Andy Clark

The engine of reason, the seat of the soul: a philosophical journey into the brain, Paul M. Churchland. Cambridge: Bradford Books, MIT Press, 1995 ISBN: 0–262–03244–4Cognition in the wild, Edwin Hutchins. Cambridge, MA: MIT Press, 1995. ISBN: 0–262–08231–4Dimensions of creativity, Margaret A. Boden, (Ed.) Cambridge, MA: MIT Press, 1994 ISBN 0–262–02368–7Contemplating minds: a forum for Artificial Intelligence, William J. Clancey, Stephen W. Smoliar & Mark J. Stefik (Eds) Cambridge: Bradford Books, MIT Press, 1994 ISBN 0–262–53119–4Passion and reason: making sense of our emotions, Richard S. Lazarus & Bernice N. Lazarus. Oxford: Oxford University Press, 1994. ISBN 0–19–508757–7Mental reality, Galen Strawson. Cambridge, MA: MIT Press, 1994 ISBN 0–262–19352–3The body and the self, Jose Luis Bermudez, Anthony Marcel & Naomi Eilan (Eds) Cambridge, MA: MIT Press, 1995. ISBN 0–262–02386–5.

First, a theoretical background to the volume’s topic, extended epistemology, is provided by a brief outline of its cross-disciplinary theoretical lineage and some key themes. In particular, it is shown how and why the emergence of recent and more egalitarian thinking in the cognitive sciences about the nature of human cognizing and its bounds—viz., the so-called ‘extended cognition’ program, and the related idea of an ‘extended mind’—has important and interesting ramifications in epistemology. Second, an overview is provided of the papers included as chapters in the volume. The sixteen contributions are divided into two categories: those that engage with foundational issues to do with extended epistemology, and those that pursue applications of extended epistemology to new areas of research.

Can what we know change what we see? Does language affect cognition and perception? The last few years have seen increased attention to these seemingly disparate questions, but with little theoretical advance. We argue that substantial clarity can be gained by considering these questions through the lens of predictive processing, a framework in which mental representations—from the perceptual to the cognitive—reflect an interplay between downward-flowing predictions and upward-flowing sensory signals. This framework provides a parsimonious account of how what we know ought to change what we see and helps us understand how a putatively high-level trait such as language can impact putatively low-level processes such as perception. Within this framework, language begins to take on a surprisingly central role in cognition by providing a uniquely focused and flexible means of constructing predictions against which sensory signals can be evaluated. Predictive processing thus provides a plausible mechanism for many of the reported effects of language on perception, thought, and action, and new insights on how and when speakers of different languages construct the same “reality” in alternate ways.

Strange inversions occur when things work in ways that turn received wisdom upside down. Hume offered a strangely inverted story about causation, and Darwin, about apparent design. Dennett suggests that a strange inversion also occurs when we project our own reactive complexes outward, painting our world with elusive properties like cuteness, sweetness, blueness, sexiness, funniness, and more. Such properties strike us as experiential causes, but they are really effects—a kind of shorthand for whole sets of reactive dispositions rooted in the nuts and bolts of human information processing. Understanding the nature and origins of that strange inversion, Dennett believes, is thus key to understanding the nature and origins of human experience itself. This paper examines this claim, paying special attention to recent formulations that link that strange inversion to the emerging vision of the brain as a Bayesian estimator, constantly seeking to predict the unfolding sensory barrage.

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness.

Recent work in cognitive and computational neuroscience depicts the brain as in some sense implementing probabilistic inference. This suggests a puzzle. If the processing that enables perceptual experience involves representing or approximating probability distributions, why does experience itself appear univocal and determinate, apparently bearing no traces of those probabilistic roots? In this paper, I canvass a range of responses, including the denial of univocality and determinacy itself. I argue that there is reason to think that it is our conception of perception itself that is flawed. Once we see perception aright, as the slave of action, the puzzlement recedes. Perceptual determinacy reflects only the mundane fact that we are embodied, active, agents who must constantly engage the world they perceptually encounter.

The use of forward models is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model account, forward models lie at the heart of all forms of perception and action. We compare these neighbouring but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches.

Many models of cognitive ability and disability rely on the idea of cognition as abstract reasoning processes implemented in the brain. Research in cognitive science, however, emphasizes the way that our cognitive skills are embodied in our more basic capacities for sensing and moving, and the way that tools in the external environment can extend the cognitive abilities of our brains. This chapter addresses the implications of research in embodied cognition and extended cognition for how we think about cognitive impairment and rehabilitation, how cognitive reserve mitigates neural impairment, and the distinction between medical and social models of disability.

Andy Clark is currently Professor of Philosophy and Director of the Philosophy/Neuroscience/Psychology program at Washington University in St. Louis, Missouri. He is the author of two books MICROCOGNITION (MIT Press/Bradford Books 1989) and ASSOCIATIVE ENGINES (MIT Press/Bradford Books, 1993) as well as numerous papers and four edited volumes. He is an ex- committee member of the British Society for the Philosophy of Science and of the Society for Artificial Intelligence and the Simulation of Behavior. Awards include a visiting Fellowship at the Australia National University, Canberra in 1989, and an ESRC Senior Research Leave Fellowship in 1992. He is an Associate of BEHAVIORAL AND BRAIN SCIENCES, serves on the editorial boards of the journals.

The use of forward models is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model account, forward models lie at the heart of all forms of perception and action. We compare these neighbouring but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches.