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Poster D150

Joint longitudinal and survival modeling predicts avoidance decisions

Poster Session D - Monday, April 15, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Brooke Staveland1 (, Julia Oberschulte2, Olivia Kim-McManus3,4, Jon Willie5,6, Peter Bunner5,6, Mohammad Dastjerdi7, Jack Lin8,9, Ming Hsu1,10, Robert Knight1,11; 1UC Berkeley, 2Department of Psychology, LMU Munich, 3Division of Neurology, Rady Children’s Hospital, 4Department of Neurosciences, UC San Diego, 5Department of Neurosurgery, Washington University School of Medicine, St. Louis, 6National Center for Adaptive Neurotechnologies, 7Department of Neurology, Loma Linda University, 8Department of Neurology, UC Davis, 9Center for Mind and Brain, UC Davis, 10Haas School of Business, UC Berkeley, 11Department of Psychology, UC Berkeley

Decision making requires approaching and avoiding stimuli representing rewarding and aversive outcomes. Approach-avoidance is dependent on the temporal dynamics of the decision. Both when an actor chooses to approach/avoid and what environmental changes occur are crucial to adaptive decisions. Modeling temporal dynamics of continuous predictors (e.g., increasing threat or diminishing reward) leading to a single choice (switching from approach to avoidance), is difficult. We apply models that jointly account for dependencies between survival outcomes (when) and longitudinal measurements (what), to an approach-avoidance conflict task (Pacman). Decisions to move along a corridor involved potential gains (dots, resulting in points) and losses (ghost attack, resulting in death). Our joint models build on a linear mixed effects model, capturing how a predictor (e.g., threat, reward) changes over time, combined with a time-to-event model, capturing event occurrence. These sub-models are linked via shared random effects structures using Bayesian model fitting. These models are effective at predicting trial-level subject behavior in an online sample (n=191), and in presurgical epilepsy patients (n=15). These models predict temporal avoidance decisions in held-out trials (average online AUC: 0.76 [95% CI:0.75-0.78]; average patient AUC: 0.80 [95% CI:0.76-0.85]). They outperform permuted models where predictors were shuffled (mean online AUC difference: 0.19 [95% CI:0.16-0.22]; mean patient AUC difference: 0.24 [95% CI: 0.15-0.32]). Additionally, we find preliminary evidence that these models can incorporate neural measures, such as hippocampal theta or prefrontal high-frequency activity, to predict avoidance decisions.

Topic Area: THINKING: Decision making


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April 13–16  |  2024