Lunch is available beginning at 12 PM

Speaker to begin promptly at 12:30 PM

This talk will feature discussions from Jonathan Pillow and Helena Liu.

The process of learning new behaviors is of great interest in neuroscience and artificial intelligence. However, most standard analyses of training data either treat behavior as fixed, or track only coarse performance statistics (e.g., accuracy and bias), providing limited insight into behavioral strategies that evolve over the course of learning. To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training. I will describe new work based on a dynamic logistic regression model that captures the dynamic dependencies of behavior on stimuli and common task-irrelevant variables including choice history, sensory history, reward history, and choice bias.  We apply our method to psychophysical data from both human subjects and rats learning a delayed sensory discrimination task. We successfully track the dynamics of psychophysical weights during training, capturing day-to-day and trial-to-trial fluctuations that underlie changes in performance, choice bias, and dependencies on task history. We leverage the model's flexibility model to investigate why rats frequently make mistakes on easy trials, demonstrating that so-called "lapses" often arise from sub-optimal weighting of task covariates.  Finally, I will describe recent work on adaptive optimal training, which combines ideas from reinforcement learning and adaptive experimental design to formulate methods for inferring animal learning rules from behavior, and using these rules to speed up animal training.

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