Education and Training
• Sage Junior Fellow, Sage Center for the Study of the Mind and Laboratory for Computational Cognitive Neuroscience, Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA
• Ph.D., Cognition and Perception, Department of Psychology, University of Iowa, Iowa City, IA
• M.S., Psychology, Department of Psychology, University of Chile, Santiago, Chile
Dr. Soto studies the neural and computational mechanisms of learning and generalization at work during object categorization and causal learning. His research focuses on how existing visual representations shape the mechanisms of learning and generalization that are deployed in a particular task, and how in turn learning modifies visual representations. His research involves developing computational models of these processes, and testing them using brain imaging (both fMRI and EEG) and behavioral research.
Soto, F. A., & Wasserman, E. A. (2010). Error-driven learning in visual categorization and object recognition: A common elements model. Psychological Review, 117(2), 349-381.
Soto, F. A., & Wasserman, E. A. (2010). Missing the forest for the trees: Object discrimination learning blocks categorization learning. Psychological Science, 21(10), 1510-1517.
Soto, F. A., & Wasserman, E. A. (2012). Visual object categorization in birds and primates: Integrating behavioral, neurobiological, and computational evidence within a “general process” framework. Cognitive, Affective, and Behavioral Neuroscience, 12(1), 220-240.
Soto, F. A., Waldschmidt, J. G., Helie, S., & Ashby, F. G. (2013). Brain activity across the development of automatic categorization: A comparison of categorization tasks using multi-voxel pattern analysis. Neuroimage, 71, 284-297.
Soto, F. A., Gershman, S. J., & Niv, Y. (2014). Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. Psychological Review, 121(3), 526-558.
Soto, F. A., Musgrave, R., Vucovich, L., & Ashby, F. G. (2015). General recognition theory with individual differences: A new method for examining perceptual and decisional interactions with an application to face perception. Psychonomic Bulletin & Review, 22(1), 88-111.
Soto, F. A., & Ashby, F. G. (2015). Categorization training increases the perceptual separability of novel dimensions. Cognition, 139, 105-129.