Research Article Statistical Learning Using Real-World Scenes Extracting Categorical Regularities Without Conscious Intent

ABSTRACT—Recent work has shown that observers can parse streams of syllables, tones, or visual shapes and learn statistical regularities in them without conscious intent (e.g., learn that A is always followed byB).Here, we demonstrate that these statistical-learning mechanisms can operate at an abstract, conceptual level. In Experi-ments 1 and 2, observers incidentally learned which se-mantic categories of natural scenes covaried (e.g., kitchen scenes were always followed by forest scenes). In Experi-ments 3 and 4, category learning with images of scenes transferred to words that represented the categories. In each experiment, the category of the scenes was irrelevant to the task. Together, these results suggest that statistical-learning mechanisms can operate at a categorical level, enabling generalization of learned regularities using ex-isting conceptual knowledge. Such mechanisms may guide learning in domains as disparate as the acquisition of causal knowledge and the development of cognitive maps from environmental exploration. One of the primary tasks of the brain is to extract regularities from the environment in order to make inferences and guide behavior in novel situations. Indeed, sensitivity to statistical regularities plays an important role in various domains of per-ception and cognition, from preparing motor actions (Nissen & Bullemer, 1987) to parsing language (Trueswell, 1996) and making higher cognitive judgments, such as predicting the amount of money a movie will earn (Griffiths & Tenenbaum, 2006). Recent work on statistical learning has demonstrated that observers extract the covariance between syllables, tones, or shapes that appear in a predictable order or spatial arrangemen