Mental Models as Finite-State Machines: Examples and Computational Methods

Abstract

The term mental model often refers to an internal representation that can be mentally run to produce inferences, explanations, and predictions about the environment. Holland, Holyoak, Nisbett, and Thagard (1986) propose a formalism to capture the dynamics of mental models: a transition function defined on a set of model states, the result of a categorizing of environmental states. This transition function mimics the state changes that unfold in the environment. The paper shows that the addition of a few reasonable constraints to this formalism results in a class of transition functions with well known properties; the general class of finite state machines. Finite state machines can be used to model interactions between humans and artifices (e.g., an application program, an ATM, or a car). We present a method to test a hypothesis involving the partitioning of a set of environment states into equivalence classes, which are identified as states of the model. The method is demonstrated on a spatial reasoning task performed by second and third grade children.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADA357588

Entities

People

  • A. Leo Beem
  • Martin J. Ippel

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Application Software
  • Cognition
  • Cognitive Science
  • Cognitive Systems Engineering
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Decoding
  • Environment
  • Information Theory
  • Probability
  • Psychology
  • Reasoning
  • Social Sciences
  • Statistics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Artificial Intelligence
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms