Curriculum Vitae

Research Interests

  • Computational Cognitive Neuroscience. Biologically plausible neural network models of cognitive systems
  • Visual Working Memory. Visual short-term memory, and its interaction with other visual systems
  • Perceptual Learning. Neurophysiological mechanisms of visual perceptual learning
  • Object Recognition. Visual object recognition in human observers and computer models
  • Data Science & Predictive Analytics. Statistical modeling approaches to large and complex sources of data

Education

  • Ph.D., Cognitive Psychology, The Ohio State University, 2014
  • M.A., Cognitive Psychology, The Ohio State University, 2011
  • B.A., Japanese Language, The Ohio State University, 2007

Grant Support

Grants Awarded

  • MassMine Advancement Grant for Sustainable Humanities Data-Driven Research. Role: PI. National Endowment for the Humanities (NEH). September 2019. $324,865 direct costs.

  • MassMine: Collecting and Archiving Big Data for Social Media Humanities Researchers. Role: Co-Investigator. National Endowment for the Humanities (NEH). May 2015. $60,000 direct costs.

  • MassMine Development and Training Project. Role: Lead software developer. University of Florida Informatics Institute. May 2015. $49,837 direct costs.

Grants Submitted

  • Epidemiological investigation of underage drinking on social media, Co-investigator on
    proposed National Institutes of Health (NIH) grant, $405,950 requested.

Scholarly Work

Peer-Reviewed Publications

  • Beveridge, A. Van Horn, N. M., (2022). Mining Hope: Preserving and Exploring Twitter Data for Digital Visual Studies. In L. E. Gries & B. Hallinan (Eds.), Doing Digital Visual Studies: One Image, Multiple Methodologies. Computers and Composition Digital Press/Utah State University Press.

  • Beveridge, A. Van Horn, N. M., (2022). Big data, tiny computers: Making data-driven methods accessible with a raspberry pi. In M. Faris & S. Holmes (Eds.), Reprogrammable Rhetoric: Critical Making Theories and Methods in Rhetoric and Composition. Logan, UT: Utah State University 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.

Abstracts, Talks, and Posters

  • Coleman, Nicole and Van Horn, N. M. (2021). Musical Effects on Individual Time Perception. In Proceedings from the national conference on undergraduate scholarship.

  • Coleman, Nicole and Van Horn, N. M. (2021). Effects of Presentation Styles and Personality Traits on Cyberbullying. In Proceedings from the national conference on undergraduate scholarship.

  • Szabo, Valerie and Van Horn, N. M. (2018). Fake news: Identifying misinformation in widespread media. In Proceedings from the national conference on undergraduate scholarship.

  • Van Horn, N. M. & Petrov, A. A. (2015). An equivalent noise method for measuring delay-induced degradation in VSTM. Journal of Vision, 15(12), 660.

  • Van Horn, N. M. & Beveridge, A. (2015). Writing eScience: Using Data Science Tools to Study Networked Writing Ecologies. College, Conference on Composition and Communication.

  • Van Horn, N. M., & Petrov, A. A. (2014). Practice abolishes similarity’s influence on VSTM-induced interference on perception. In Proceedings of the 2014 annual meeting of the vision sciences society.

  • Petrov, A. A., & Van Horn, N. M. (2014). Training on orientation recall improves the precision of visual short-term memory under high and low levels of memory masking. In Proceedings of the 2014 annual meeting of the vision sciences society.

  • Beveridge, A., & Van Horn, N. M. (2014). Humanities Software Development: Data Mining and Writing Studies. The humanities and technology camp (THATCamp) conference.

  • Van Horn, N. M., & Petrov, A. A. (2013). Cross-talk between visual short-term memory and low-level vision: Evidence for interactions across shared neural resources. In Proceedings of the 2013 annual meeting of the vision sciences society.

  • Van Horn, N. M., Petrov, A. A., & Todd, J. T. (2011). Can configural relations be encoded by image histograms of higher-order filters? [abstract and a talk at the session on models of perceptual learning]. In Proceedings of the 2011 annual meeting of the vision sciences society.

  • Petrov, A. A., Van Horn, N. M., & Ratcliff, R. (2011). Dissociable perceptual learning mechanisms revealed by diffusion-model analysis. In Proceedings of the 2011 annual meeting of the vision sciences society.

