The Interaction Between Chunking and Stimulus Complexity in Infant Visual Statistical Learning

Human infants are known to learn statistical regularities of the sensory environment implicitly in various perceptual domains. Visual statistical leaning studies have illustrated that this learning is highly sophisticated and well_approximated by optimal probabilistic chunking of the unfamiliar input. However, the emergence and unitization of such perceptual chunks at an early age and their relation to stimulus complexity have not been investigated before. This study examines how 8_month_old infants can extract statistical relationships within more complex, hierarchically structured visual scenes and how unitization of chunks is linked to familiarity performance. In the first experiment, infants were habituated to quadruplet scenes composed of a triplet of elements always appearing in the same relative spatial arrangement and one noise element connected to the triplet in various ways. After meeting a criterion of habituation, in each of several test trials, infants saw one original triplet and a new triplet containing a rearrangement of familiar elements. Contrary to earlier results obtained with pairs rather than triplets, infants did not show a significant preference for either test stimulus (N = 20, p > 0.9). In a second experiment, infants were habituated using the same quadruplet scenes, but during the test, they saw one of the habituation quadruplets, and a second quadruplet in which the associated noise element was switched with a noise element from another triplet. Infants that habituated (N = 13) to the familiar quadruplet looked longer to the novel quadruplets, indicating they can recognize a change of one single element (p = 0.026), whereas non_habituating infants (N = 9) showed no preference (p > 0.9). These results suggest that as stimulus complexity increases, infants’ ability to learn and unitize chunks becomes limited, even though they are perfectly able to encode the structure of the scene. Apparently, unitization and the ability to use embedded features in more general contexts emerge after encoding itself is already operational.