معرفی
Patrick Simen is an Associate Professor in the Neuroscience Department at Oberlin College's College of Arts and Sciences, where he conducts research on decision-making, timing mechanisms, and computational modeling of neural circuits. His work bridges psychological and neural levels of description through mathematical frameworks that optimize reward-based behavior.
His educational background includes:
- ScB in Mathematics and AB in Philosophy from Brown University (1993)
- PhD in Computer Science and Engineering from the University of Michigan (2004)
- Postdoctoral research in Psychology/Neuroscience at Princeton University (2004-2011)
Dr. Simen's research focuses on reward-driven decision circuits and temporal processing using minimalist computational models. His lab develops:
- Drift-diffusion frameworks for decision-timing unification
- Neural control mechanisms for reward optimization
- Subsymbolic models linking neural activity to symbolic cognition
Analysis of his 2016-2017 publications reveals a cohesive research program examining scale invariance in decision-timing processes. Key themes include BOLD signal correlates of evidence accumulation, reward-time interactions in perceptual decisions, and mathematical formalization of double-threshold decision dynamics across neural and behavioral levels.
Dr. Simen actively mentors undergraduate researchers, including senior thesis students like Zoe Swann (Class of 2019) who pursued neuroscience PhD studies at Arizona State University. His lab receives institutional support for EEG/fMRI research infrastructure and computational resources, enabling empirical validation of theoretical models through human behavioral experiments.
His research group operates from Science Center A244, integrating computational modeling with experimental neuroscience. The lab employs undergraduate researchers in all phases of work—from developing "deep learning" neural networks that emulate production systems to conducting EEG studies on timing behavior—fostering a collaborative environment where theoretical and empirical approaches converge.
