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ing these methods, operating at vastly higher rates, should have made them intelligent. It did not. It turns out that general-purpose reasoning methods are very weak, and have crippling defects (e.g., combinatorial explosion) that are a direct consequence of their domain generality (Tooby and Cosmides, 1992; Cosmides and Tooby, 2001; Tooby et al., 2005). Also, content effects (changes in reasoning performance based on changes in content) are ubiquitous (Wason and Johnson-Laird, 1972; Gigerenzer and Murray, 1987) yet difficult to account for in the consensus view. After all, differences in content should make little difference to procedures whose operation is designed to be content-independent. Unfortunately, the effects of content on reasoning have traditionally been dismissed as noise to be ignored rather than a window on reasoning methods of a radically different, content-specific design.

INTELLIGENCE AND EVOLVED SPECIALIZATIONS

The integration of evolutionary biology with cognitive science led to a markedly different approach to investigating human intelligence and rationality: evolutionary psychology (Tooby and Cosmides, 1992; Cosmides and Tooby, 2001). Organisms are engineered systems that must operate effectively in real time to solve challenging adaptive problems. The computational problems our ancestors faced were not drawn randomly from the universe of all possible problems; instead, they were densely clustered in particular, recurrent families (e.g., predator avoidance, foraging, mating) that occupy only miniscule regions of the space of possible problems. Massive efficiency gains can be achieved when different computational strategies are tailored to the task demands of different problem types. For this reason, natural selection added a diverse array of inferential specializations, each tailored to a particular, adaptively important problem domain (Gallistel, 1990). Freed from the straightjacket of a one-size-fits-all problem-solving strategy, these reasoning specializations succeed by deploying procedures that produce adaptive inferences in a specific domain, even if these operations are invalid, useless, or harmful if activated outside that domain. They can do this by exploiting regularities—content-specific relationships—that hold true within the problem domain, but not outside of it. This approach naturally predicts content effects, because different content domains should activate different inferential rules.

In this view, human intelligence is more powerful than machine intelligence because it contains, alongside general-purpose inferential tools, a large and diverse array of adaptive specializations—expert systems, equipped with proprietary problem-solving strategies that evolved to match the recurrent features of their corresponding problem domains.



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