  • Petrov, A. A., Van Horn, N. M., & Ratcliff, R. (2010). Dissociable perceptual learning mechanisms revealed by diffusion-model analysis. In Abstracts of the psychonomic society (Vol. 15, p. 218).

  • Petrov, A. A., & Van Horn, N. M. (2009a). Motion aftereffect duration is not changed by perceptual learning: Evidence against the representation-modification hypothesis. In Abstracts of the psychonomic society (Vol. 14, p. 5094).

  • Petrov, A. A., & Van Horn, N. M. (2009b). Perceptual learning of visual motion: The role of the spatial frequency of the carrier. Journal of Vision, 9(8), 886a.

Awards

Research

  • Summer Research Excellence Fellowship (2014), The Ohio State University. Research fellowship awarded to top 5 graduate students in Psychology.

  • Presidential Fellowship (2013), The Ohio State University. Merit-based scholarship rewarding outstanding scholarly accomplishments (~17 recipients across all departments at OSU).

  • Graduate Student Research Excellence Award (2012), The Ohio State University. Summer research fellowship awarded to top 15 graduate students for outstanding research ability

  • OSU Graduate Student Conference Presentation Award (2011). Travel award to present research at the 11th annual meeting of the Vision Sciences Society

  • Graduate Student Research Excellence Award (2011), The Ohio State University. Summer research fellowship awarded to top 15 graduate students for outstanding research ability

  • OSU Graduate Student Conference Presentation Award (2010). Travel award to present research at the 50th annual meeting of the Psychonomic Society

  • OSU Graduate Student Conference Presentation Award (2009). Travel award to present research at the 9th annual meeting of the Vision Sciences Society

  • University Fellowship (2008-2009), The Ohio State University, Department of Psychology. Merit-based departmental scholarship

Teaching

  • Meritorious Teaching Award, The Ohio State University, Department of Psychology. Award winner for the 2011-2012 academic year.

  • Nominated for the College of Arts and Sciences Outstanding Graduate Teaching Associate Award, The Ohio State University. Nominated for the 2010-2011 academic year.

Teaching Experience

  • Computational Psychology and Neuroscience (2018-present), Capital University
  • Experimental Psychology (2017-present), Capital University
  • Cognitive Psychology (2016-present), Capital University
  • Advanced Social Media and Big Data Research (2016), Capital University
  • Social Media and Self-Surveillance (2016), Capital University
  • Social Sciences Statistics (2015), Capital University
  • Biological Psychology (2015-present), Capital University
  • Research Methods (2014-present), Capital University
  • Introduction to Psychology (2016-present), Capital University
  • Principles of Psychology (2014), Capital University
  • Introduction to Psychology (2011-2014), The Ohio State University. (3 sections total)
  • Memory & Cognition (2012), The Ohio State University. (guest lecturer for 3 weeks)
  • Introduction to Psychology (2009-2011), The Ohio State University. (9 sections total under quarter system)

Computer and Technical Experience

Languages

  • R: Extensive experience (15+ years) in statistical analysis, mathematical and computational modeling, social media analysis, and neural networks

  • Matlab/Octave: Many carefully controlled experimental protocols (using the Psychophysics Toolbox) including a strictly controlled fMRI experiment, as well as mathematical modeling and machine learning algorithms on linux clusters (at the Ohio Supercomputer Center)

  • Python: Work on computational models of memory and stochastic Bayesian diffusion modeling of human reaction time data

  • Lisp: Many open source contributions in various lisp dialects, including primarily Racket, Emacs lisp, Scheme, and Clojure

  • Various: Advanced familiarity with many tools commonly used in managing Linux systems and web applications

Research Technology

  • EyeLink 1000 eye tracking

Operating Systems and Administrative Experience

  • 18+ years of active maintenance of Linux, MacOS, and Windows environments.

Publishing and Productivity

  • Advanced knowledge of Emacs and Emacs Lisp, including reproducible research techniques via org-babel, LaTeX, and markdown (including CommonMark and RMarkdown). This site is generated from markup using the Racket programming language.

Professional Activities

Scientific Memberships

  • Vision Sciences Society
  • Psychonomic Society

Conferences and Workshops

  • Vision Sciences Society Proceedings
  • Mathematical Biosciences Institute, Cognitive Neuroscience Workshop: 2012
  • Psychonomic Society Proceedings