Assistant Professor, Department of Human Physiology
Ph.D. University of California, San Diego
B.A Tufts University
Ian Greenhouse’s research examines how humans initiate and cancel movement. His lab combines behavioral testing with electrophysiology, neuroimaging, and brain stimulation in healthy and clinical populations. His current research explores the relationship between the inhibitory neurochemical gamma-aminobutyric acid (GABA) and motor performance.
Dr. Greenhouse earned his BA in Psychology at Tufts University and his Ph.D. at the University of California, San Diego. He completed postdoctoral training at the University of California, Berkeley. He joined the Department of Human Physiology at the University of Oregon in the Fall of 2017.
A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.
Elife. 2019 Apr 29;8:
Authors: Verbruggen F, Aron AR, Band GP, Beste C, Bissett PG, Brockett AT, Brown JW, Chamberlain SR, Chambers CD, Colonius H, Colzato LS, Corneil BD, Coxon JP, Dupuis A, Eagle DM, Garavan H, Greenhouse I, Heathcote A, Huster RJ, Jahfari S, Kenemans JL, Leunissen I, Logan GD, Matzke D, Morein-Zamir S, Murthy A, Li CR, Paré M, Poldrack RA, Ridderinkhof KR, Robbins TW, Roesch M, Rubia K, Schachar RJ, Schall JD, Stock AK, Swann NC, Thakkar KN, van der Molen MW, Vermeylen L, Vink M, Wessel JR, Whelan R, Zandbelt BB, Boehler CN
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide twelve easy-to-implement consensus recommendations and point out the problems that can arise when these are not followed. Furthermore we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
PMID: 31033438 [PubMed - as supplied by publisher]