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Reasoning as simulation

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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.

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Notes

  1. We refer to inference and reasoning interchangeably as the process by which people come to believe or suspect new facts given existing facts. We intend this broadly to include, for example, subconscious and automatic inference in perception and language understanding in addition to cognition studied in the psychology of reasoning.

  2. Following Jackendoff (2007), we avoid the word “representation” in order to make it clear that we are referring to nothing more than aspects of brain state. This is intended to avoid philosophical confusions regarding the use of the term.

  3. It is perhaps less obvious (and not relevant to this paper) that logical or symbolic rule-based notions can capture analog (as opposed to discrete) aspect of cognition. However, many logics and rule systems take real numbers as values.

  4. In fact, there is no reason why a production rule system might not be part of a pattern completion mechanism.

  5. DPLL’s “world knowledge” is expressed in conjunctive normal form (CNF), e.g., (P or Q) and (P or not-Q or not-R) and…. The parenthesized “clauses” in CNF can be written as a rule. For example, (P or not-Q or not R) is logically equivalent to (R and Q) → P. DPLL’s elaboration step involves performing “unit propagation”. Unit propagation sets propositions to truth values implied by already inferred and/or assumed truth values. Thus, with the CNF above, if P is false, then Q is inferred.

  6. Although not developed explicitly as a simulation theory, several aspects of Soar are consistent with this work. For example, Soar’s use of “problem spaces” to consider hypothetical actions is a kind of simulation. Further, Soar uses “impasses” to control its use of problem spaces. These impasses often involve conflicting options. Negative priming and the likelihood bias are both heuristics for dealing with conflicting options.

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Authors and Affiliations

  1. Rensselaer Polytechnic Institute, Troy, NY, USA

    Nicholas L. Cassimatis, Arthi Murugesan & Perrin G. Bignoli

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  1. Nicholas L. Cassimatis
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  2. Arthi Murugesan
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  3. Perrin G. Bignoli
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Correspondence to Nicholas L. Cassimatis.

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Cassimatis, N.L., Murugesan, A. & Bignoli, P.G. Reasoning as simulation. Cogn Process 10, 343–353 (2009). https://doi.org/10.1007/s10339-009-0256-0

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