The Effects of Feature-Label-Order and Their Implications for Symbolic Learning

Michael Ramscar, Daniel Yarlett, Melody Dye, Katherine Denny, Kirsten Thorpe

Research output: Contribution to journalArticlepeer-review

156 Scopus citations


Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning-in particular, word learning-in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference.

Original languageEnglish (US)
Pages (from-to)909-957
Number of pages49
JournalCognitive Science
Issue number6
StatePublished - Aug 1 2010
Externally publishedYes


  • Computational modeling
  • Concepts
  • Language
  • Learning
  • Prediction
  • Representation

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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