Purchase this article with an account.
Jordan W. Suchow, Denis G. Pelli; Learning to identify letters: Generalization in high-level perceptual learning. Journal of Vision 2005;5(8):712. doi: 10.1167/5.8.712.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Learning to identify letters is crucial to reading. The benefit of training on most perceptual tasks is highly task and location specific. However, unlike the specificity of perceptual learning, many studies of conceptual learning have found a great deal of generalization between related tasks and stimuli. Three new experiments explore the specificity of letter learning, assessing transfer across letters (Exp. 1), from part to whole (Exp. 2), and across the visual field (Exp. 3). In Experiment 1, observers learned to identify letters in one subset of a foreign alphabet (Chinese) before learning a second subset of that same alphabet. Observers are found to receive no benefit from having partially learned the alphabet, proving that letter learning is letter specific. In Experiment 2, observers trained on the components of Chinese characters (i.e. brushstrokes, specific combinations of features, and radicals, specific combinations of brushstrokes) before learning the characters themselves. The results show that observers learning to identify a new object need not relearn combinations of features with which they are already familiar; in fact, knowledge of an object's parts instills a more effective learning strategy in the observer. Experiment 3 explores the specificity of letter learning with regard to location in central and peripheral vision. Observers' efficiency for foreign letter identification (Armenian) is found to be highly dependent on eccentricity of training and testing. In sum, the results reveal two mechanisms that identify letters: a process in the central visual field that recognizes an object by parts and a ubiquitous process that recognizes objects holistically.
Supported by grant EY04432 to Denis Pelli.
This PDF is available to Subscribers Only