Biography

Richard Aslin is a Research Scientist at Haskins Laboratories and the Department of Psychology at Yale University. Prior to joining Haskins and Yale in 2017 he was on the faculty at the University of Rochester for 33 years, where he established the Rochester BabyLab and was Director of the Rochester Center for Brain Imaging for 14 years. Aslin has published widely in several sub-areas of infant development, including perceptual and motor systems, speech and language acquisition, and statistical learning. In the past decade, Aslin has focused on extending his research from behavioral methods to neuroimaging measures using fMRI, EEG, and fNIRS. His collaborative work has shown that the infant brain deploys predictive signals to encode expected events and that both EEG and fNIRS measures have sufficient fidelity to “decode” events on a trial-by-trial basis. Aslin has been the recipient of several major awards, including the APA Distinguished Scientific Contributions Award (2014) and the APS Mentor Award for Lifetime Achievement (2015), and several honors, including election to the American Academy of Arts and Sciences (2006) and the National Academy of Sciences (2013).

Website: https://haskinslabs.org/people/richard-aslin

The Value-Added of Neuroimaging Data for Studies of Infant Development

Session Type: Invited Talk

Abstract

Despite the limited behavioral repertoire of human infants, detailed studies of how infants respond to various stimuli and control their motor responses have formed the core of our understanding of the major milestones achieved in early development. Over the past few decades, tremendous progress has been made in describing developmental trajectories at the behavioral level and the putative neural mechanisms that enable those behaviors to emerge. However, these neural mechanisms cannot be verified without direct access to underlying brain states. I will review the three neuroimaging methods suitable for use with human infants -- EEG, MRI, NIRS -- and describe recent progress in using machine-learning techniques to extract data about brain states, at both the group and individual subject level. Crucially, these neuroimaging data hold the promise of clarifying whether developmental change observed at the behavioral level is the result of a qualitative change in a neural mechanism or a quantitative change in intrinsic neural noise. At issue is the value-added of neuroimaging data for revealing the causal principles -- maturational or experiential -- that affect infant development.

Sub Unit

• Invited Program

Moderator

• Roberta M Golinkoff, University of Delaware

Speaker

• Richard N. Aslin, Haskins Laboratories

Introducer

• Ethan Kutlu, University of Iowa