Rachel Wu - US grants

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

Attention and learning are two of the most important aspects of cognition. While studying attention and learning separately has its benefits, it is also misleading. In the past few years, there has emerged a new wave of research demonstrating that attention constrains learning and that learning guides future attention. The studies in these two areas span disparate fields (developmental psychology, cognitive neuroscience, behavioral neuroscience, computational modeling). Although researchers are asking the same questions across different fields, in general, they do not attend the same conferences, rarely cite each other, and in most cases do not even know about each other's work. This 2-day workshop will bring together these diverse researchers to catalyze further interaction and promote innovative collaborative research. The workshop also will include moderators, who will encourage constructive critiques and discussion of theoretical and methodological limitations for the different approaches.

This workshop will solidify, stabilize, and grow this critical research area (especially for a new generation of researchers) and thereby promote a more nuanced, ecologically valid, and biologically informed understanding of attention-learning interactions and mechanisms. Research on attention and learning interactions is crucial for understanding a wide range of developmental processes, including infants' development of social skills, children's memory, perceptual-motor skill acquisition, and learning in school. It is also increasingly apparent that developmental disabilities originally thought to be attention-based, such as ADHD, interact heavily with learning difficulties, though the exact mechanisms are still unclear.

The mental health and well-being of older adults are being threatened by the COVID-19 social distancing requirements that have limited social connectivity. For older adults, long-term social isolation predicts cognitive decline trajectories, reduced subjective well-being, and increased mortality. Thus, the COVID-19 global pandemic could intensify negative aging trajectories, even for healthy older adults. The proposed research will investigate factors that lead to or mitigate against social isolation and loneliness amid the current physical distancing restrictions. The primary hypothesis is that resilience across adulthood is dependent on two theoretically-derived factors: engagement in novel skill learning and positive personal beliefs. The results of these studies could guide the design of future interventions, such as supportive learning opportunities through technology. The unknown duration of the physical distancing restrictions, and the potential for future waves of infection drive the urgency of this research to develop enhanced resilience pathways.

This RAPID proposal seeks to examine social distancing in older adults and other potentially vulnerable populations through three aims: 1) Investigate how and to what extent different demographic groups (e.g. age, SES) are staying socially connected despite physical distancing; 2) Evaluate how learning and adaptive behaviors, and personal beliefs about age/abilities, predict successful social connectivity, higher subjective well-being, and lower levels of isolation and loneliness during the pandemic; and 3) Conduct match pair-comparisons with older adults who previously participated in a learning intervention promoting adaptation and positive beliefs. Integrating beliefs and behaviors to predict outcomes is central to Social Cognitive Theory. The current research will collect data before and after restrictions are revised to assess an extended conceptual model of this theory that focuses specifically on novel skill learning for adaptation. Findings from this project will inform development of infrastructures to better support older adults under social distancing practices of COVID-19 and other future crises.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.