LSLT: Language science research online
Join us for a panel discussion with researchers who transitioned their lab-based research online over the summer.
Arynn Byrd (HESP) is using a web app to remotely administer a sentence-picture matching task with children. The goal of the study is to evaluate if there are differences in how children who speak different dialects use verb knowledge to disambiguate the sentence. Her web app displays pictures, plays audio, and gathers reaction times and accuracy using the child's tablet.
Tyler Knowlton (LING) is using a web app and Amazon's Mechanical Turk to continue his research on people's interpretations of quantifier words like each and every. His online studies involve showing participants images and collecting simple behavioral measures like key presses and reaction times. E.g., in one task participants were shown three circles and asked to judge whether "every circle is green". After answering, the circles disappeared and returned with one color potentially changed. Participants were then asked to judge whether "the circles are the same color as before".
Cassidy Wyatt (LING), Maggie Kandel (Harvard), and Colin Phillips (LING/LSC) have been testing the feasibility of gathering spoken sentence production data online. They have run a side-by-side comparison of a study that they had previously run in the lab, to figure out whether they could collect good quality recordings, whether people would be able to follow elicitation instructions, and whether the error rates and timing patterns would be comparable in the lab and online. The results are very encouraging. They set up the study using a server in LSC, and have it configured so that other UMD groups can piggy-back on their setup, with some documentation already prepared.
