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Nicholas Van Horn

Associate Professor


  • Psychology

Contact Information


Dr. Van Horn is a cognitive psychologist with a specific focus in the interdisciplinary field of computational cognitive neuroscience. Work in this area represents the convergence of a number of related fields, including psychology, neuroscience, computer vision, artificial intelligence, mathematical modeling, as well as computer science. His research and teaching span topics from decision making, memory and learning, the perception of time, and vision to cyberpsychology—the study of the psychological antecedents and consequences of networked and online social behaviors and relationships. In parallel with his teaching and research, Van Horn actively writes and maintains open source software related to social media data analysis, writing and productivity, statistics, text processing, and other data science solutions.
Degrees Earned
Ph.D. in Cognitive Psychology, The Ohio State University
M.A. in Cognitive Psychology, The Ohio State University
B.A. in Japanese Language, The Ohio State University
Biological Psychology
Cognitive Psychology
Computational Neuroscience and Psychology
Experimental Psychology
Introduction to Psychology
Social Science Research Methods
Social Sciences Statistics
Beveridge, A. Van Horn, N. M., (in press). Mining Obama Hope: Tracking Changes in the Hope Trend. Doing Digital Visual Studies. Computers and Composition Digital Press, an imprint of Utah State University Press.

Beveridge, A. Van Horn, N. M., (in press). Big data, tiny computers: Making data-driven methods accessible with a raspberry pi. In S. Homles & A. Stagliano (Eds.), Re-programmable rhetoric. Ann Arbor, MI: University of Michigan Press.

Van Horn, N. M., & Beveridge, A. (2016). MassMine: Your Access to Data. The Journal of Open Source Software, 1(8).

Van Horn, N. M., Beveridge, A., & Morey, S. (2016). Attention Ecology: Trend Circulation and the Virality Threshold. Digital Humanities Quarterly, 10(4).

Petrov, A., & Van Horn, N. M. (2012). Motion aftereffect duration is not changed by perceptual learning: Evidence against the representation modification hypothesis. Vision Research, 61, 4-14.

Petrov, A. A., Van Horn, N. M., & Ratcliff, R. (2011). Dissociable perceptual learning mechanisms revealed by diffusion-model analysis of the patterns of specificity. Psychonomic Bulletin & Review, 18(3), 490–497.

Petrov, A. A., Van Horn, N. M., & Todd, J. (2011). The visual identification of relational categories. Journal of Vision, 11(12), 1–11.