Reasoning as simulation

Abstract

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.

Text comprehension as a problem solving situation

“…It is important to re-emphasise, however, that the representation of the task environment and the problem space that the reader constructs is analogous to an embodied situation model arising from interaction between the reader's cognitive processes and aspects of the text. Any such process of simulation is an essential component in problem solving and reasoning in general (Cassimatis, Murugesan, & Bignoli, 2009). Moreover, neuroscientific research indicates that inherent in simulations of specific goals in problem solving situations is the activation of the default network and the executive dorsolateral prefrontal cortex in order to, among other processes, coordinate motor actions in relation to goals and deal with the task's level of abstractness and emotional valence (Gerlach, Spreng, Gilmore, & Schacter, 2011) (see Figure 3).…”

Flexible Inference with Structured Knowledge through Reasoned Unification

“…12,13 Reasoned Unifi cation How to represent the full range of nonlinguistic knowledge is a broad and diffi cult problem. However, for our purposes, it's enough to describe how we use constraints to represent structures such as frames and scripts.…”