Abstract
The challenge of explaining how cognition can be tractably realized is widely recognized. Classical rationality is thought to be intractable due to its assumptions of optimization and/or domain generality, and proposed solutions therefore drop one or both of these assumptions. We consider three such proposals: Resource‐Rationality, the Adaptive Toolbox theory, and Massive Modularity. All three seek to ensure the tractability of cognition by shifting part of the explanation from the cognitive to the evolutionary level: Evolution is responsible for producing the tractable architecture. We consider the three proposals and show that, in each case, the intractability challenge is not thereby resolved, but only relocated from the cognitive level to the evolutionary level. We explain how non‐classical accounts do not currently have the upper hand on the new playing field.
Highlights
Cognitive scientists face a key explanatory challenge: How can cognition both fit the demands of our complex world and still be computationally tractable? To understand the weight of this challenge, it is useful to explicate the nature of the problem solved by a cognitive agent who successfully navigates its world
The task has been seen as explaining how sophisticated cognition could be realized through the coordinated action of simple modules alone. (Observe the parallel with Adaptive Toolbox (AT)’s challenge of explaining how heuristics could be matched to the right environments.) Fodor himself saw no real possibilities for solving this task (Fodor, 2001), solutions have been proposed
Evolution looms large in all three of these research programs. It is responsible for producing the heuristics posited by RR and AT, and the modules posited by Massive Modularity (MM)
Summary
Cognitive scientists face a key explanatory challenge: How can cognition both fit the demands of our complex world and still be computationally tractable? To understand the weight of this challenge, it is useful to explicate the nature of the problem solved by a cognitive agent who successfully navigates its world. Classical rationality (CR) poses a computationally intractable problem; that is, there exists no general tractable (polynomial-time) process that, for any given situation, selects an action with maximum expected utility (Bossaerts et al, 2019). Note that the computational-level characterizations of the competing theories that we provide throughout the paper illustrate this: These theories hypothesize how cognition works in a salient sense by hypothesizing what problem it solves with a greater degree of precision.. Note that the computational-level characterizations of the competing theories that we provide throughout the paper illustrate this: These theories hypothesize how cognition works in a salient sense by hypothesizing what problem it solves with a greater degree of precision.4 With respect to this expanded explanatory landscape, the debate regarding cognitive architectures includes both the cognitive (causal) and evolutionary levels. Our results are important because, while we rarely know exactly how evolution proceeded, they establish that offloading explanation to evolutionary processes is insufficient to meet the important explanatory challenge that we started with
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