Perceptual simulations can be as expressive as first-order logic.

Theories asserting that human reasoning is based on perceptual simulations often suppose these simulations are of concrete individual objects and the specific relations that hold among them. However, much human knowledge involves assertions about which relations do not hold, generalities over large numbers of objects and conditional facts. Can simulation theories explain how the mind represents these forms of knowledge, or are they inferior in their expressive power to knowledge representation schemes based on logical formalisms designed specifically to deal with negative, conditional and quantificational knowledge? In this paper, we show how assertions about mental simulations can in fact straightforwardly express all the concepts that comprise first-order logic, including negation, conditionals and both universal and existential quantification. We also speculate on how to extend this approach to deal with probabilistic and more expressive logics.

Reasoning as simulation.

The theory that human cognition proceeds through mental simulations, if true, would provide a parsimonious explanation of how the mechanisms of reasoning and problem solving integrate with and develop from mechanisms underlying forms of cognition that occur earlier in evolution and development. However, questions remain about whether simulation mechanisms are powerful enough to exhibit human-level reasoning and inference. In order to investigate this issue, we show that it is possible to characterize some of the most powerful modern artificial intelligence algorithms for logical and probabilistic inference as methods of simulating alternate states of the world. We show that a set of specific human perceptual mechanisms, even if not implemented using mechanisms described in artificial intelligence, can nevertheless perform the same operations as those algorithms. Although this result does not demonstrate that simulation theory is true, it does show that whatever mechanisms underlie perception have at least as much power to explain non-perceptual human reasoning and problem solving as some of the most powerful known algorithms.

Ability, breadth, and parsimony in computational models of higher-order cognition.

Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern-day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher-order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model.