Human infants are confronted with a complex world that is filled with ambiguity. Not only are many different features and dimensions of information present in the environment, but these cues are often unrelated to any reinforcement or feedback. There are two solutions to learning in a complex and ambiguous environment: (a) innate constraints on the cues selected for processing (bottom-up), or (b) rapid learning-to-learn mechanisms that assess cues (top-down). Learned top-down mechanisms of information selection may be tuned more to specific task demands, and thus more useful for learning. Given how much infants have to learn over the first two years of life, it is not efficient to use mainly slow but precise (top-down) search methods. My hypothesis is that the developmental progression of learning how to learn requires using bottom-up information in a systematic way, while creating top-down buffers against bottom- up distraction. The experiments in the research plan will test this hypothesis, with each experiment evaluating an additional level of learning. Sophisticated behavioral techniques (i.e., both table- and head-mounted eye- tracking) and complementary state-of-the-art neuroimaging methods (i.e., functional near-infrared spectroscopy [fNIRS], measuring spatially-localized neural activation via non-invasive light probes on the scalp), as well as data mining tools applied to infant eye movement data, will examine how infants learn to learn from both computer displays and in naturalistic settings. There are four specific aims in this research program: 1) to establish a new, robust measure of learning with both behavioral and neural measures, 2) to investigate how attentional deployment can optimally improve learning, 3) to apply the learning paradigm to the natural environment, and 4) to conduct microanalyses on and to develop computational models of infant eye movements. The training component focuses on learning to use two state-of-the-art methods in infancy research (a head-mounted eye-tracker and fNIRS), and learning to use innovative data mining tools to analyze patterns of infant eye movements to link looking behavior to cognitive abilities. This training program is essential for the applicant's career goal of identifying the optimal strategies for learning to learn that will lead to training regimens for populations with learning difficulties. The findings will benefit researchers within the larger community of developmental science, as well as artificial intelligence, perceptual learning, education, animal learning, machine learning, and evolutionary psychology. This work will contribute to a foundational understanding of the dynamics of selective attention and learning in typical development, which in turn would inform populations with learning difficulties.
This multi-disciplinary research program will indicate signatures of optimal attentional deployment for efficient learning among distractions via converging evidence from behavioral and neuroimaging methods and data mining tools. This work will contribute to a foundational understanding of the dynamics of selective attention and learning in typical development, which in turn would inform populations with learning difficulties.