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How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics.
In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation.
- Sales Rank: #82297 in Books
- Published on: 2015-11-03
- Original language: English
- Number of items: 1
- Dimensions: 6.40" h x 1.20" w x 9.40" l, .0 pounds
- Binding: Hardcover
- 424 pages
Review
"A stimulating read for anyone interested in the intersection of neuroscience and philosophy of mind from a scientific perspective." --Library Journal
"A wonderful book...Clark's Surfing Uncertainty will become an essential point of departure for philosophers and cognitive scientists trying to come to grips with the apparatus of predictive processing." -- Metascience
About the Author
Andy Clark is Professor of Logic and Metaphysics in the School of Philosophy, Psychology and Language Sciences, at Edinburgh University in Scotland. He is the author of Being There: Putting Brain, Body And World Together Again (1997), Mindware (OUP, 2nd Edition 2014), Natural-Born Cyborgs: Minds, Technologies and the Future of Human Intelligence (OUP, 2003), and Supersizing the Mind: Embodiment, Action, and Cognitive Extension (OUP, 2008). His interests include artificial intelligence, embodied cognition, robotics, and the predictive mind.
Most helpful customer reviews
16 of 16 people found the following review helpful.
Surfing - Rather, a Very Deep Dive
By Stephen E. Robbins
One has to admire and appreciate Clark. This work represents a tremendous effort at bringing together the many developments in, or related to, the emerging Predictive Processing framework (PP). Though familiar with some of this literature, I was surprised at the scope, depth and breadth of the research efforts in this framework that have been occurring over the years.
The PP framework argues that there is, in the brain, both an up-flow of information from the external world, and most significantly, a down-flow of predictive information, with the predictive information flow actually having priority (suppressing the up-flow), though this priority is constrained by the degree of error in the match between the two flows. The predictions in this neural network/computational framework are in essence probabilistic Bayesian priors, and in one of the significant developments Clark describes, networks/algorithms are being created that learn these priors from the incoming information, thus solving an important bootstrap problem as to how this predictive process gets going. The book goes into the many perceptual phenomena – numerous illusions – that PP can be extended to explain, and the deep ins and outs of this story. Clark devotes a great deal to developing the crucial role of bodily action in this picture – how it is integrally part of PP flows. Throughout, Clark is continually relating things to the many other theorists in or related to this subject – a welcome point of tie-in. Of interest to me were his attempts to tie/reconcile all this to the “enactive” theorists such as Varela, as well as to Gibson’s theory of affordances and the structure of perceptual information. Indeed, Clark describes how PP can be seen/read as supporting the creation of multiple simultaneous “priors” relating to possible affordances (actions) re any given perceptual scene.
Clark is well aware that this framework is no solution to the problem of perception, i.e., what I like to term the origin of the image of the external world. We have two neural or chemical-neural flows – up and down – neither of which look anything like Clark’s example coffee cup on the kitchen table, i.e., like our actual experience of the external world. The down-flowing “prior” for a coffee cup is, nevertheless, only neural firings. He chides PP theorist Jacob Hohwy for arguing that this framework can indeed explain the origin of our experience, as Hohwy invokes the notion that from these flows emerges a “virtual image” of the coffee cup and table. If Clark tends to sin, it is that he slips continually into over-enthusiastic language, e.g., PP is, “…a compelling ‘cognitive package deal’ in which perception, imagination, understanding, reasoning, and action are co-emergent…” If one has no theory of the origin of our image of the external world, one wonders how one can claim a theory of perception? And given no theory of perception, then a theory of memory – of how this perceived experience is stored - is ungrounded (indeed, there is no accepted theory), and given this, how can we be saying we are actually explaining imagination – something which relies on invoking this experience? Yet one finds this type of language throughout the book.
Just to give an idea of where the limitations might lie with which PP and Clark struggle, let me place PP and Clark within another perspective for a moment. PP is an impressive computational framework, but mere, abstract computations cannot account for consciousness – that image of the coffee cup, "out there," on the table, with spoon stirring away. For this, a real, concrete dynamics is needed. As an example of such, consider Bergson’s concept, described in his “Matter and Memory,” 1896. Bergson, to the detriment of his contemporaries understanding him, had presciently anticipated the essence of Gabor’s discovery of holography by fifty years, as well as Bohm’s later generalization of the holographic to the universal field (The Implicate Order, 1980). But contrary to anyone who later tried to employ the holographic framework, e.g., Pribram, what Bergson had argued, in essence, is that the brain is creating (forms globally, across the totality of its processing) a modulated reconstructive wave passing through the external, holographic field and specific to, or specifying, a source of information in the field, and by this process now an image of a subset of the field – the coffee cup and spoon. We are explaining then how perception is limited, not how it arises. The selection of information from the “hologram” is based on its relation to action - to the body’s action capability - and thus, per Bergson, perception is “virtual action.” This is where the “multiple potential affordances as priors” would fit, but this is a very concrete dynamics supported/created by the brain – a concrete wave – as concrete as an AC motor generating a field of electric force. It is within this concrete dynamical structure that the entire PP model and its computations would somehow have to be incorporated as a part – certainly not the whole – of the story. And as perception, in this framework, is not occurring solely within the brain (the specification is to events/objects within the external field, right, for example, where the coffee cup indicates it is), experience cannot be solely stored there (perhaps the source of our theoretical difficulty on storing experience?). A new model of memory retrieval would be required.
Nevertheless, exception above noted, I consider Clark’s work an invaluable and to-be-appreciated source for getting up to speed on the current state of the art and theory in cognitive science and robotics.
