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In a standard supervised classification paradigm, stimuli are presented sequentially, participants make a classification, and feedback follows immediately. In this article, we use a semisupervised classification paradigm, in which feedback is given after a prespecified percentage of trials only. In Experiment 1, feedback was given in 100%, 0%, 25%, and 50% of the trials. Previous research reported by Ashby, Queller, and Berretty (1999) indicated that in an information-integration task, perfect accuracy was obtained supervised (100%) but not unsupervised (0%). Our results show that in both the 100% and 50% conditions, participants were able to achieve maximum accuracy. However, in the 0% and the 25% conditions, participants failed to learn. To discover the influence of the no-feedback trials on the learning process, the 50% condition was replicated in Experiment 2, substituting unrelated filler trials for the no-feedback trials. The results indicated that accuracy rates were similar, suggesting no impact of the no-feedback trials on the learning process. The possibility of ever learning in a 25% setting was also researched in Experiment 2. Using twice as many trials, the results showed that all but 2 participants succeeded, suggesting that only the total number of feedback trials is important. The impact of the semisupervised learning results for ALCOVE, COVIS, and SPEED models is discussed.

Semisupervised category learning: The impact of feedback in learning the information-integration task