Learning in Complex, Multi-Component Cognitive Systems: Different Learning Challenges Within the Same System

Bonnie L. Breining, Nazbanou Nozari, Brenda C Rapp

Research output: Contribution to journalArticle

Abstract

Using word learning as an example of a complex system, we investigated how differences in the structure of the subcomponents in which learning occurs can have significant consequences for the challenge of integrating new information within such systems. Learning a new word involves integrating information into the two key stages/subcomponents of processing within the word production system. In the first stage, multiple semantic features are mapped onto a single word. Conversely, in the second stage, a single word is mapped onto multiple segmental features. We tested whether the unitary goal of word learning leads to different local outcomes in these two stages because of their reversed mapping patterns. Neurotypical individuals (N = 17) learned names and semantic features for pictures of unfamiliar objects presented in semantically related, segmentally related and unrelated blocks. Both similarity types interfered with word learning. However, feature learning was differentially affected within the two subcomponents of word production. Semantic similarity facilitated learning distinctive semantic features (i.e., features unique to each item), whereas segmental similarity facilitated learning shared segmental features (i.e., features common to several items in a block). These results are compatible with an incremental learning model in which learning not only strengthens certain associations but also weakens others according to the local goals of each subcomponent. More generally, they demonstrate that the same overall learning goal can lead to opposite learning outcomes in the subcomponents of a complex system. The general principles uncovered may extend beyond word learning to other complex systems with multiple subcomponents.

Original languageEnglish (US)
JournalJournal of Experimental Psychology: Learning Memory and Cognition
DOIs
StateAccepted/In press - Jul 23 2018

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Learning
learning
Semantics
semantics
Cognitive Systems
Word Processing
Word Learning
Names
Complex Systems
Semantic Features
Word Production

Keywords

  • Blocked cyclic naming
  • Incremental learning
  • Segmental similarity
  • Semantic similarity
  • Word learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Linguistics and Language

Cite this

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title = "Learning in Complex, Multi-Component Cognitive Systems: Different Learning Challenges Within the Same System",
abstract = "Using word learning as an example of a complex system, we investigated how differences in the structure of the subcomponents in which learning occurs can have significant consequences for the challenge of integrating new information within such systems. Learning a new word involves integrating information into the two key stages/subcomponents of processing within the word production system. In the first stage, multiple semantic features are mapped onto a single word. Conversely, in the second stage, a single word is mapped onto multiple segmental features. We tested whether the unitary goal of word learning leads to different local outcomes in these two stages because of their reversed mapping patterns. Neurotypical individuals (N = 17) learned names and semantic features for pictures of unfamiliar objects presented in semantically related, segmentally related and unrelated blocks. Both similarity types interfered with word learning. However, feature learning was differentially affected within the two subcomponents of word production. Semantic similarity facilitated learning distinctive semantic features (i.e., features unique to each item), whereas segmental similarity facilitated learning shared segmental features (i.e., features common to several items in a block). These results are compatible with an incremental learning model in which learning not only strengthens certain associations but also weakens others according to the local goals of each subcomponent. More generally, they demonstrate that the same overall learning goal can lead to opposite learning outcomes in the subcomponents of a complex system. The general principles uncovered may extend beyond word learning to other complex systems with multiple subcomponents.",
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