The impact of training sequence and between-category similarity on unsupervised induction.
Studies of supervised categorization often show better learning when examples are presented in random alternation rather than massed by category, but such interleaving impairs learning in unsupervised tasks. The exemplar comparison hypothesis explains this result by assuming that people in unsupervised tasks discover generalizations about categories by comparing individual examples, and that interleaving increases the difficulty of such within-category comparisons. The category invention hypothesis explains the interleaving effect by assuming that people are more likely to merge or aggregate potentially separable categories when they are interleaved, and this initial failure to recognize separate categories then acts as an effective barrier to further learning. The present experiments show that the interleaving effect depends on the similarity or alignability of the presented categories. This result provides evidence in favour of the category invention hypothesis, which expects that highly dissimilar (nonalignable) categories will resist aggregation and hence will not be affected by interleaving. The nonmonotonic pattern of learning, and the interaction between sequence and similarity, observed in the alignable conditions of Experiment 3 were also consistent with category invention, but not with exemplar comparison. Implications are discussed for real-world learning, especially the relationship between exposure and learning and between supervised and unsupervised learning.
a Department of Psychology , California State University , San Bernardino , CA , USA.
John P
JP
Clapper
D000704:Analysis of Variance; D003210:Concept Formation / Q000502:physiology*; D003463:Cues; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D011939:Mental Recall / Q000502:physiology*; D011214:Practice, Psychological; D013334:Students; D014495:Universities; D014706:Verbal Learning
Categorization; Sequence; Similarity; Spacing; Unsupervised learning