Image Image Image Image Image
Scroll to Top

To Top

Computational Models

Note: The computational models below are written in Common Lisp and require a Lisp interpreter. Many interpreters are available for free, but we recommend LispWorks or Clozure CL.


mReasoner: A unified computational implementation of the model theory

mReasoner is a unified computational system that implements mental models theory. It is written in Common Lisp, and it is a psychologically plausible inferential engine for syllogistic, quantificational, monadic deductive and probabilistic reasoning.


mSentential: A unified theory of sentential reasoning

This program (v0.4) does various sorts of sentential reasoning (based on if, or, and, not) according to the theory of mental models (Khemlani, Goodwin, Byrne, & Johnson-Laird, under review).


mAbducer: Recursive simulation and abductive reasoning

mAbducer solves universal rearrrangement problems containing a single static loop, and it automatically programs two functions for solving any instance of a class of problems, such as reversing the order of a list, sorting palindromes, and parity-sorts (the inverse of riffle shuffles). Rearrangment problems can be set in the ‘railway’ environment (described in Khemlani, Mackiewicz, Bucciarelli, & Johnson-Laird, 2013).


PRISM: Preferred inferences in spatial models

PRISM is a computational cognitive model that can be used to simulate and explain how preferred mental models are constructed, inspected, and varied in a spatial array that functions as if it were a spatial working memory. A spatial focus inserts tokens into the array, inspects the array to find new spatial relations, and relocates tokens in the array to generate alternative models of the problem description, if necessary.


Propositional reasoning

Note: This computational model is deprecated in favor of mSentential, available above.

This computational model implements a revised version of a psychological theory of propositional reasoning originally developed by Johnson-Laird and Ruth Byrne (see their book Deduction). It postulates 4 stages of performance (increasing in accuracy), the first three are psychological and the fourth is an exercise in artificial intelligence.


Syllogistic reasoning

This program models the psychological theory of syllogistic reasoning developed by Johnson-Laird as a successor to the theory sketched in Deduction (1991) by J-L and Byrne. (June 22-July 3 1992).


Spatial reasoning

The program makes spatial deductions using a compositional semantics and bu + bt parser. The program constructs only one falsifying model. It does not allow premises to assert that one item is in the same place as another, but it does put items in the same place temporarily in the course of searching for models that refute conclusions.


Temporal reasoning

It makes simple temporal deductions using a compositional semantics driven by a bottom-parser to update models (in the form of arrays).  The program constructs all the possible models for premises as it interprets them, including the multiple models for indeterminacies. Hence, it does not need to search for alternative models in order to test for validity. If the number of models exceeds its *capacity*, then the program tries an alternative strategy in which it uses the question to search for just those premises that are relevant to the answer. In this way, it ignores irrelevant premises and deals with relevant ones in a referentially coherent order.


Boolean concept learning

This is a program for constructing mental models of instances of concepts. Its input is a set of fully explicit models, which it then seeks to simplify.  (Written by Phil Johnson-Laird in April 2007).


Reverse engineering of Boolean circuits

The program tries to reverse engineer simple electrical circuits in the sort of way in which Dr. N.Y. Louis Lee discovered naive human reasoners do (see Lee & Johnson-Laird, under review).