Adults’ self-directed learning of an artificial lexicon: The dynamics of neighborhood reorganization

Bardhan, N. P. (2010). Adults’ self-directed learning of an artificial lexicon: The dynamics of neighborhood reorganization. PhD Thesis, University of Rochester, Rochester, New York.
Artificial lexicons have previously been used to examine the time course of the learning and recognition of spoken words, the role of segment type in word learning, and the integration of context during spoken word recognition. However, in all of these studies the experimenter determined the frequency and order of the words to be learned. In three experiments, we asked whether adult learners choose to listen to novel words in a particular order based on their acoustic similarity. We use a new paradigm for learning an artificial lexicon in which the learner, rather than the experimenter, determines the order and frequency of exposure to items. We analyze both the proportions of selections and the temporal clustering of subjects' sampling of lexical neighborhoods during training as well as their performance during repeated testing phases (accuracy and reaction time) to determine the time course of learning these neighborhoods. In the first experiment, subjects sampled the high and low density neighborhoods randomly in early learning, and then over-sampled the high density neighborhood until test performance on both neighborhoods reached asymptote. A second experiment involved items similar to the first, but also neighborhoods that are not fully revealed at the start of the experiment. Subjects adjusted their training patterns to focus their selections on neighborhoods of increasing density was revealed; evidence of learning in the test phase was slower to emerge than in the first experiment, impaired by the presence of additional sets of items of varying density. Crucially, in both the first and second experiments there was no effect of dense vs. sparse neighborhood in the accuracy results, which is accounted for by subjects’ over-sampling of items from the dense neighborhood. The third experiment was identical in design to the second except for a second day of further training and testing on the same items. Testing at the beginning of the second day showed impaired, not improved, accuracy, except for the consistently dense items. Further training, however, improved accuracy for some items to above Day 1 levels. Overall, these results provide a new window on the time-course of learning an artificial lexicon and the role that learners’ implicit preferences, stemming from their self-selected experience with the entire lexicon, play in learning highly confusable words.
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