Alignment effects on learning multiple, use-relevant classification systems

People often learn multiple classification systems that are relevant to some goal or use. We compared conditions in which subclassification within a category hierarchy was predicted by values on either the same (alignable) or different (nonalignable) dimensions between category hierarchies. The results indicated that learning in alignable conditions occurred in fewer blocks and with fewer errors than did learning in nonalignable conditions. This facilitation was not the result of differences between conditions in the representations learned by the participants, the number of dimensions needed for subclassification (Experiment 1), or the objective complexity of the learning task (Experiment 2). The facilitated learning in the alignable conditions appears to reflect a commitment on the part of the learner to alignment: the belief that the structure relevant to the use of one category system will also be relevant to the use of a comparable system.