- Sc.B. Math / A.B. Philosophy, Brown University, 1993
- Ph.D. Computer Science & Engineering, University of Michigan, 2004
- Postdoctoral research in Psychology/Neuroscience, Princeton University, 2004-2011
The primary focus of my research in cognitive neuroscience is on the role of reward and cost monitoring in human and animal decision-making and interval timing. I attempt to model basic decision-making and timing circuits in the brain, using mathematical models that are as simple as possible, but that achieve enough functionality to account for critical features of both behavioral and physiological data. These models typically 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. The resulting models generate precise, quantitative hypotheses about choices, response times and brain activity that I test with experiments in human behavior, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI).
I also focus theoretically on the composition of these basic circuits into larger models capable of more complex behavior. Such complex behavior is typically modeled with rule-based systems in the artificial intelligence tradition of computer science. My mathematical and computational modeling work in this area is intended to provide a theoretical link between different levels of description in psychology and neuroscience. At the neural level of description, individual neurons and neural populations are the objects of study by physiological methods, and dynamical systems and stochastic processes are theoretical tools of choice. At the psychological level of description, entities such as percepts, goals and actions are the objects of behavioral investigation, and symbolic cognitive architectures known as "production systems" are leading theoretical constructs. In order to unify these different approaches, I seek to explain how the psychological description level emerges from the neural level by building neural networks that emulate production systems.
I live in Oberlin with my wife Maureen and my son Ben.
NSCI 201: The Brain: An Introduction to Neuroscience
NSCI 211: Neuroscience Laboratory
NSCI 360: Introduction to Cognitive Neuroscience
NSCI 361: Cognitive Neuroscience Research Methods
Simen, P. (2012). Evidence accumulator or decision threshold - which cortical mechanism are we observing? Frontiers in Cognitive Science, 3: 183. doi: 10.3389/fpsyg.2012.00183
Simen, P., Balci, F., deSouza, L., Cohen, J.D. and Holmes, P. (2011). A model of interval timing by neural integration. Journal of Neuroscience, 31, 9238-9253.
Simen, P., Contreras, D., Buck, C., Hu, P., Holmes, P. and Cohen, J.D. (2009). Reward-rate optimization in two-alternative decision making: Empirical tests of theoretical predictions. Journal of Experimental Psychology: Human Perception & Performance, 35, 1865-1897.
Simen, P. and Cohen, J.D. (2009). Explicit melioration by a neural diffusion model. Brain Research, 1299, 95-117.