- Tapa blanda: 294 páginas
- Editor: OUP Oxford (28 de enero de 2014)
- Idioma: Inglés
- ISBN-10: 0199686734
- ISBN-13: 978-0199686735
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- Clasificación en los más vendidos de Amazon: nº86.235 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
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The Predictive Mind (Inglés) Tapa blanda – 28 ene 2014
Descripción del producto
How does your brain generate accurate perceptual experiences? How does it initiate action? How does it do virtually everything else it does? Jakob Hohwy's book provides an ambitious, controversial answer ... I predict that Hohwy's book will be an important part of the discussion. (Jona Vance, Notre Dame Philosophical Reviews)
This is a wonderful and deep book. I have heard it said that it heralds a paradigm shift in cognitive neuroscience and perhaps neurophilosophy. It is an eloquent and accessible synthesis of recent advances in theoretical neurobiology, as they apply to the human brain and mind. I confess that I had thought about writing a book addressing the more technical themes but having read The Predictive Mind, I feel curiously complacent and content, because this book says everything that needed to be said and much more. (Karl Friston, University College London)
Every now and then a book appears that looks set to be a milestone in the interdisciplinary study of mind. This is one of those rare and important books. The core organizing principle of mentality itself, Hohwy persuasively argues, is the prediction of our own ongoing streams of sensory input. Hohwy applies this principle to cases ranging from simple sensing all the way to hallucinations, delusions, consciousness, emotion, the sense of presence, and the nature of the self. A wonderful, timely, ground-breaking treatment, and required reading for anyone interested in the nature and possibility of mind. (Andy Clark FRSE, Professor of Logic and Metaphysics, University of Edinburgh)
Reseña del editor
A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind is that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it.
The first part of the book sets out how the theory enables rich, layered perception. The theory's probabilistic and statistical foundations are explained using examples from empirical research and analogies to different forms of inference. The second part uses the simple mechanism in an explanation of problematic cases of how we manage to represent, and sometimes misrepresent, the world in health as well as in mental illness. The third part looks into the mind, and shows how the theory accounts for attention, conscious unity, introspection, self and the privacy of our mental world.
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In this framework, following its early originator, Helmholtz, "...perceptions are regarded as similar to the predictive hypotheses of science, but are psychologically projected into external space and accepted as our most immediate reality." Thus the "problem of perception," as Hohwy argues at the start of the book, is how the right hypothesis about the world is shaped and selected. The brain is seen as maintaining a generative model of the world, an internal mirror of nature that recapitulates the causal structure of the world, and prediction error is minimized relative to the model's expected (hypothesized) states. In an inversionary twist, perceptual inference is always trying to use its prior knowledge to predict and suppress (yes, suppress) the sensory input the system is receiving. Thus, rather than the "bottom-up" data (received from the external world) being used as the material to create the perception, it is the predictive model best fitting or minimizing the error that is actually perceived.
Here I must add some critical observations. What this internal predictive model could actually look like, embodied as it must be in chemical-neural flows, how it could possibly look (in the brain) like my experience of the kitchen with its table, its chairs, and my hand with spoon stirring coffee in a cup on the table, and how this internal model is "projected into space," i.e., how, from this internal, neural-based "hypothesis" we obtain an image of the external world - these are questions one instantly (and impatiently) presumes that the book will quickly deliver on. But in an unfortunate structural aspect of the book, we wait until about 200 pages in to hit the disclaimer that, "this is not intended as a proposal that can explain why perceptual states are phenomenally conscious rather than not," that this is merely, "a proposal that describes the states that are conscious... [via] those representations of the world that currently best predict the sensory input," and that it "does not intend to touch the hard problem" (i.e., Chalmers' hard problem of consciousness).
