Visual Statistical Learning in Infants

Statistical probability theory posits that we learn about regularly-occurring events in the perceptual environment by determining the likelihood of each event’s occurrence (Aslin, Saffran, & Newport, 1998). The current study investigates infants’ ability to extract properties of repetitive visual events and represent predictable combinations of visual elements. Using a noveltypreference paradigm, 2-, 5-, and 8-month-old infants were habituated to a continuous stream of colored shapes that were presented in a statistically predictable pattern, and then tested alternatively on the same sequence and a randomly-ordered sequence. The randomly-ordered sequence differed from the originally presented sequence only in between-shape transitional probabilities. At each age, infants demonstrated a significant novelty preference for the random sequence. In conjunction with Marcus, Vijayan, Rao, and Vishton (1999) and Saffran, Aslin, and Newport’s (1996) work looking at statistical learning in language with 7and 8-month-olds, these results can be taken as preliminary evidence of a domain general learning mechanism.