SESSION 13: TECHNIQUES FROM MACHINE LEARNING/AI
COMPUTATIONAL MODELS AND COGNITIVE NEUROIMAGING DATA
Slides from class: Machine Learning/Neuroimaging [PDF]
Readings:
Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10, 59-63.
Henson, R. (2006). Forward inference using functional neuroimaging: Dissociations versus associations. Trends in Cognitive Sciences, 10, 64-68.
Brown, J. B., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulated cortex. Science, 307, 1118-1121.
References:
Kruschke, J. K. (2006). Learned attention. International
Conference on Learning and Development
Westermann, G., Sirois, S., Shultz, T. R., & Mareschal, D. (2006). Modeling developmental cognitive neuroscience. Trends in Cognitive Sciences, 10, 227-231.
Frank, M. J., Seeberger, L. C., & OÕReilly, R. C. (2004). By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science, 306, 1940-1943.
Atallah, H. E., Frank, M. J., & OÕReilly, R. C. (2004). Hippocampus, cortex, and basil ganglia: Insights from computational models of complementary learning systems. Neurobiology of Learning and Memory, 82, 253-267
Anderson, J. R. (in press). Using brain imgaing to guide the development of a cognitive architecture. In W. D. Gray (Ed.), Integrated Models of Cognitive Systems.New York, NY: Oxford University Press.
Just, M. A., & Varma, S. (2006). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Manuscript submitted for publication.
Resources: