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A my K ind produce rational behavior Second, the proposal by McCulloch and Pitts that we should think of neurons as biological logic gates enabled cognitive scientists to understand how those simple internal state transitions might be thought to occur in the brain (A logic gate is a unit whose operations can be explained in terms of the truth tables for conjunction and disjunction.) This work also led to the development of connectionist networks, often referred to as neural nets Third, Wiener’s work showed how systems could learn While Turing’s original model did not address the question of how system rules could be modified, Wiener showed how we could incorporate feedback control into the model This helps to account for learning On Wiener’s model, learning occurs when an agent appropriately modifies its behavior in response to experience that is itself a consequence of prior behavior Fourth, Shannon’s work helped us to understand what was meant by “information” in discussions of information processing In particular, Shannon showed that the statistical or probabilistic structure inherent in communication elucidates and indeed quantifies the notion of information The final element of the conceptual package did not emerge until David Marr’s work in the 1980s Marr was a vision scientist, but his approach to explanation helped to unify cognitive science research across its many disciplines In particular, Marr identified three different kinds of questions we need to answer in order to explain the workings of an information-processing system The first question concerns computation: What is the problem that the system needs to solve? The second question concerns algorithm: What sorts of representations and rules are utilized in solving the problem? And the third question concerns implementation: In what physical mechanisms are these representations (and the processes in which they are involved) realized? Figdor’s chapter concludes by noting that “the basic conceptual package for explaining agency will soon be fully elaborated in outline if not in its empirical details,” and she predicts that, with respect to cognitive science, the 21st century will likely be the century of the social, i.e., it will involve intense study of social cognition In the final chapter of the book, Susan Schneider and Pete Mandik directly take up the question of what lies ahead in the 21st century Their discussion begins with findings from the artificial intelligence community that strongly suggests that sophisticated artificial intelligence will be here by 2070, if not earlier This looming development suggests that philosophy of mind will have a very important role to play in the decades ahead Though discussing this role requires Schneider and Mandik to make some predictions, their chapter is better viewed as prescriptive than as predictive, as their primary concern is to suggest ways that philosophy of mind can positively impact the future that lies before us Consider, for example, the fact that a dizzying array of neural enhancements will likely soon be widely available Philosophical work on the extended mind – and here recall Farkas’ discussion in Chapter 10 – can help us to better understand and navigate these emerging technologies Or consider the fact that one may soon be able to upload one’s consciousness and thereby in some way “migrate” to a cloudbased existence In this context, Johansson’s discussion of the problem of personal 18
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