Clearly, this Bayesian framework is at best a partial answer; it is a computational piece of the puzzle, but just a computational piece. There is no true, concrete dynamics involved - where by concrete dynamics, I mean a dynamics as concrete as that of an AC motor generating a field of force. Computations alone cannot account for consciousness. While clearly aware of this, at least to some extent, Hohwy yet tries to take the framework into explaining Searle's Chinese Room, i.e., how such a Bayesian network could account for the conscious understanding of an event which is created via the mediating device of a string of linguistic symbols - "The man stirred the coffee with the spoon." But he does not acknowledge that beneath this sentence-created event perception, lies the same difficulty as that beneath the hard problem of explaining the origin of the image of the kitchen, chairs and coffee-stirring spoon.
Events. While Hohwy describes the earlier-mentioned "causal structure of the world" (captured/recapitulated in these hypotheses) in terms of invariance (regularities) which exist over various scales of time, the discussion of these is very abstract, the concept of invariance seeming very limited - almost entirely in terms of "causal regularities," e.g., dropping an egg to the floor => a broken egg. Missing is any reference to ecological psychology and J. J. Gibson, a discipline and theory where the regularities have a precise mathematical structure - texture gradients, gradients over velocity flows, tau ratios, adiabatic ratios, inertial tensors, etc. Strangely missing too is any reference to (or attempt at integration with) the premier Bayesian model of the perception of dynamic form (Weiss, Simoncelli and Adelson, Nature Neuroscience, 2002), a model simply and concretely employing mathematically specified constraints (priors) upon estimates of the optical velocity flows of Gibson (for a review, "On Time, Memory and Dynamic Form," Consciousness and Cognition, 2004). All of these - the flow fields, inertial tensors, adiabatic ratios, etc - comprise the structure of invariance defining even the little event of "coffee stirring." It is a structure where the invariants are defined over time - an extended, flowing time. As Gibson argued, these invariants cannot exist in an "instant" or be transmitted as "bits" over the nerves. It is this dynamic structure over time that is arriving in the brain as the "bottom up" information - to be compared against an operative hypothesis and suppressed. And...it is this very same dynamic structure that would have to be incorporated within - would have to define - this operative "hypothesis" of the stirring event that is being "projected" as our experience. What would the "matching" or comparison process (this is in reality a "comparator" model) of these two dynamic structures (hypothesis vs. the invariance structure of the dynamically changing external event) possibly look like? How are such structures - intrinsically dynamic flows - stored in the brain? Or can they be? How are such events retrieved, memory-wise, to become "an hypothesis?"
The invariance laws/structure defining events, the deep problem of the elementary memory that supports our perception of time flowing events such as the stirring spoon (which is a problem of the nature of time itself), the problem of storage of such events in the brain (there is no actual theory of event storage), the mechanism for their retrieval - these are just some of many subjects that need to be addressed in this conception. In a word, the predictive brain, despite its promise, or indeed because of it, will need to get very serious about the actual nature of its own hypotheses.
And so, Hohwy and scholars such as Andy Clark in his book "Surfing Uncertainty" (Oxford University Press, 2016) which also does not cite Powers' in its "References" seem to have gone on their own tangents of explanation and story [as Clark states] of the brain as a prediction machine, while ignoring the apparently more straightforward and highly developed PCT explanation and model of why we do the things we do (i.e., to control our perceptions so that they match important references [goals, purposes, homeostatic set points, ...] that we have as much as possible).
These assume you're on a budget and have to choose one or the other to get up to speed on recent developments in PP theory. Other reviews, including of Clark, make the field of PP seem new, however robotic kinematics at Carnegie Mellon where I've done work in computational robotics were looking at Bayesian prediction in neural nets 15 years ago as a way of making robotic kinematics more precise-- ironically using probability made the visual/next step programming loops and feedback MORE realistic and accurate even back then vs. the earlier models of direct, linear, analog, vision-to-step deterministic programming.
This is relevant to both books, as both ground much of the new work in popular body/brain feedback flows, particularly since the perception and memory sides are still far less understood than kinematics. Hohwy is an easier read than Clark, however neither get into specific neural net code examples or stochastic formulas. I honestly think reading both would be a treasure, as there are plenty of areas that both overlap and complement each other, but if your budget only allows one, Clark is the way to go IMHO in uncomplicated, bank for the buck terms, though Clark requires a more dedicated reader attention investment than Jakob.