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Evolving agents as a metaphor for the developing child.

Matthew Schlesinger¹

¹Department of Psychology, Southern Illinois University Carbondale, 62901, USA. matthews@siu.edu

Developmental Science

|August 24, 2004

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Evolutionary Computation (EC) provides developmental psychologists with mathematical tools to simulate development. This review highlights EC approaches, focusing on comparative stochastic optimization for modeling infant reaching behaviors.

Area of Science:

Developmental Psychology
Evolutionary Computation
Computational Neuroscience

Background:

Evolutionary Computation (EC) is inspired by neo-Darwinian principles like natural selection and mutation.
EC offers developmental psychologists novel mathematical tools for simulating ontogenetic processes.
Various EC approaches exist, including Artificial Life, evolutionary robotics, and comparative stochastic optimization.

Purpose of the Study:

To review key Evolutionary Computation approaches relevant to developmental psychology.
To highlight the advantages of comparative stochastic optimization for studying development.
To present a concrete model simulating infant reaching development using EC.

Main Methods:

Review of established EC methodologies (Artificial Life, evolutionary robotics).

Related Experiment Videos

Focus on comparative stochastic optimization as a developmental research tool.

Design and implementation of an EC model for simulating infant reaching.

Main Results:

Comparative stochastic optimization presents significant advantages for developmental research.
A functional EC model was developed to simulate the ontogeny of infant reaching.
The model demonstrates the potential of EC to replicate complex developmental trajectories.

Conclusions:

Evolutionary Computation offers a powerful framework for developmental psychology.
Comparative stochastic optimization is a viable method for modeling developmental processes.
EC models can provide insights into the mechanisms underlying infant motor development.