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Symbol interdependency in symbolic and embodied cognition.
1Department of Psychology, University of Memphis.
Topics in Cognitive Science
|August 29, 2014
Summary
Computational algorithms like latent semantic analysis (LSA) extract meaning from language by analyzing statistical regularities in surface structure, not special algorithms or embodied simulations. This challenges existing cognitive science theories.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Psycholinguistics
Background:
- Debate exists on whether computational algorithms advance human cognition theories.
- Symbolic and embodied cognition accounts often contrast, underestimating linguistic surface structure's role.
- Latent semantic analysis (LSA) is a key computational tool in this debate.
Purpose of the Study:
- To review symbolic and embodied cognition accounts.
- To demonstrate that meaning induction stems from language itself, specifically its statistical properties.
- To re-evaluate the contributions of LSA and embodiment findings.
Main Methods:
- Overview of symbolic and embodied cognition theories.
- Analysis of latent semantic analysis (LSA) performance.
- Examination of findings attributed to perceptual simulations in embodied cognition.
Main Results:
- LSA's effectiveness is primarily due to linguistic surface structure, not inherent algorithmic sophistication.
- Embodiment findings can be explained by distributional linguistic information, not solely perceptual simulations.
- Statistical regularities in language are underestimated in current cognitive theories.
Conclusions:
- Meaning induction in language processing is fundamentally linked to statistical linguistic properties.
- Computational models and embodied cognition research should more heavily consider surface-level linguistic data.
- Reinterpreting LSA and embodiment findings highlights the primacy of distributional information in language understanding.
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