Associate Professor, Psychology
200B Lazenby Hall
1872 Neil Avenue
Columbus, OH 43210
Welcome and thanks for stopping by!
I am a cognitive scientist. Some prominent foci of my research are:
Relational reasoning, analogy-making, and high-level cognition. I am particularly interested how such high-level tasks, which require role-filler binding, can be implemented in biologically realistic neural networks. In the words of my former advisor John Anderson (and Allen Newell before him),
How can the human mind occur in the physical universe?
My specific interest is in human -- that is, relational -- cognition.
Visual cognition, particularly the extraction and encoding of relations among two or more objects in a visual scene. Again, I am particularly interested in biologically plausible models of the underlying neural mechanisms.
Visual perceptual learning, spatial vision, and neural-network models thereof.
Cognitive architectures, specifically ACT-R, Leabra, and Nengo.
Methodologically, research in the CogMod/CCN Lab involves a combination of behavioral and psychophysical experimentation, mathematical and computational modeling, eyetracking and pupillometry. Typically, the papers that we publish involve both empirical data and computational models thereof. We also use and develop sophisticated models to analyze the behavioral data collected in the lab or in collaboration with other labs.
To prospective students: Our lab is looking to accept up to 2 graduate students for the incoming class of 2017.The Prospective Students page on the lab website can help you determine whether our lab is a place for you and, if so, provides tips on how to apply. Students with background in mathematics, computer science, engineering, and/or philosophy are especially encouraged to apply. The deadline is Nov 30. The Research page on the lab website contains more detailed and up-to-date information on our current projects. Check it out!
* Petrov, A. (2013). Associative Memory-Based Reasoning: A Computational Model of Analogy-Making in a Decentralized Multi-Agent Cognitive Architecture. Saarbrücken, Germany: Lambert Academic Publishing. ISBN 978-3-659-26248-7.
Selected Journal Articles
* Hayes, T. & Petrov, A. (2016). Pupil diameter tracks the exploration-exploitation tradeoff during analogical reasoning and explains individual differences in fluid intelligence, Journal of Cognitive Neuroscience, 28 (2), 308-318.
* Hayes, T., Petrov, A. & Sederberg, P. (2015). Do We Really Become Smarter When Our Fluid-Intelligence Test Scores Improve?, Intelligence, 48 (1), 1-14.
* Hayes, T., Petrov, A. & Sederberg, P. (2011). A Novel Method for Analyzing Sequential Eye Movements Reveals Strategic Influence on Raven's Advanced Progressive Matrices. Journal of Vision, 11, 1-9.
* Petrov, A., Van Horn, N., & Todd, J. (2011). The Visual Identification of Relational Categories. Journal of Vision, 11(12:11), 1-11.
* Petrov, A. & Hayes, T. R. (2010). Asymmetric transfer of perceptual learning of luminance- and contrast-modulated motion. Journal of Vision, 10(4:11), 1-22.
* Petrov, A. (2009). Symmetry-based methodology for decision-rule identification in same-different experiment.. Psychonomic Bulletin & Review, 16(6), 1011-1025.
* Petrov, A., Dosher, B., & Lu, Z.-L. (2005). The Dynamics of Perceptual Learning: An Incremental Reweighting Model. Psychological Review, 112(4), 715-743.
* Petrov, A. & Anderson, J. R. (2005). The Dynamics of Scaling: A Memory-Based Anchor Model of Category Rating and Absolute Identification. Psychological Review, 112(2), 383-416.
* Kokinov, B. & Petrov, A. (2001). Integrating memory and reasoning in analogy-making: The AMBR model. In D. Gentner, K. Holyoak, & B. Kokinov (Eds.), The analogical mind: Perspectives from cognitive science (pp. 59-124). Cambridge, MA: MIT Press.