Automatic Generation of Cognitive Theories using Genetic Programming

Frı́as-Martı́nez, Enrique; Gobet, Fernand

doi:10.1007/s11023-007-9070-6

Cited by 19 publications

(20 citation statements)

References 32 publications

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“…An extension of this idea is to use the canonical results as a target for the automatic generation of theories. In unrelated work, Frias-Martinez and Gobet [8] have shown how theories may be generated automatically using genetic programming. The quality of the theories is judged based on their fitness to a standard set of experimental data -this standard data, in our terminology, would form the canonical results of the resultant theories.…”

Section: Discussionmentioning

confidence: 99%

A Methodology for Developing Computational Implementations of Scientific Theories

Lane

Gobet²

2008

Tenth International Conference on Computer Modeling and Simulation (Uksim 2008)

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Section: Discussionmentioning

confidence: 99%

A Methodology for Developing Computational Implementations of Scientific Theories

Lane

Gobet²

2008

Tenth International Conference on Computer Modeling and Simulation (Uksim 2008)

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“…learning algorithms) of the models to be evolved. Frias-Martinez and Gobet (2007), using genetic programming, have shown that this is possible with a simple task. Third, our method focuses on parameter optimisation, but does not address issues such as theory parsimony when comparing models.…”

Section: Discussionmentioning

confidence: 99%

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

Lane

Gobet

2013

Journal of Artificial General Intelligence

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?? Peter C. R. Lane, Fernand Gobet. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY-NC-ND 3.0)Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the ???speciated non-dominated sorting genetic algorithm??? for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data

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“…At the very least, it requires acquiring skills in computer science and programming in addition to skills specific to a particular domain, such as psychology. In addition, the generation of scientific models can be described as a heuristic search in the combinatorial space of all the possible candidate models that explain a specific phenomenon (Frias-Martinez & Gobet, 2007; Simon, 1977). Given the infinite size of such spaces, searching them can be difficult indeed, both theoretically and computationally, and human scientists can explore only a limited portion of those spaces.…”

Section: Value-based Decision-makingmentioning

confidence: 99%

Modeling Value-Based Decision-Making Policies Using Genetic Programming

Pirrone

Gobet

2020

Swiss Journal of Psychology

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Abstract. An important way to develop models in psychology and cognitive science is to express them as computer programs. However, computational modeling is not an easy task. To address this issue, some have proposed using artificial-intelligence (AI) techniques, such as genetic programming (GP) to semiautomatically generate models. In this paper, we establish whether models used to generate data can be recovered when GP evolves models accounting for such data. As an example, we use an experiment from decision-making which addresses a central question in decision-making research, namely, to understand what strategy, or “policy,” agents adopt in order to make a choice. In decision-making, this often means understanding the policy that best explains the distribution of choices and/or reaction times of two-alternative forced-choice decisions. We generated data from three models using different psychologically plausible policies and then evaluated the ability and extent of GP to correctly identify the true generating model among the class of virtually infinite candidate models. Our results show that, regardless of the complexity of the policy, GP can correctly identify the true generating process. Given these results, we discuss implications for cognitive science research and computational scientific discovery as well as possible future applications.

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Automatic Generation of Cognitive Theories using Genetic Programming

Cited by 19 publications

References 32 publications

A Methodology for Developing Computational Implementations of Scientific Theories

A Methodology for Developing Computational Implementations of Scientific Theories

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

Modeling Value-Based Decision-Making Policies Using Genetic Programming

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