Category

We investigated human category learning from partial information provided as equivalence constraints. Participants learned to classify stimuli on the basis of either positive or negative equivalence constraints, that is, when informed that two exemplars belong to the same category or to different categories, respectively. Knowing that in natural contexts positive constraints are usually informative while negative constraints are rarely so, we suspected that participants would not use the two types of constraints in similar ways, even in a setting in which the amount of information in the two types of constraints is identical and sufficient for perfect performance. We found that in general, people can use the two types of constraints for category learning. Further analysis revealed that when participants were provided with highly informative positive constraints, categorization performance of most participants was moderate and normally distributed. In contrast, there was a dichotomy of participants who were provided with highly informative negative constraints, with some achieving even higher performances, while that of others was significantly poorer. These results, together with those of a battery of controls, support the following conclusions: (i) People use positive constraints more intuitively, although they fail to use them perfectly. (ii) The use of negative constraints enables a less natural, but potentially more accurate categorization strategy, which many participants failed to implement even in the current simplified setting. These results are consistent with the view that people are naturally biased towards similarity-based categorization strategies (e.g. prototypes or exemplars) rather than rule-based strategies