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  1. Human Semi-Supervised Learning.Bryan R. Gibson, Timothy T. Rogers & Xiaojin Zhu - 2013 - Topics in Cognitive Science 5 (1):132-172.
    Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and (...)
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  • The Effects of Feature-Label-Order and Their Implications for Symbolic Learning.Michael Ramscar, Daniel Yarlett, Melody Dye, Katie Denny & Kirsten Thorpe - 2010 - Cognitive Science 34 (6):909-957.
    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 (...)
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  • Linguistic Self‐Correction in the Absence of Feedback: A New Approach to the Logical Problem of Language Acquisition.Michael Ramscar & Daniel Yarlett - 2007 - Cognitive Science 31 (6):927-960.
    In a series of studies children show increasing mastery of irregular plural forms (such as mice) simply by producing erroneous over‐regularized versions of them (such as mouses). We explain this phenomenon in terms of successive approximation in imitation: Children over‐regularize early in acquisition because the representations of frequent, regular plural forms develop more quickly, such that at the earliest stages of production they interfere with children's attempts to imitatively reproduce irregular forms they have heard in the input. As the strength (...)
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  • How Young Children Learn From Examples: Descriptive and Inferential Problems.Charles W. Kalish, Sunae Kim & Andrew G. Young - 2012 - Cognitive Science 36 (8):1427-1448.
    Three experiments with preschool- and young school-aged children (N = 75 and 53) explored the kinds of relations children detect in samples of instances (descriptive problem) and how they generalize those relations to new instances (inferential problem). Each experiment initially presented a perfect biconditional relation between two features (e.g., all and only frogs are blue). Additional examples undermined one of the component conditional relations (not all frogs are blue) but supported another (only frogs are blue). Preschool-aged children did not distinguish (...)
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  • Impact of feature saliency on visual category learning.Rubi Hammer - 2015 - Frontiers in Psychology 6.
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