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Poster F21

My Brain Matters: How Multimodal Intra-Individual Classifiers Reliably Predict Attention, While Inter-Individual Classifiers Do Not

Poster Session F - Tuesday, April 16, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Joshua Friedman1 (, Shiang-Yun Chuko2, Lily Penn3, Hafsah Shaik4, Conor Lee Shatto2, John Thorp2, Xianjue Huang5, Hengbo Tong5, Xiaofu He6, Alfredo Spagna2; 1Teachers College, Columbia University, 2Department of Psychology, Columbia University, 3Cognitive Science, Columbia University, 4Quantitative Methods in the Social Sciences, Columbia University, 5Department of Statistics, Columbia University, 6Department of Psychiatry, Columbia University

Introduction. While the nature and impacts of attention have been hotly debated in the neuroscience community since the field’s origins, the nature of classroom attention, and how students deploy it dynamically over time to learn effectively, is still a nascent field by comparison. Method. We measured brain activity via mobile EEG headsets, facial action amplitudes, posture, and trained observer ratings in real-time in a real classroom during lecture activities to compare the predictability of objectively defined attention via in-class quiz performance and subjectively defined attention via in-class self-reports. Results. Our findings suggest that objectively defined attention is better predicted by the combination of these data than subjectively defined attention, and further, that predicting attention intra-individually (i.e., using subject A’s brain, face, body, and observer-rated data at time t to predict subject A’s attention at time t + 1 (or time t + 100) is highly reliable, while predicting attention inter-individually (i.e., using subject A’s data to predict subject B’s attention) is highly unreliable. Discussion. Our findings suggest that in-classroom models and systems designed to predict or provide feedback to teachers or students on the level of students’ attention in the classroom requires, with current technology, training classifiers on every student in the classroom, rather than a subset of them. Further, they suggest that the combination of brain, face, body, and observer data better explain variation in objective performance than subjective reports, and this carries implication for studies that exclude one or the other form of data in operationalizing classroom attention.

Topic Area: ATTENTION: Other


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April 13–16  |  2024