211 Porter Hall
Athens, OH 45701
- Mathematical Modeling
- Computational Modeling
- Cognitive Processes
Although I am interested in many areas of cognitive research, my core work focuses on the formulation and development of mathematical and computational models of concept learning, categorization, and perception. For example, I have investigated the degree of difficulty that humans experience when learning different types of concepts. Two key questions drive this research. First, why are some types of concepts more difficult to learn than others? Secondly, can the subjective degree of learning difficulty of these concepts be reliably predicted? In my work, I argue that the key to answering these questions lies on the structural properties of the categorical stimulus from which a concept is learned and on specific mental operations that facilitate their detection.
Toward this end, I have created several mathematical frameworks and formal models for characterizing the way that observers learn concepts from categorical stimuli. I have been able to show that these structural models, algebraic, analytic, and non-probabilistic in nature (and hence, much like the models encountered in classical physics), are more robust and cognitively plausible predictors of the degree of concept learning difficulty experienced by humans than the well-known alternatives. Notably, all of this is often accomplished without the need for free parameters. This research has been articulated in several papers (Vigo, 2006, 2008, 2009, 2011, 2012, 2013) and in a book titled "Mathematical Principles of Human Conceptual Behavior: The Structural Nature of Conceptual Representation and Processing" (Vigo, 2014).
The SCOPE LAB (Structure, Concepts, and Perception Laboratory) at Ohio University seeks to extend the above research empirically and theoretically. For example, in the SCOPE Lab we conduct empirical and theoretical research on human concept learning and categorization behavior using eye tracking technology. More specifically, we use eye tracking techniques to explore correlations between saccades and the concept learning behavior predicted by a variety of models, including my concept invariance model (Vigo, 2008, 2009).
Other research activities in the SCOPE Lab include empirical and theoretical research on decision making behavior as a function of similarity assessment, dissimilarity assessment, and categorization. Also, we are interested in researching how humans judge similarity and dissimilarity between structural or configural stimuli such as human faces. In related work, I introduced a mathematical model of similarity that predicts the empirical similarity ordering of a key class of configural stimuli associated with deductive inference (Vigo, 2009a, 2009b). Last, but not least, the SCOPE Lab conducts empirical and theoretical research on problem solving behavior in mathematical domains such as geometry, algebra, and physics, and on the nature of aesthetic judgments. For more information on our research, visit the SCOPE Lab website here.
Ph.D., Indiana University at Bloomington
M.A., University of California at Irvine
M.A., University of Southern California/UCLA
B.A., University of California at Los Angeles
Vigo, R., Evans, S., Owens, J. (2014). Categorization Behavior in Adults, Adolescents, and ADHD-Adolescents: A Comparative Investigation. Quarterly Journal of Experimental Psychology (In Press).
Vigo, R. (2014). Mathematical Principles of Human Conceptual Behavior: The Structural Nature of Conceptual Representation and Processing (Oct, 2014), Scientific Psychology Series, Routledge, Taylor and Francis.
Vigo, R. (2013). The GIST (Generalized Invariance Structure Theory) of Concepts, Cognition, 129(1), 138-162.
Vigo, R., Zeigler, D., Halsey, P. (2013). Gaze and Informativeness During Category Learning: Evidence for an Inverse Relation. Visual Cognition, 1-31.
Vigo, R., Basawaraj (2013). Will the most informative object stand? Determining the impact of structural context on informativeness judgments. Journal of Cognitive Psychology, 1-19.
Vigo, R. (2012). Complexity over Uncertainty in Generalized Representational Information Theory (GRIT): A Structure-Sensitive General Theory of Information. Information, 4, 1-30.
Vigo, R. (2011). Representational information: a new general notion and measure of information. Information Sciences, 181, 4847-4859.
Vigo, R. (2011). Towards a Law of Invariance in Human Concept Learning, L. Carlson, C. Hölscher, T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Austin, TX: Cognitive Science Society, 2580-2585.
Vigo, R., (2010). A Dialogue on Concepts. Think, Volume 9, Issue 24, March 2010, pp 109-120.
Vigo, R. (2009). Categorical Invariance and Structural Complexity in Human Concept Learning. Journal of Mathematical Psychology, 53, 203-221.
Vigo, R. (2009). Modal Similarity. Journal of Experimental and Theoretical Artificial Intelligence, 21(3), 181-196.
Vigo, R., Allen, C., (2009). How to reason without words: inference as categorization. Cognitive Processing, 10(1), 51-88.
Vigo, R. (2006). A Note on the Complexity of Boolean Concepts. Journal of Mathematical Psychology, 50, 501-510.
- Mikayla Barcus
- Charles Doan
- Phillip Andrew Halsey
- Li Zhao
- Derek Ziegler
- Jinling Zhao
- Sunil Carspecken