The aim of this article is to strengthen links between cognitive brain research and formal logic. The work covers three fundamental sorts of logical inferences: reasoning in the propositional calculus, i.e. inferences with the conditional “if...then”, reasoning in the predicate calculus, i.e. inferences based on quantifiers such as “all”, “some”, “none”, and reasoning with n-place relations. Studies with brain-damaged patients and neuroimaging experiments indicate that such logical inferences are implemented in overlapping but different bilateral cortical networks, including parts of the (...) fronto-temporal cortex, the posterior parietal cortex, and the visual cortices. I argue that these findings show that we do not use a single deterministic strategy for solving logical reasoning problems. This account resolves many disputes about how humans reason logically and why we sometimes deviate from the norms of formal logic. (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 (1983) considered that modules are useful only for explaining relatively low-level systems (input systems). These are the systems involved in capacities like perception and language. For Fodor, the central (high-level) systems of mind — (...) those involved in capacities like judgment (the “fixation of belief” in Fodor’s jargon), planning (practical reasoning), 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 (high-level cognitive capacities). Admittedly, it is also possible to raise doubts about the modularity of peripheral systems (Prinz, 2006), but this kind of skepticism is not widespread in the literature. In contrast, both the empirical and the theoretical (a priori) 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 (viz. the hypothesis that consists in asserting that both the peripheral and the central systems of human mind are largely composed of modules). 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 (Sperber, 1994, 2001). 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 (the evolvability of complexity argument), a presumption of optimality expressed in the apparent design of phenotypic traits shaped by natural selection (the task-specificity argument) and dismissing nonmodular mental architectures as computationally intractable (the tractability argument). 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)

According to Kanazawa (Psychol Rev 111:512–523, 2004), general intelligence, which he considers as a synonym of abstract thinking, evolved specifically to allow our ancestors to deal with evolutionary novel problems while conferring no advantage in solving evolutionary familiar ones. We present a study whereby the results contradict Kanazawa’s hypothesis by demonstrating that performance on an evolutionary novel problem (an abstract reasoning task) predicts performance on an evolutionary familiar problem (a social reasoning task).