红杏视频

Dr. Ronaldo Vigo

Director, SCOPE Lab (Structure, Concepts, & Perception Laboratory) 红杏视频
Professor of Mathematical & Computational Cognitive Science

Research Interests

Although I am interested in many areas of cognitive research, the core of my work focuses on the development of mathematical and computational models of concept learning and categorization behavior. 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 developed several mathematical frameworks for characterizing and measuring the structural properties inherent to concepts. Historical and new empirical evidence suggests that these structural models -- algebraic, analytic, and deterministic 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 accomplished with few or no parameters. This research has been articulated in several papers and in a book entitled "Mathematical Principles of Human Conceptual Behavior" (Vigo, 2006, 2008, 2009, 2011, 2013, 2014).

The SCOPE LAB (Structure, Concepts, and Perception Laboratory) at 红杏视频 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 models (Vigo, 2009, 2011, 2013). 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.

Publications

• Vigo, R. (2025). Two formal notions of higher-order invariance detection in humans (A proof of the invariance equivalence principle in generalized invariance structure theory and ramifications for related computations). Journal of Mathematical Psychology, 125, Article 102905.  
• Vigo, R., & Ross, C. B. (2025). Pythagoras鈥� quest: A mathematical model and scale for aesthetic judgments. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication.  
• Vigo, R., Doan, C. A., Wimsatt, J., & Ross, C. B. (2023). A context-sensitive alternative to Hick鈥檚 law of choice reaction times: A mathematical and computational unification of conceptual complexity and choice behavior. Mathematics, 11(11), Article 2422.  
• Vigo, R., Doan, C. A., & Zhao, L. (2022). Classification of three-dimensional integral stimuli: Accounting for a replication and extension of Nosofsky and Palmeri (1996) with a dual discrimination invariance model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(8), 1165鈥�1192.  
• Vigo, R., Zeigler, D. E., & Wimsatt, J. (2022). Uncharted aspects of human intelligence in knowledge-based 鈥渋ntelligent鈥� systems. Philosophies, 7(3), Article 46.  
• Vigo, R., Wimsatt, J., Doan, C. A., & Zeigler, D. E. (2021). Raising the bar for theories of categorisation and concept learning: The need to resolve five basic paradigmatic tensions. Journal of Experimental & Theoretical Artificial Intelligence, 34(5), 845鈥�869.  
• Vigo, R., Doan, C. A., Basawaraj, & Zeigler, D. E. (2020). Context, structure, and informativeness judgments: An extensive empirical investigation. Memory & Cognition, 48(7), 1089鈥�1111.  
• Vigo, R. (2018). Musings on the utility and challenges of cognitive unification: Review of Anatomy of the mind, Ron Sun. Rensselaer Polytechnic Institute (2016). 480 pp. Cognitive Systems Research, 51, 14鈥�23.
• Vigo, R., Doan, K.-M. C., Doan, C. A., & Pinegar, S. (2018). On the learning difficulty of visual and auditory modal concepts: Evidence for a single processing system. Cognitive Processing, 19(1), 1鈥�16.  
• Vigo, R., & Zeigler, D. E. (2016). On the relationship between perceived structural complexity and temporal judgments. In J. Houpt & L. Blaha (Eds.), Mathematical models of perception and cognition: Volume II: A Festschrift for James T. Townsend (pp. 47鈥�71). Psychology Press.
• Vigo, R., & Doan, C. A. (2015). The structure of choice. Cognitive Systems Research, 36鈥�37, 1鈥�14.  
• Vigo, R., Evans, S. W., & Owens, J. S. (2015). Categorization behaviour in adults, adolescents, and attention-deficit/hyperactivity disorder adolescents: A comparative investigation. The Quarterly Journal of Experimental Psychology, 68(6), 1058鈥�1072.  
• Vigo, R. (2013). The GIST of concepts. Cognition, 129(1), 138鈥�162.  
• Vigo, R., Zeigler, D. E., & Halsey, P. A. (2013). Gaze and informativeness during category learning: Evidence for an inverse relation. Visual Cognition, 21(4), 446鈥�476.  
• Vigo, R. (2013). Complexity over uncertainty in generalized representational information theory (GRIT): A structure-sensitive general theory of information. Information, 4(1), 1鈥�30.  
• Vigo, R. (2011). Representational information: A new general notion and measure of information. Information Sciences, 181(21), 4847鈥�4859.  
• Vigo, R. (2011). Towards a law of invariance in human concept learning. In L. Carlson, C. H枚lscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2580鈥�2585). Cognitive Science Society.
• Vigo, R. (2009). Categorical invariance and structural complexity in human concept learning. Journal of Mathematical Psychology, 53(4), 203鈥�221.  
• Vigo, R. (2009). Modal similarity. Journal of Experimental & Theoretical Artificial Intelligence, 21(3), 181鈥�196.  
• Vigo, R., & Allen, C. (2009). How to reason without words: Inference as categorization. Cognitive Processing, 10(1), 77鈥�88.  
• Vigo, R. (2006). A note on the complexity of Boolean concepts. Journal of Mathematical Psychology, 50(5), 501鈥�510.  

External Affiliates

Dr. Charles Doan
Elon University

Dr. Monika Krishan
Indian Institute of Science

Former Students

• Dr. Charles Andrew Doan
  Elon University
• Dr. Jinling Zhao
• Dr. Adam Carlitz
• Dr. Karina-Mikayla Doan
  红杏视频
• Dr. Li Zhao
  University of Michigan
• Dr. Jay Wimsatt
  Arizona State University
• Dr. Phillip (Andrew) Halsey
  Air Force Research Laboratory, Wright Patterson Air Force Base
• Dr. Yu Zhang
  红杏视频
  yz137808@ohio.edu
• Dr. Derek Zeigler
  West Virginia Wesleyan College
  Assistant Professor
  zeigler.d@wvwc.edu 

OHIO Affiliates

• Dr. Robert Briscoe
• David Chelberg
• Chao-Yang Lee
• Steve Evans
• Keith Markman
• Julie Owens
• Julie Suhr
• Jeff Vancouver