The goal of this program of research is to determine how the developing human infant forms representations of the visual world. The focus of the research is on a class of powerful learning mechanisms that have been shown by the Principal Investigator and his colleagues to rapidly extract from sequences of auditory stimuli the statistical properties that form coherent units (e.g., words and melodies). Visual statistical learning will be studied to determine whether a similarly powerful set of mechanisms is present in a modality other than audition. Infants ranging in age from 3- to 12-months of age, as well as adults, will be tested on a variety of statistical learning tasks in which small visual shapes are arranged into scenes. At issue is how infants and adults learn that some of these shapes appear together (co-occur) across many different scenes, forming the basic building blocks for representing those scenes in memory. Four different techniques will be used with infants. The primary technique involves the repeated presentation of a sequence of 16-28 different scenes composed of 3-6 different shapes. After a decline in looking time (habituation) to these displays, infants will be presented with test displays containing coherent (high statistical relatedness) and incoherent (unrelated) shapes that were embedded in the scenes. The other three techniques involve forced-choice preferential looking, automated corneal reflection eye-tracking, and anticipatory eye-movements to learned categories. These techniques will be used to determine whether newly learned features activate attention in cluttered scenes, how these features are learned when low-level properties of the scenes compete for attention, how variations over time in the input statistics affect the accuracy of feature learning, and how other perceptual constraints affect feature learning. A key hypothesis of the Pl's statistical approach will be tested -- that learners represent the largest coherent unit in a complex array of elements, rather than also representing all of the embedded elements that are redundant with this larger unit. Taken together, these proposed studies will reveal how infants and adults learn new information from complex visual scenes and represent that information in a computationally efficient manner. Failure to learn efficiently could lead to deficits in the early phases of learning in infancy and negatively affect the formation of higher level categories.
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| Wu, Rachel; McGee, Brianna; Rubenstein, Madelyn et al. (2018) Emergence of the benefits and costs of grouping for visual search. Psychophysiology 55:e13087 |
| Culbertson, Jennifer; Newport, Elissa L (2017) Innovation of Word Order Harmony Across Development. Open Mind (Camb) 1:91-100 |
| Bankieris, Kaitlyn R; Bejjanki, Vikranth Rao; Aslin, Richard N (2017) Sensory cue-combination in the context of newly learned categories. Sci Rep 7:10890 |
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| Karuza, Elisabeth A; Emberson, Lauren L; Roser, Matthew E et al. (2017) Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes. J Cogn Neurosci 29:1963-1976 |
| Bergelson, Elika; Aslin, Richard (2017) Semantic Specificity in One-Year-Olds' Word Comprehension. Lang Learn Dev 13:481-501 |
| Aslin, Richard N (2017) Statistical learning: a powerful mechanism that operates by mere exposure. Wiley Interdiscip Rev Cogn Sci 8: |
| Reeder, Patricia A; Newport, Elissa L; Aslin, Richard N (2017) Distributional learning of subcategories in an artificial grammar: Category generalization and subcategory restrictions. J Mem Lang 97:17-29 |
| Bergelson, Elika; Aslin, Richard N (2017) Nature and origins of the lexicon in 6-mo-olds. Proc Natl Acad Sci U S A 114:12916-12921 |
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