This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.

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)

Cognitive modules are internal mental structures. Some theorists and empirical researchers hypothesize that the human mind is either partially or massively comprised of structures that are modular in nature. Modules are also invoked to explain cognitive capacities associated with the performance of specific functional tasks. Jerry Fodor considered that modules are useful only for explaining relatively low-level systems. These are the systems involved in capacities like perception and language. For Fodor, the central systems of mind — those involved in capacities (...) like judgment, planning, decision-making, and so forth — are not explainable in terms of modular mechanisms. However, some other philosophers as well as proponents and practitioners of evolutionary psychology consider that it is sensible — and even necessary — to invoke modules for explaining these systems, too. Indeed, the debate over modularity is mainly a debate over the modularity of central systems. Admittedly, it is also possible to raise doubts about the modularity of peripheral systems, but this kind of skepticism is not widespread in the literature. In contrast, both the empirical and the theoretical cases in favor of central modularity are usually contested on multiple fronts and in tough terms. The case for the modularity of central systems is the core of the case for the massive modularity of mind hypothesis. Granted, the massive modularity of mind hypothesis is an empirical statement and, as such, its truth should be ultimately decided in an empirical way, not a priori. Nonetheless, much of the debate over massive modularity has taken place on theoretical grounds. This is due to an alleged underdetermination of the hypotheses regarding the modularity of central systems by data. In such conditions, the remaining open option has been to advance theoretical considerations in favor of the plausibility — and not directly in favor of the empirical truth or truthlikeness — of the massive modularity of mind generally, and of the modularity of some central systems in particular. These theoretical considerations are mostly based on an adaptationist view of evolution cum a classical computationalist approach to mind. They include arguments about the nature and evolution of hierarchically ordered complex systems, a presumption of optimality expressed in the apparent design of phenotypic traits shaped by natural selection and dismissing nonmodular mental architectures as computationally intractable. Is the massive modularity of mind hypothesis a cogent view about the ontological nature of human mind or is it, rather, an effective/ineffective adaptationist discovery heuristic for generating predictively successful hypothesis about both heretofore unknown psychological traits and unknown properties of already identified psychological traits? Considering the inadequacies of the case in favor of massive modularity as an ontological hypothesis, I suggest approaching and valuing massive modularity as an adaptationist discovery heuristic. (shrink)

Dual process theorists posit the existence of two distinct types of cognitive processing in order to explain domains of higher cognition such as reasoning and decision-making. Such theories typically allude to co-varying clusters of properties as well as an underlying mechanism or system for each processing type. I will argue that a host of empirical findings cast doubt upon the validity of both theses, and that simpler models of higher cognition—that do not appeal to types of processing—might be preferable.

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)