MIT Media Lab, E14-525
Misunderstanding Detection demonstrates strategic interpretations from a lab-based study, a school-based study, and a home-based study in order to situate stronger interpersonal understandings of people diagnosed with autism spectrum disorder (ASD). Misunderstanding is typically the result of a biased hypothesis with missing information. For people equipped with hyper- or hypo-sensory capacity (e.g., many people diagnosed with ASD), over-aroused situations or "meltdowns" are often accompanied by misinterpretation of potential stressors in their everyday lives. A series of physiology-based technologies are implemented as a toolkit (e.g., providing in-situ visual and tactile feedback, or enabling interactive and analytical indexing collected data) for assisting the interpretations of individuals' arousal states along this discovery.
First, the lab-based Study is an ABAB single-case design experiment with direct replications focusing on class teachers' interpretations of students' arousal states. The goal of this study is to assess how real-time displays of student physiological activity (i.e., heart rate) affect teacher estimation of arousal and relaxation. The results suggest that arousal estimation varies as a function of how physiological information is displayed. Second, the school-based study presents a collaboration with an occupational therapist (OT) and three teenage participants. This study documents the iterated investigations of the OT's interpretations with/without the presence of students' physiological data (i.e., skin conductance data). This study demonstrates how participants' arousal information assists the OT in making judgments from a clinical perspective. Third, the home-based study is a participant-driven longitudinal study following Kanner's perspective of documenting "fascinating peculiarities." Lee arrives in a family as an ethnographer documenting a dynamic process of hypothesizing and interpreting situations in order to seek a dialogue with a young man who is able to name objects, but does not use language in a typical way.
This dissertation presents Misunderstanding Detection as an experimental method to calibrate typical assumptions about people diagnosed with ASD. With the intervention of physiology-based technologies, this research shows a novel approach of debugging reciprocal understandings under more naturalistic settings of experimental environments.
Host/Chair: Rosalind W. Picard
Ted Selker, Matthew Goodwin