A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

A critical survey of some attempts to define ‘computer’, beginning with some informal ones, then critically evaluating those of three philosophers, and concluding with an examination of whether the brain and the universe are computers.

ABSTRACT The status and role of Popper’s ‘Rationality Principle’ (RP) is still the subject of disputes. The ‘prevailing view’ among Popper’s commentators seems to be that RP is better interpreted as a methodological principle. Yet, this view is challenged in a recent study where RP is interpreted as an‘idealization’. We critically review these two accounts of RP and propose a novel one according to which RP is, first and foremost, the scientific version of a heuristic that ordinary people unconsciously use (...) when they seek to explain and predict other people’s behavior. However, we recognize that, to the extent RP is part of a methodological strategy whereby, in the wake of adverse empirical results, social scientists place the ‘onus of proof’ on their situational model it is legitimate to claim that RP is adopted, at least partly, for methodological reasons. Further, to the extent that the adoption of RP implies suppressing those mental processes that may affect actors’ reasoning and willpower it is also legitimate to claim that RP is, at least partly, an ‘idealization’. We conclude that the status and role of RP is multi-faceted and that the three accounts of RP complement each other. (shrink)

A fundamental task for any prospective cognitive architecture is information control: routing information to the relevant mechanisms to support a variety of tasks. Jerry Fodor has argued that the Massive Modularity Hypothesis cannot account for flexible information control due to its architectural commitments and its reliance on heuristic information processing. I argue instead that the real trouble lies in its commitment to nativism—recent massive modularity models, despite incorporating mechanisms for learning and self-organization, still cannot learn to control information flexibly enough. (...) After defending this position, I close the paper by discussing wider implications for control and cognitive architecture. (shrink)

:In this paper, we analyse the difference between two types of behavioural policies – nudges and boosts. We distinguish them on the basis of the mechanisms through which they are expected to operate and identify the contextual conditions that are necessary for each policy to be successful. Our framework helps judging which type of policy is more likely to bring about the intended behavioural outcome in a given situation.

A lot of philosophy taught to science students consists of scientific methodology. But many philosophy of science textbooks have a fraught relationship with methodology, presenting it either a system of universal principles or entirely permeated by contingent factors not subject to normative assessment. In this paper, I argue for an alternative, heuristic perspective for teaching methodology: as fallible, purpose- and context-dependent, subject to cost-effectiveness considerations and systematically biased, but nevertheless subject to normative assessment. My pedagogical conclusion from this perspective is (...) that philosophers should aim to teach science students heuristic reasoning: strategies of normative method choice appraisal that are sensitive to purposes, contexts, biases and cost-effectiveness considerations; and that we should do so by teaching them exemplars of such reasoning. I illustrate this proposal at the hand of three such exemplars, showing how they help students to appreciate the heuristic nature of both methods and methodology, and to normatively assess method choice in such circumstances. (shrink)

Scientific methods are heuristic in nature. Heuristics are simplifying, incomplete, underdetermined and fallible problem-solving rules that can nevertheless serve certain goals in certain contexts better than truth-preserving algorithms. Because of their goal- and context-dependence, a framework is needed for systematic choosing between them. This is the domain of scientific methodology. Such a methodology, I argue, relies on a form of instrumental rationality. Three challenges to such an instrumentalist account are addressed. First, some authors have argued that the rational choice of (...) at least some methods, namely those supporting belief formation, is not goal-dependent. Second, some authors have observed that some method choices seem intuitively rational, even though relevant goals are lacking. Thirdly, some authors have argued that instrumental rationality itself depends on a goal-independent form of rationality. It is the heuristic nature of scientific methods that affords me the arguments against these challenges. This heuristic-instrumentalist account provides the means-ends analysis needed to evaluate heuristic method choice. The paper thus offers the conceptual basis for a scientific methodology that is both compatible with the heuristic nature of actual scientific practice and also normatively relevant for assessing method choice. (shrink)

This thesis contributes to a better conceptual understanding of how self-organized control works. I begin by analyzing the control problem and its solution space. I argue that the two prominent solutions offered by classical cognitive science (centralized control with rich commands, e.g., the Fodorian central systems) and embodied cognitive science (distributed control with simple commands, such as the subsumption architecture by Rodney Brooks) are merely two positions in a two-dimensional solution space. I outline two alternative positions: one is distributed control (...) with rich commands, defended by proponents of massive modularity hypothesis; the other is centralized control with simple commands. My goal is to develop a hybrid account that combines aspects of the second alternative position and that of the embodied cognitive science (i.e., centralized and distributed controls with simple commands). Before developing my account, I discuss the virtues and challenges of the first three. This discussion results in a set of criteria for successful neural control mechanisms. Then, I develop my account through analyzing neuroscientific models of decision-making and control with the theoretical lenses provided by formal decision and social choice theories. I contend that neural processes can be productively modeled as a collective of agents, and neural self-organization is analogous to democratic self-governance. In particular, I show that the basal ganglia, a set of subcortical structures, contribute to the production of coherent and intelligent behaviors through implementing “democratic" procedures. Unlike the Fodorian central system—which is a micro-managing “neural commander-in-chief”—the basal ganglia are a “central election commission.” They delegate control of habitual behaviors to other distributed control mechanisms. Yet, when novel problems arise, they engage and determine the result on the basis of simple information (the votes) from across the system with the principles of neurodemocracy, and control with simple commands of inhibition and disinhibition. By actively managing and taking advantage of the wisdom-of-the-crowd effect, these democratic processes enhance the intelligence and coherence of the mind’s final "collective" decisions. I end by defending this account from both philosophical and empirical criticisms and showing that it meets the criteria for successful solution. (shrink)