Matthew Botvinick is an expert in cognitive control and performance monitoring. His work includes modeling the role of prefrontal cortex in hierarchically structured behavior, working memory, and sequential action (Botvinick & Plaut, Psychological Review, 2004; Botvinick & Plaut, Psychological Review, 2006), as well as landmark work on the role of anterior cingulate cortex in conflict monitoring and representing the costs of performance (Botvinick et al., Nature, 1999; Botvinick et al., Psychological Review, 2001; Yeung et al., Psychological Review, 2004).

Yael Niv is an expert in reinforcement learning and its relationship to decision making and goal-directed behavior (Daw et al., Nature Neuroscience, 2005; Niv et al., Neural Information Processing Systems, 2005; Niv et al., Trends in Cognitive Sciences, 2006). Recently, with Botvinick, she has pioneered the application of newly developed hierarchical reinforcement learning methods to understanding the development and organization of goal representations in prefrontal cortex (Botvinick, Niv & Barto, Cognition, 2009).

Kenneth Norman is an expert in computational modeling of episodic memory. He has published extensively on neural network models of learning in cortex and hippocampus (Norman et al., Psychological Review, 2003; Polyn et al., Psychological Review, 2009), and is also a pioneer in the development of MVPA methods for decoding internal mental states and applying these to the study of learning and memory (Polyn et al., Science, 2005; Norman et al., Trends in Cognitive Sciences, 2006).

Jonathan Cohen, the project leader, has over two decades of experience integrating computational modeling, behavioral testing and neuroimaging to study the neural bases of cognitive control (Cohen et al., Psychological Review, 1990; Cohen et al., Nature, 1997; Miller & Cohen, Annual Review of Neuroscience, 2001; D’Ardenne et al., Proceedings of the National Academy of Sciences, in press). His collaboration with investigators in this project has produced some of the foundational findings in the field. For example, with Botvinick he established the role of anterior cingulate cortex (ACC) in conflict monitoring (Botvinick et al., Nature, 1999; Botvinick et al., Psychological Review, 2001), with Norman he generated the first application of multivariate pattern analysis (MVPA) of fMRI data to memory (Polyn et al., Science, 2005), and with Niv he pioneered the first uses of Bayesian modeling and reinforcement learning to characterize control representations in prefrontal cortex (Gershman et al., Proceedings of the Annual Meeting of the Cognitive Science Society, 2010).

Nicholas Turk-Browne is an expert in human perception and memory, in particular the learning mechanisms such as statistical learning that transform perceptual experience into memory (e.g., Schapiro et al., Current Biology, 2012; Zhao et al., Psychological Science, 2013; Kim et al., Proceedings of the National Academy of Sciences, 2014), as well as the attention mechanisms that help control this transformation and the subsequent expression of resulting knowledge in behavior (e.g., Chun et al., Annual Review of Psychology, 2011; Hutchinson & Turk-Browne, Trends in Cognitive Sciences, 2012; Al-Aidroos et al., Proceedings of the National Academy of Sciences, 2012). Recently, with Cohen, he has pioneered a full correlation matrix analysis (FCMA) method for unbiased multivariate decoding of the correlational structure of fMRI data (see Turk-Browne, Science, 2013), and with Norman and Cohen, he has pioneered methods for real-time preprocessing and analysis of fMRI data in the service of training cognitive control.