Beyond Nonutilization: Irrelevant Cues Can Gate Learning in Probabilistic Categorization

In probabilistic categorization, also known as multiple cue probability learning (MCPL), people learn to predict a discrete outcome on the basis of imperfectly valid cues. In MCPL, normatively irrelevant cues are usually ignored, which stands in apparent conflict with recent research in deterministic categorization that has shown that people sometimes use irrelevant cues to gate access to partial knowledge encapsulated in independent partitions. The authors report 2 experiments that sought support for the existence of such knowledge partitioning in probabilistic categorization. The results indicate that, as in other areas of concept acquisition (such as function learning and deterministic categorization), a significant proportion of participants partitioned their knowledge on the basis of an irrelevant cue. The authors show by computational modeling that knowledge partitioning cannot be accommodated by 2 exemplar models (Generalized Context Model and Rapid Attention Shifts 'N Learning), whereas a rule-based model (General Recognition Theory) can capture partitioned performance. The authors conclude by pointing to the necessity of a mixture-of-experts approach to capture performance in MCPL and by identifying reduction of complexity as a possible explanation for partitioning.