Graduate studies at Western
Cognitive Science 34 (6):909-957 (2010)
|Abstract||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|
|Categories||categorize this paper)|
|Through your library||Configure|
Similar books and articles
Reiko Yakushijin & Robert A. Jacobs (2011). Are People Successful at Learning Sequences of Actions on a Perceptual Matching Task? Cognitive Science 35 (5):939-962.
Knud Illeris (ed.) (2009). Contemporary Theories of Learning: Learning Theorists -- In Their Own Words. Routledge.
Philippe G. Schyns, Robert L. Goldstone & Jean-Pierre Thibaut (1998). The Development of Features in Object Concepts. Behavioral and Brain Sciences 21 (1):1-17.
Scott P. Johnson (2010). How Infants Learn About the Visual World. Cognitive Science 34 (7):1158-1184.
Jiajie Zhang Todd R. Johnson Hongbin Wang (1998). The Relation Between Order Effects and Frequency Learning in Tactical Decision Making. Thinking and Reasoning 4 (2):123 – 145.
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
Stevan Harnad & Stephen J. Hanson, Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding.
John E. Hummel (2010). Symbolic Versus Associative Learning. Cognitive Science 34 (6):958-965.
Added to index2010-08-11
Total downloads10 ( #114,476 of 739,396 )
Recent downloads (6 months)0
How can I increase my downloads?