This book consists of six chapters, four of which are versions of material that can be found elsewhere. For three reasons, however, the book is essential reading for any philosopher with a serious interest in the metaphysics of mind: first, after several decades of often rather diffident reflection on the mind-body problem, Kim, our most distinguished metaphysician of mind, finally tells us, courteously but firmly, his considered view of the matter; secondly, that view swims against the prevailing tide by rejecting physicalism; thirdly, one of the new chapters ch. I turn at once to critical discussion of this stimulating and enjoyable book's most important though not its only important line of thought.
Although each chapter is meant to be and is capable of being read alone, the six chapters are also meant to fit together to constitute a sustained argument for Kim's main conclusion. That conclusion is that physicalism about the mind is false but very nearly true: it's probably true of propositional attitudes, but almost certainly false of phenomenal properties i. Kim's case for the positive part of this conclusion about the attitudes is rather jejune, however, and so I will quickly pass it by.
Its core is the claim that propositional attitudes must be functionally reducible because it would be incoherent not to ascribe propositional attitudes to systems functionally and behaviorally indistinguishable to us p. But the literature already contains conceivable cases e. So I doubt philosophers who worry that the original intentionality of propositional attitudes resists physicalist analysis will find anything in this book to persuade them otherwise. Kim's case for the non-physical character of phenomenal properties, on the other hand, is full of interest. A key part of that case pp.
This argument can only be part of Kim's case, however, because its conclusion, 4 , doesn't quite say that physicalism is false of phenomenal properties: perhaps phenomenal properties are functionally irreducible to the physical, as 4 says, and yet stand in some weaker relation to the physical that nonetheless suffices for their physicalistic acceptability. But, unless I am very much mistaken, Kim never explains how to take the extra step from 4 to the falsity of physicalism, even though he surely takes it. Here is one passage in which the step is taken:.
So qualia are not functionalizable, and hence physically irreducible. Qualia, therefore, are the "mental residue" that cannot be accommodated within the physical domain. This means that global physicalism is untenable. Intrinsic qualities of qualia are not functionalizable and therefore are irreducible, and hence causally impotent. They stay outside the physical domain … p. Speculation about what inference Kim had in mind in these passages is impeded by the fact that, again unless I am very much mistaken, he never gives an explicit account of what it would take for a mental property to "be accommodated within the physical domain", i.
However, he does remark without commentary that physicalism entails that "any phenomenon of the world can be physically explained if it can be explained at all" p. So perhaps Kim intends us to combine 4 with the following further premises:. For 4 , 5 , 6 , and 7 do entail that phenomenal properties can't be accommodated within the physical domain.
However, what I really want to discuss in this review is the argument consisting of claims 1 through 4. Kim's defense of 3 is, as he acknowledges, familiar stuff: he endorses well-known arguments of Joe Levine and David Chalmers p. Kim's case for claim 1 takes up a large part of his first chapter and all of his second; it refines a line of argument familiar from his previous work, and has the form of a reductio p. It begins by assuming the causal efficacy of the mental and then, with the help of other premises, generates a contradiction.
The other premises are the supervenience of the mental on the physical, the causal closure of the physical, a causal exclusion principle see below , and the irreducibility of mental properties to physical properties. It is, of course, the irreducibility of mental properties to physical properties that is singled out as the target of the reductio. Now Kim explicitly says pp.
What is Kobo Super Points?
So Kim's conclusion at this stage is that if mental properties are causally efficacious, then instances of mental properties must be physically reducible in the sense of being identical with instances of physical properties; no weaker relation will do. Let me pause in my exposition to answer three questions. Nurul Afiza. Kendra Rice. Naga Bhushana. Crystal Walker.
Georg Northoff: Translate this page
Ashley Garcia. Latha Nachu. Rob Nikolewski. Ahmad Syami. Georgescu Tudor. Virgiawan Huda Akbar. Bobi Ardiansyah. Sandeep Rao. Francisco George. Ronaldo Paloma. Raspberry Pi DropCam Alternative Steve Attwood. Anonymous L9fB0XU. Each dot depicts for some n ; standard errors are. Importantly, conducting this experiment in actu is not beyond current technological limitations.
This work makes the following contributions. First, we define statistical supervenience based on Davidson's canonical statement Definition 1. This definition makes it apparent that supervenience implies the possibility of perfect classification Theorem 1. We then prove that there is no viable test against supervenience, so one can never reject a null hypothesis in favor of supervenience, regardless of the amount of data Theorem 2.
Moreover, the data will not, in general, satisfy the above assumptions. Nonetheless, synthetic data analysis suggests that under somewhat realistic assumptions, convergence obtains with an amount of data one might conceivably collect Figure 1 and ensuing discussion. Unfortunately, failure to reject is more ambiguous. Interestingly, much of contemporary research in neuroscience and cognitive science could be cast as mind-brain supervenience investigations.
Testing supervenience of various mental properties on these brain-graphs will perhaps therefore become increasingly compelling; the framework developed herein could be fundamental to these investigations. For example, aspects of visual perception appear to supervene on visual cortical activity for example, binocular rivalry Similar supervenience tests on increasingly complex mental properties will potentially benefit from either higher-throughput imaging modalities 30 , 31 , more coarse brain-graphs 32 , 33 , or both.
The 1-nearest neighbor 1-NN classifier works as follows. The 1-NN algorithm predicts that the unobserved mind is of the same class as the closest brain's class:.
Defending the piggyback principle against Shapiro and Sober’s empirical approach | SpringerLink
The k n nearest neighbor is a straightforward generalization of this approach. It says that the test mind is in the same class as whichever class is the plurality class among the k n nearest neighbors,. Let g n be the k n nearest neighbor k n NN classifier when there are n training samples.
In the main text, we refer to the whole sequence as a classifier. In Stone's original proof 34 , b was assumed to be a q -dimensional vector, and the L 2 norm , where j indexes elements of the q -dimensional vector was shown to satisfy the constraints on a distance metric for this collection of classifiers to be universally consistent.
Later, others extended these results to apply to any L p norm 9. When brain-graphs are represented by their adjacency matrices, one can stack the columns of the adjacency matrices, effectively embedding graphs into a vector space, in which case Stone's theorem applies.
Stone's original proof also applied to the scenario when was infinite, resulting in a universally consistent regression algorithm as well. Note that the above extension of Stone's original theorem to the graph domain implicitly assumed that vertices were labeled, such that elements of the adjacency matrices could easily be compared across graphs. In theory, when vertices are unlabeled, one could first map each graph to a quotient space invariant to isomorphisms, and then proceed as before.
Unfortunately, there is no known polynomial time complexity algorithm for graph isomorphism 35 , so in practice, dealing with unlabeled vertices will likely be computationally challenging 8. CEP ran the experiment.
The authors would like to acknowledge helpful discussions with J. Lande, B. Vogelstein, S. Seung, and K. This work was partially supported by the Research Program in Applied Neuroscience. National Center for Biotechnology Information , U.