Visual Statistical Learning in Infants

Visual Statistical Learning in Infants Natasha Zoe Kirkham (nzk2@cornell.edu) Department of Psychology, Cornell University Ithaca, NY 14853 USA Jonathan Andrew Slemmer (jas234@cornell.edu) Department of Psychology, Cornell University Ithaca, NY 14853 USA Scott P. Johnson (sj75@cornell.edu) Department of Psychology, Cornell University Ithaca, NY 14853 USA Abstract 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 novelty- preference 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 7- and 8-month-olds, these results can be taken as preliminary evidence of a domain general learning mechanism. Introduction One of the fundamental questions asked by developmental psychologists concerns how infants learn so much in so little time, with apparently very little explicit instruction. Research suggests that, as adults, we are remarkably good at implicit learning (e.g., see Stadler & Frensch, 1998 for a review of the implicit learning literature). Implicit learning can be defined as non-conscious facilitation of task performance due to information acquired during previous exposure. Given the robust nature of implicit learning skills in adults, it is perhaps a reasonable assumption that these skills play some role in early learning. The research on implicit learning in children suggests that they too show implicit sequence learning to the same degree as adults (Meulemans, Van der Linden, & Perruchet, 1998; Thomas, 1998). In other words, children showed increased reaction time in a task that contained a predictable sequence, and, like adults, did not have explicit knowledge of this sequence. There were no reports in the literature, however, with participants younger than 3 years of age until recent studies of statistical learning, a form of implicit learning based on statistical regularities in the perceptual environment. Saffran, Aslin, and Newport (1996) and Alsin, Saffran, and Newport (1998) presented evidence that 8- month-old infants determine the statistical probability of neighboring speech sounds based on a 2-minute exposure. Infants heard four three-syllable words composed of 12 unique syllables (e.g., tupiro, golabu, dapiku, and tilado), presented in a continuous stream in random order (e.g., dapikutupirotiladogolabutupiro …). Between-word spaces were removed, as were all other cues to word boundaries (e.g., rhythm, intonation, and stress). Thus, the only cues to word boundaries were the transitional probabilities between syllable pairs. For example, the transitional probability of tu-pi in this corpus is 1.00, because pi always follows tu within the word tupiro, whereas the probability of ro-go is .33, because golabu is one of three words that can follow tupiro. After exposure, Saffran et al. (1996) presented infants with both familiar words from the corpus and nonwords. Nonwords were created by combining the last syllable of one word with the first two syllables of a second word (e.g., rogola and butupi). Infants showed greater interest in the nonwords than in the words. On the logic that infants often exhibit a post-familiarization novelty preference (Bornstein, 1985), these results suggest that they detected the difference between words and nonwords. This outcome is necessarily based on learning of the transitional probabilities defining the stimuli.