Richard A. Hullinger
Lecturer and Director of Pedagogy
Department of Psychological and Brain Sciences
Indiana University, 1101 E. 10th St., Bloomington, IN 47405
Office: (812) 856-6854
Email: rahullin{AT}indiana{DOT}edu
Updated July, 2015
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Ph.D. in Psychological and Brain Sciences and Cognitive Science, 2011
Indiana University, Bloomington, Indiana
Advisor: John K. Kruschke
Thesis Title: An Evolutionary Analysis of Selective Attention

B.S. in Physics, 1996
B.S. in Computer Science, 1996
Magna Cum Laude
Rensselaer Polytechnic Institute, Troy, NY

Honors and Fellowships

Teaching Experience

Director of Pedagogy, Indiana University:
P211 Methods of Experimental Psychology, Spring 2015, Summer 2015
P335 Cognitive Psychology, Spring 2015
P660 The Teaching of Psychology, Spring 2015

Lecturer, Indiana University:
P335 Cognitive Psychology, Fall 2013 — Spring 2014
K300 Statistical Techniques, Fall 2013 — Fall 2014
C105 Brains & Minds, Robots & Computers, Fall 2013 & 2014
P101 Introductory Psychology, Fall 2014

Visiting Assistant Professor, Indiana University
K300 Statistical Techniques, Spring 2012 — Spring 2013
P335 Cognitive Psychology, Fall 2011 & 2012
Q301 Brain and Cognition, Spring 2013
C105 Brains & Minds, Robots & Computers, Fall 2011 & 2012
P199 Carrers in Psychology, Spring 2012 & 2013
P102 Introductory Psychology, Fall 2014

Research Interests

       As an instructor, I am deeply interested in studying pedagogical issues in post-secondary education. Specifically, I am drawn towards the interface between technology and the classroom – electronic textbooks, student response systems, internet access during class time, interactive applets, etc. – and the effects that technology can have on learning.
       I am also interested in a range of cognitive science topics with a primary focus on evolutionary simulations of attention and learning. I use simulated evolution as a means to investigate the types of environmental information structures that lead to the emergence of attention as an adaptive mechanism. I employ genetic algorithms to evolve simple connectionist networks in a range of environments. These environments may vary in terms of the underlying structure of the cues and responses, the temporal structure, or the amount of noise that is present in the environment. I then analyze the evolved agents to determine if they show signs of attentional behavior. The primary goal of this work is to explain attentional behaviors as adaptive evolved responses that can only be fully understood in the context of the environments that gave rise to them..


Hullinger, R. A., Kruschke, J. K., & Todd, P. M. (2015). An evolutionary analysis of learned attention. Cognitive Science, 39: 1172–1215. (doi:10.1111/cogs.12196)

Kruschke, J.K., & Hullinger, R.H. (2010). Evolution of attention in learning. In: N. Schmajuk (Ed.), Computational Models of Classical Conditioning, pp. 10 - 52. Cambridge University Press.


Professional Experience


 “System for Analyzing Television Programs,” U.S. Patent Number 6,295,092, issued Sept. 25, 2001. Inventors: R. Hullinger et al