Rick O. Gilmore

Professor of Psychology

The Pennsylvania State University

Databrary.org

Biography

Rick Gilmore is Professor of Psychology at The Pennsylvania State University. He studies the development of visual perception in infants, children, and adults using behavioral, neural imaging, and computational methods, as described more fully on his lab website. He co-founded and co-directs the Databrary.org data library, co-founded the Penn State Social, Life, & Engineering Sciences Imaging Center (SLEIC), and advocates for more open, transparent, and reproducible scientific practices, especially in psychology and neuroscience.

Interests

Perception and Action
Developmental Neuroscience
Computational Neuroscience
Open Science
AI and ML in Behavioral Science

Education

Ph.D. in Psychology and Cognitive Neuroscience, 1997

Carnegie Mellon University
BA cum laude in Cognitive Science, 1985

Brown University

Selected Publications

They get rhythm--Who could ask for anything more? Commentary on Brakke & Pacheco, 2019

Psychology is harder than physics, and developmental psychology is harder still. The monograph by Karen Brakke and Matheus Pacheco (2019) reveals some of the reasons why, and its many positive features emphasize the virtues of the extended monograph form and the utility of focusing on the often complex details of how seemingly simple behaviors unfold in time.

Rick O. Gilmore

MM, 2019

Source Document

AutoViDev: A computer-vision framework to enhance and accelerate research in human development

Interdisciplinary exchange of ideas and tools can accelerate scientific progress. For example, findings from developmental and vision science have spurred recent advances in artificial intelligence and computer vision. However, relatively little attention has been paid to how artificial intelligence and computer vision can facilitate research in developmental science. The current study presents AutoViDev—an automatic video-analysis tool that uses machine learning and computer vision to support video-based developmental research. AutoViDev identifies full body position estimations in real-time video streams using convolutional pose machine-learning algorithms. AutoViDev provides valuable information about a variety of behaviors, including gaze direction, facial expressions, posture, locomotion, manual actions, and interactions with objects. We present a high-level architecture of the framework and describe two projects that demonstrate its usability. We discuss the benefits of applying AutoViDev to large-scale, shared video datasets and highlight how machine learning and computer vision can enhance and accelerate research in developmental science.

Ori Ossmy, Rick O. Gilmore, Karen E. Adolph

In CVC 2019, 2019

DOI