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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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What Are the Nodes? Unitization and Configural Learning vs. Selective Attention

What Are the Nodes? Unitization and Configural Learning vs. Selective Attention

(p.69) 3 What Are the Nodes? Unitization and Configural Learning vs. Selective Attention
Changing Minds Changing Tools

Vsevolod Kapatsinski

The MIT Press

This chapter introduces the debate between elemental and configural learning models. Configural models represent both a whole pattern and its parts as separate nodes, which are then both associable, i.e. available for wiring with other nodes. This necessitates a kind of hierarchical inference at the timescale of learning and motivates a dual-route approach at the timescale of processing. Some patterns of language change (semanticization and frequency-in-a-favourable-context effects) are argued to be attributable to hierarchical inference. The most prominent configural pattern in language is argued to be a superadditive interaction. However, such interactions are argued to often be unstable in comprehension due to selective attention and incremental processing. Selective attention causes the learner to focus on one part of a configuration over others. Incremental processing favors the initial part, which can then overshadow other parts and drive the recognition decision. Only with extensive experience, can one can learn to integrate multiple cues. When cues are integrated, the weaker cue can cue the outcome directly or can serve as an occasion-setter to the relationship between the outcome and the primary cue. The conditions under which occasion-setting arises in language acquisition is a promising area for future research.

Keywords:   selective attention, configural learning, superadditive, occasion-setting, cue weighting, cue integration, incremental processing, hierarchical inference, frequency-in-a-favourable-context, semanticization

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