On the acquisition of abstract knowledge: Structural alignment and explication in learning causal system categories

Micah B. Goldwater*, Dedre Gentner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

This research studies a relatively unexplored aspect of expertise - the ability to detect causal relational patterns in multiple contexts - and demonstrates learning processes that foster this ability. Using the Ambiguous Sorting Task (AST), in which domain information competes with causal patterns, we previously found that science experts spontaneously noticed and sorted by causal patterns such as positive feedback, while novices sorted primarily by content domain. We investigated two kinds of learning experiences that we claim are needed to achieve high fluency in detecting key cross-domain patterns. We found that direct explication of example phenomena increased people's accuracy in depicting the examples, but did not increase sensitivity to the causal patterns in new examples. However, analogical comparison between parallel examples did lead to greater propensity to detect the causal patterns across diverse examples. Combining within-example explication with between-example alignment led to the greatest gains in generalized sensitivity to causal patterns.

Original languageEnglish (US)
Pages (from-to)137-153
Number of pages17
JournalCognition
Volume137
DOIs
StatePublished - Apr 1 2015

Keywords

  • Analogy
  • Categorization
  • Causal learning
  • Education

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Cognitive Neuroscience

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