My book appeared and can be purchased in print or in Kindle, or directly from MIT Press. Oron Shagrir reviewed it in Notre Dame Philosophical Reviews, and Frances Egan in the Review of Metaphysics (behind a paywall). I also talk about the book with Carrie Figdor in New Books in Philosophy.
For the book, I was awarded the National Science Centre prize in humanities and social sciences for young scientists in 2014.
In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.
Defending the computational explanation against objections to it—from John Searle and Hilary Putnam in particular— I conclude that computationalism is here to stay but is not what many have taken it to be. In particular, it does not rely on a Cartesian gulf between either software and hardware or mind and brain. The computational method of describing the ways information is processed is usually abstract—but cognition is possible only when computation is realized physically, and the physical realization is not the same thing as its description. The mechanistic construal of computation allows me to show that no purely computational explanation of a physical process will ever be complete. This is because we also need to account for how the computation is physically implemented, and in explaining this, we cannot simply appeal to computation itself. In addition, we need to know how the computational mechanism is embedded in the environment, which, again, is not a purely computational matter. For this reason, computationalism is plausible only if you also accept explanatory pluralism: the proposition that there are acceptable causal explanations that are not spelled out in terms of any computational idiom. This is perfectly in line with the mechanistic philosophy of science.
I sketch a mechanistic theory of implementation of computation against a background of extant conceptions, describing four dissimilar computational models of cognition. The first model is Allen Newell and Herbert Simon’s model of problem solving involved in so-called cryptarithmetics, which is a kind of mathematical puzzle. Then, a connectionist model of past tense acquisition of English verbs, developed by David Rumelhart and James McClelland in 1980s, is scrutinized, to be followed by a biologically plausible model of path integration in rats. The latter one was built in 2005 by John Conklin and Chris Eliasmith and is one of the cutting-edge developments in computational neuroscience. The last case study is a robotic model of phonotaxis in crickets, developed by Barbara Webb, which shows the application of robotic explanations in neuroethology.
I review other philosophical accounts of implementation and computational explanation and defends a notion of representation that is compatible with his mechanistic account and adequate vis à vis the four models discussed earlier. Instead of arguing that there is no computation without representation, I invert the slogan and show that there is no representation without computation—but explains that representation goes beyond purely computational considerations. My arguments vindicate computational explanation in a novel way by relying on mechanistic theory of science and interventionist theory of causation. The overall ambition of the project is to furnish cognitive scientists with an up-to-date conceptual and methodological framework of computational explanation.
This work was supported by Polish Ministry of Science and Higher Education grant N N101 138039 under the contract 1380/B/H03/2010/39. In 2013, it won the Tadeusz Kotarbiński prize for the best book in philosophy in 2011-2013 from the Section I of the Polish Academy of Sciences.