Academic journal article Perception and Psychophysics

Semisupervised Category Learning: The Impact of Feedback in Learning the Information-Integration Task

Academic journal article Perception and Psychophysics

Semisupervised Category Learning: The Impact of Feedback in Learning the Information-Integration Task

Article excerpt

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.

In category learning, there are two supervised learning paradigms: the supervised classification learning paradigm, and the supervised observational learning paradigm. Current research is dominated by the supervised classification learning paradigm. The typical ingredients of this paradigm are that (1) participants know in advance the number of contrasting categories; (2) stimuli are presented one at a time; (3) participants classify each stimulus; and (4) feedback (showing the correct category label of the stimulus) follows immediately. (See Shepard, Hovland, & Jenkins, 1961, for a description of the basic experiment.) Numerous studies have demonstrated that, using the supervised classification learning paradigm, participants can learn most kinds of complex category tasks (e.g., Ashby & Gott, 1988; Ashby & Maddox, 1990, 1992; Ashby, Queller, & Berretty, 1999; Medin & Schwanenflugel, 1981). In the supervised observational learning paradigm, participants are shown the category label of each stimulus prior to its presentation, and simply confirm the category label with an appropriate buttonpress (see Ashby, Maddox, & Bohil, 2002, for a description of the basic experiment). Learning performance is tested in test trials, in which neither feedback nor the category label is given. Results indicate successful learning in simple and in complex category tasks (Cincotta & Seger, 2007). However, using complex category tasks, higher accuracy rates are obtained in supervised feedback learning when training progresses (Ashby et al., 2002).

Few studies have focused on unsupervised category learning. In this setting, neither feedback (as in the supervised classification learning paradigm) nor category labels (as in the supervised observational learning paradigm) are given. Unsupervised learning can be intentional or incidental. In incidental learning, the main task does not require any category responses; instead, participants are asked to rate the pleasantness of the stimuli, for example, or to judge the relative positions of the stimuli. Yet these interactions with the stimuli can result in category formation, even though participants may have never received category information (for more details, see Love, 2002, 2003). By contrast, the main task in intentional category learning is the categorization of the stimuli. Several paradigms have been developed to study intentional unsupervised category learning. Perhaps the most used paradigm is the free sorting task, in which stimuli are presented simultaneously. The task of the participants is to order the stimuli in a way that seems natural to them (see, e. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.