended functionalism (now with an even broader canvas for multiple realizability than ever before). The present paper displays the tension, scouts the space of possible responses, and ends by attempting to specify what the body actually needs to be, given its complex role in these recent debates.

Fodor’s theory makes thinking prior to doing. It allows for an inactive agent or pure reflector, and for agents whose actions in various ways seem to float free of their own conceptual repertoires. We show that naturally evolved creatures are not like that. In the real world, thinking is always and everywhere about doing. The point of having a brain is to guide the actions of embodied beings in a complex material world. Some of those actions are, to be sure, more recondite than others. But in every case the contents of thoughts still look to depend, in some non-unique but vitally important way, on the kinds of doings they support

What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). In the present treatment I explore a third option, which I shall call the "complementarity" view of language. According to this third view the actual symbol structures of a given language add cognitive value by complementing (without being replicated by) the more basic modes of operation and representation endemic to the biological brain. The "cognitive bonus" that language brings is, on this model, not to be cashed out either via the ultimately mysterious notion of "thinking in a given natural language" or via some process of exhaustive translation into another inner code. Instead, we should try to think in terms of a kind of coordination dynamics in which the forms and structures of a language qua material symbol system play a key and irreducible role. Understanding language as a complementary cognitive resource is, I argue, an important part of the much larger project (sometimes glossed in terms of the "extended mind") of understanding human cognition as essentially and multiply hybrid: as involving a complex interplay between internal biological resources and external non-biological resources

In the movie, Memento, the hero, Leonard, suffers from a form of anterograde amnesia that results in an inability to lay down new memories. Nonetheless, he sets out on a quest to find his wife’s killer, aided by the use of notes, annotated polaroids, and (for the most important pieces of information obtained) body tattoos. Using these resources he attempts to build up a stock of new beliefs and to thus piece together the puzzle of his wife’s death. At one point in the movie, a character exasperated by Leonard’s lack of biological recall, shouts