Semisupervised category learning: the impact of feedback investigated in the perceptual representation system

Abstract

In category learning, the evidence to the existence of different classification systems is growing. Each system is associated with different cognitive processes, operating on different category structures (e.g., Ashby & O'Brien, 2005; Ashby & Maddox, 2005). The properties of the systems are mostly investigated with the supervised classification paradigm. In this paradigm, participants make a categorization which is followed immediately by feedback. Recently, the more ecological plausible semisupervised category learning is introduced (Vandist, De Schryver & Rosseel, 2009). In this type of learning, feedback follows after a (pre-specified) percentage of trials only. By that, the impact of feedback (or the lack of feedback) on the learning process can be studied. The purpose of this poster is to examine semisupervised category learning in the perceptual representation system, which is specialized to the A not A category structure (Casale & Ashby, 2008). To discover the impact of the no-feedback trials on the learning process, semisupervised category learning is compared with supervised and unsupervised learning.


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