- ScB, math and AB in philosophy, Brown University, 1993
- PhD, computer science and engineering, University of Michigan, 2004
- Postdoctoral research in psychology/neuroscience, Princeton University, 2004-2011
I study how rewards affect human and animal decision-making, and how we keep track of time. The data my students and I collect inform how my lab models decision-making and timing circuits in the brain. We use mathematical and computational models that are as simple as possible, while still achieving enough functionality to account for critical features of behavioral and physiological data.
Our models incorporate a layer of neural control mechanisms for optimizing the performance of the underlying decision-making circuits: that is, they help these circuits maximize rewards during simulated task performance. They generate precise, quantitative hypotheses about choices, response times and brain activity that we test with experiments in human behavior and electroencephalography (EEG).
We also focus theoretically on composing these basic circuits into larger models capable of more complex behavior. Our work in this area provides a possible theoretical link between different levels of description in psychology and neuroscience. At the neural level of description, neurons and neural populations are the objects of study; we use dynamical systems, stochastic processes and "deep learning" neural networks to model them. At the psychological level of description, entities such as percepts, goals and actions are the objects of behavioral investigation; researchers often use symbolic, computational cognitive architectures known as "production systems” to model these. But how do such radically different types of description relate to each other?
To try to answer that question, we model the emergence of the psychological description-level from the neural description-level, by building subsymbolic neural networks that emulate symbolic production systems.
NSCI 201: The Brain: An Introduction to Neuroscience
NSCI 211: Neuroscience Laboratory
NSCI 360: Introduction to Cognitive Neuroscience
NSCI 361: Cognitive Neuroscience Research Methods
FYSP 041: Emergence and the Unification of Knowledge
Krueger, P., van Vugt, M., Simen, P., Nystrom, L., Holmes, P. and Cohen, J. D. (2017). Evidence accumulation detected in BOLD signal using slow perceptual decision making. Journal of Neuroscience Methods, 281, 1-12.
Simen, P. and Matell, M. (2016). How does time fly when we're having fun? Science, 354, 1231-1232. Perspective article previewing Soares, S., Atallah, B. V. and Paton, J. J. (2016), Midbrain dopamine neurons control judgment of time (2016), Science, 354,1273-1277.
Srivastava, V., Holmes, P. and Simen, P. (2016). Explicit moments of decision times for single- and double-threshold drift-diffusion processes. Journal of Mathematical Psychology, 75, 96-109.
Balcı, F. and Simen, P. (2016). A decision model of timing. Current Opinion in Behavioral Sciences, 8, 94-101.
Simen, P., Vlasov, K.* and Papadakis, S.* (2016). Scale (in)variance in a unified diffusion model of decision making and timing. Psychological Review, 123:151-181.
Patrick Simen Gives Talk in Cognitive Forum SeriesOctober 16, 2018
Associate Professor of Neuroscience Patrick Simen gave a talk "Evidence for continuous, online adaptation of decision biases, geared toward reward maximization" for the Cognitive Forum series in Michigan State University's Psychology Department.
Patrick Simen Gives Talks in AustraliaMay 28, 2018
Assistant Professor of Neuroscience Patrick Simen spent three weeks in Australia supported by a Powers Travel Grant. There, he visited labs and gave talks at University of Tasmania in Hobart, University of Melbourne, University of New South Wales in Sydney, and University of Newcastle. Simen’s talks focused on the mathematical modeling of perceptual decision making by humans and understanding how the brain incorporates reward information and timing information into simple perceptual decisions.
Patrick Simen PublishesNovember 17, 2015
Assistant Professor of Neuroscience Patrick Simen’s article “Scale (In)Variance in a Unified Diffusion Model of Decision Making and Timing” was published in Psychological Review. The article was written with student coauthors Ksenia Vlasov '13 and Samantha Papadakis '15.
The article derives behavioral predictions from a simplified mathematical model of neural activity, and it describes the results of human behavioral experiments carried out by students at Oberlin to test these predictions. Most of the predictions held up, suggesting that timing and perceptual decision making depend on a common process in which neural activity builds up over time at a constant rate.
Patrick Simen Gives Talks in U.K. and TurkeyFebruary 11, 2015
Patrick Simen, assistant professor of neuroscience, recently gave a series of talks in the U.K. and Turkey regarding a mathematical model of neural processing in perceptual decision making and interval timing. In the U.K., Simen spoke at the University of Warwick, the University of Oxford, and the Gatsby Computational Neuroscience Unit of University College London. In Turkey, Simen spoke at Koç University in Istanbul.
Simen's travel was supported by a Powers Travel Grant, the funding from which also gave him an opportunity to spend time with and see the research facilities of his collaborators at the aforementioned institutions.