Effects of biased hypothesis generation on self-directed category learning

Psychologists and educators have long pointed to myriad benefits of self-directed learning. Yet evidence of its efficacy in real-world domains is mixed and it remains unclear how it is constrained by basic perceptual and cognitive processes. Previous work suggests that, in particular, self-directed learning is affected by the way that people generate hypotheses as they learn. This study examines how biased hypothesis generation affects the learning of categorical rules, a basic building block of concept learning, through self-directed selection of training data. In both perceptual and abstract category learning tasks, participants' hypotheses regarding an unknown classification boundary were influenced by how features were represented. This bias had persistent effects on their ability to learn the underlying categorical relationship despite their opportunity to control the selection of training items. The results demonstrate that self-directed control can be beneficial for both perceptual and abstract category learning, but that the ability to discover rules of a particular form depends on how the learning environment guides the generation of new hypotheses. (PsycINFO Database Record (c) 2019 APA, all rights reserved).