Estimating the strength of unlabeled information during semi-supervised learning


Semi-supervised category learning is when participants make classification judgements while receiving feedback about the right answers on some trials (labeled stimuli) but not others (unlabeled stimuli). Sporadic feedback is common outside the laboratory, and it is important to understand how people learn in this setting. While there are numerous recent studies, the strength and robustness of semi-supervised learning effects remain unclear, particularly when labeled and unlabeled stimuli are dispersed across learning. We designed an experiment, using simple unidimensional category learning, that allows us to measure the relative contribution of labeled and unlabeled experience. Based on an analysis of this task, we find that an unlabeled stimulus is worth more than 40% of a labeled stimulus.

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