7 of 7 people found the following review helpful.
Riding the wave of predictive processing
By Steve Benner
Andy Clark is Professor of Logic and Metaphysics in the School of Philosophy, Psychology and Language Sciences at Edinburgh University, as well as author of several books on the workings of the human brain and cognitive science in general ("Being There: Putting Brain, Body, and World Together Again", "Supersizing the Mind: Embodiment, Action, and Cognitive Extension", "Mindware: An Introduction to the Philosophy of Cognitive Science", etc). His latest book, "Surfing Uncertainty: Prediction, Action and the Embodied Mind", expands and updates the ideas contained in his earlier books by reviewing the latest thinking with regard to human cognitive processing that is emerging from studies at the intersection of neuroscience, psychology, philosophy, artificial intelligence and robotics. The book expounds and develops a predictive processing ('PP') model for brain functioning which posits human cognition as the product of a hierarchical Bayesian predictive coding engine, endlessly generating probabilistic generative models matched against context-variable precision-weighted input streams -- contributed by the body's exteroceptive, interoceptive and proprioceptive sensing systems -- in a complex cycle of top-down predictions and precision estimations, modulated by a counter-flow of bottom-up residual errors. In short, this model suggests that we perceive the world by first predicting how it should be, based on a series of prior expectations and understandings, using our senses to test the correctness of this prediction and iteratively refining our prediction as necessary to minimise the errors. Only when our cognitive systems finally report that top-down predictions satisfactorily match bottom-up observations does the brain's latest refined view register as our current perception of the world and our state within it.
Central to this model is a view of the brain's role as very different from that traditionally assigned to it by many cognitive scientists. Rather than have the brain as a passive observer, the PP process requires the brain to take on a much more proactive role, not only constantly predicting its most likely sensory input but also driving the body towards the best position to gather that input. The essential feature here is that this positions the brain as a central agency for action and engagement with the world, ensuring that the human animal is best positioned to "surf the uncertainties" of our sensory arrays, not only for survival in a world by avoiding its hazards and exploiting its opportunities, but also for structuring the world itself and altering the very things with which our brains must engage and predict.
What emerges in this book is an elegant and stunningly unified (as well as simplifying) vision of the way the body's cognitive system functions, able not only to model with great precision many of the empirically observed characteristics of human cognition and the way our bodies interact with the world so fluently but also to explain very neatly features such as the autistic spectrum and to shed light on mental illnesses such as schizophrenia. It illuminates other facets of the human condition also, such as dreaming, imagination and the creative urge. And it makes perfect sense from an evolutionary perspective, too. It should be stressed that these ideas are not entirely new, nor are they the author's own: PP is a model of cognitive processing that has been gaining traction for some time now -- for an earlier position of the field, see Jakob Hohwy's 2014 volume, "The Predictive Mind", for instance -- but Andy Clark's survey here is more comprehensive and deeper delving than most others on the subject.
Given its weighty, highly theoretical subject matter, this book is not surprisingly a fairly heavy read. Although the author does his best to keep his text pitched as much for a lay readership as for an expert one, the sheer complexity of many of the ideas and notions that he covers necessitates a certain level of reliance on jargon and the employment of precise technical language; the result otherwise would undoubtedly have been an undesirable degree of ambiguity and vagueness that would have served no purpose whatsoever. The book can be read by an interested lay reader but will still require considerable investment of effort if anything is to be gained from it.
The serious student of the subject should find within its pages an excellent position statement on the current development of PP theories, together with a cogent and succinct analysis of its current shortcomings (few) and useful directions for future investigative activity. Particularly welcome also are the 75 pages of additional notes, appendix and full references, as well as comprehensive index.
4 of 4 people found the following review helpful.
Extremely thorough, but a bit shallow in places once you get the general principle
By Eli Gottlieb
Basically, Clark spends the whole book giving a vast overview of the probabilistic turn in neuroscience and cognitive science, emphasizing everywhere how the brain does not merely estimate probability densities of its observables, but also estimates the expected precisions of its actual observations. This helps Clark and the probabilistic mind-scientists to unify a truly astounding amount of experimental data and theoretical machinery.
Clark emphasizes the work of the Friston and Hohwy labs in neuroscience, and occasionally the Tenenbaum and Gershman labs in computational cognitive science. Clark also relentless emphasizes the embodied mind: how based on estimated precisions, the brain learns and recruits chiefly "action-oriented" probability models (or in other words, models which infer an action directly from sensory data, without necessarily encoding a causal reconstruction of the world from sensory data that accounts for all causal structure). In less technical language, the brain uses simpler representations when it expects its experiences to be noisier.
Once you start to understand the general principle, though, you end up realizing the dive into the work is slightly shallow. While too many equations would have bogged down the book, I don't feel like I even remotely understand how precision-weighting can actually work based on the total lack of equations (and graphs, etc) given. Please just show me an equation and a graph to give some intuition for how nonparametric (arbitrarily complicated) probabilistic inference can include estimates of precision. It's easy to assume a normal curve and then infer the precision as a parameter, but what about all the real-world quantities the brain handles that aren't normal curves?
I did, however, especially appreciate the discussion of how precision-weighted probabilistic inference plays into the current view of several major mental illnesses. As the family and friend of some sufferers, I'm pleasantly surprised to find out computational psychiatry is even a thing!
For the deeper look at how specific cognition problems get solved in this framework, I've ended up purchasing "The Predictive Mind" by Jacob Hohwy. We'll see how that goes.
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