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Material representations: from the genetic code to the evolution of cellular automata
Luis Mateus Rocha1, Wim Hordijk
1School of Informatics and Cognitive Science Program, Indiana University, 1900 East Tenth Street, Bloomington, IN 47406, USA. rocha@indiana.edu
Artificial Life
|April 7, 2005
Summary
This study defines representation in cognitive science using inert structures for memory in evolving systems. Evolved cellular automata show these structures capture some, but not all, representational characteristics.
Area of Science:
- Cognitive Science
- Artificial Life
- Computational Neuroscience
Background:
- The concept of representation is central to cognitive science.
- Understanding how information is stored and processed in evolving systems is crucial.
- Existing models often struggle to bridge computation and dynamics.
Purpose of the Study:
- To propose a novel definition of representation for cognitive science.
- To investigate the origin of memory-encoding structures in evolving systems.
- To explore the role of artificial life in the computation-versus-dynamics debate.
Main Methods:
- Computer experiments evolving cellular automata using genetic algorithms.
- Analysis of evolved rules within the computational mechanics framework.
- Examination of biological evidence concerning genetic memory.
Main Results:
- Evolved cellular automata rules for nontrivial tasks, including the density task.
- Identified that representations require inert structures to encode information.
- Found evolved structures capture some, but lack essential, characteristics of representations.
Conclusions:
- Artificial life can offer insights into cognitive science debates.
- Proposed definitions and experiments provide a bridge between computation and dynamics.
- Inert structures are necessary but not sufficient for representation.