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Changing Minds Changing ToolsFrom Learning Theory to Language Acquisition to Language Change$
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Vsevolod Kapatsinski

Print publication date: 2018

Print ISBN-13: 9780262037860

Published to MIT Press Scholarship Online: September 2019

DOI: 10.7551/mitpress/9780262037860.001.0001

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Continuous Dimensions and Distributional Learning

Continuous Dimensions and Distributional Learning

(p.127) 5 Continuous Dimensions and Distributional Learning
Changing Minds Changing Tools

Vsevolod Kapatsinski

The MIT Press

This chapter describes the evidence for the existence of dimensions, focusing on the difference between the difficulty of attention shifts to a previously relevant vs. irrelevant dimension. It discusses the representation of continuous dimensions in the associationist framework. including population coding and thermometer coding, as well as the idea that learning can adjust the breadth of adjustable receptive fields. In phonetics, continuous dimensions have been argued to be split into categories via distributional learning. This chapter reviews what we know about distributional learning and argues that it relies on several distinct learning mechanisms, including error-driven learning at two distinct levels and building a generative model of the speaker. The emergence of perceptual equivalence regions from error-driven learning is discussed, and implications for language change briefly noted with an iterated learning simulation.

Keywords:   dimensions, selective attention, distributional learning, population coding, thermometer coding, error-driven learning, generative model, equivalence regions, receptive fields, phonetic

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