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A Neurorobotics Approach to Investigating Word Learning Behaviors

A Neurorobotics Approach to Investigating Word Learning Behaviors

Richard Veale
Copyright: © 2013 |Pages: 26
ISBN13: 9781466629738|ISBN10: 1466629738|EISBN13: 9781466629745
DOI: 10.4018/978-1-4666-2973-8.ch012
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MLA

Veale, Richard. "A Neurorobotics Approach to Investigating Word Learning Behaviors." Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence, edited by Lakshmi Gogate and George Hollich, IGI Global, 2013, pp. 270-295. https://doi.org/10.4018/978-1-4666-2973-8.ch012

APA

Veale, R. (2013). A Neurorobotics Approach to Investigating Word Learning Behaviors. In L. Gogate & G. Hollich (Eds.), Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence (pp. 270-295). IGI Global. https://doi.org/10.4018/978-1-4666-2973-8.ch012

Chicago

Veale, Richard. "A Neurorobotics Approach to Investigating Word Learning Behaviors." In Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence, edited by Lakshmi Gogate and George Hollich, 270-295. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2973-8.ch012

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Abstract

This chapter presents two examples of how neurorobotics is being used to further understanding of word learning in the human infant. The chapter begins by presenting an example of how neurorobotics has been used to explore the synchrony constraint of word-referent association in young infants. The chapter then demonstrates the application of neurorobotics to free looking behavior, another important basic behavior with repercussions in how infants map visual stimuli to auditory stimuli. Neurorobotics complements other approaches by validating proposed mechanisms, by linking behavior to neural implementation, and by bringing to light very specific questions that would otherwise remain unasked. Neurorobotics requires rigorous implementation of the target behaviors at many vertical levels, from the level of individual neurons up to the level of aggregate measures, such as net looking time. By implementing these in a real-world robot, it is possible to identify discontinuities in our understanding of how parts of the system function. The approach is thus informative for empiricists (both neurally and behaviorally), but it is also pragmatically useful, since it results in functional robotic systems performing human-like behavior.

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