Cognitive Science 34 (6):909-957 (2010)
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
|Keywords||Representation Concepts Prediction Language Computational modeling Learning|
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References found in this work BETA
The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology.Jerry A. Fodor - 2000 - MIT Press.
Citations of this work BETA
Competitive Processes in Cross‐Situational Word Learning.Daniel Yurovsky, Chen Yu & Linda B. Smith - 2013 - Cognitive Science 37 (5):891-921.
The Myth of Cognitive Decline: Non‐Linear Dynamics of Lifelong Learning.Michael Ramscar, Peter Hendrix, Cyrus Shaoul, Petar Milin & Harald Baayen - 2014 - Topics in Cognitive Science 6 (1):5-42.
Seeking Predictions From a Predictive Framework.T. Florian Jaeger & Victor Ferreira - 2013 - Behavioral and Brain Sciences 36 (4):359 - 360.
Highlighting in Early Childhood: Learning Biases Through Attentional Shifting.Joseph M. Burling & Hanako Yoshida - 2017 - Cognitive Science 41 (S1):96-119.
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