Graduate Research Projects
Below are descriptions of the projects I'm actively working on at CU and UCSD (as of May 2010).
Optimization of Learning via Cognitive Modeling
This project involves using computer models of human memory to schedule study. Just as physical processes like the weather can be successfully modeled, so too can cognitive processes be modeled. Similarly, just as the weather can be forecasted from computer models, “memory” can be “forecasted” from a computer model of human memory. For example, it is possible to predict with reasonable accuracy what percentage of facts a student will recall at a future date based on when and how long they previously studied.
Because we can predict a student’s performance at a test as a function of their study schedule (i.e., when he or she studied), we can also predict what study schedule will maximize the student’s performance at test. We are working to incorporate methods of predicting optimal study schedules into tutoring software, thereby improving students' acquisition and retention study material (above and beyond the improvement existing tutoring software provides).
Selected Papers:
Lindsey, R., Lewis, O., Pashler, H., & Mozer, M. C. (2010). Predicting students' retention of facts from feedback during training. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. xxx-xxx). Austin, TX: Cognitive Science Society.
Lindsey, R., Mozer, M., Cepeda, N. J., & Pashler, H. (2009). Optimizing Memory Retention with Cognitive Models. In A. Howes, D. Peebles, R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK.
Multiscale Context Model of Memory
Cognitive modeling is an approach to artificial intelligence motivatived by cognitive psychology. It typically involves the construction of computational “explanations” of particular aspects of human cognition. As the only existing example of genuine intelligence, human cognition is viewed as something that an artificially intelligent system should emulate. These “explanations,” which are called cognitive models, can be used in a predictive manner or to further the understanding of cognition.
The aspect of cognition this project research deals with is memory. Whenever a person learns facts, those facts are said to be “encoded” into memory for later retrieval. There are a variety of cognitive models (i.e., computational explanations) of this encoding and retrieval process. Because of our Optimization of Learning project, we're particularly interested in pinpointing the optimal spacing of study. This need motivated the development of what we call the Multiscale Context Model --- a memory model able to predict various aspects of human memory and, most importantly, how to optimally space study for students.
Selected Papers:
Mozer, M. C., Pashler, H., Cepeda, N., Lindsey, R., & Vul, E. (2009). Predicting the optimal spacing of study: A multiscale context model of memory. In Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 1321–1329). La Jolla, CA: NIPS Foundation.
Perceptual Learning via Attentional Saliency
Many cognitive tasks involve interacting with a complex visual environment, e.g., driving a car, piloting a plane, controlling air traffic, screening baggage, and even walking down a crowded street. Expertise in such environments comes from experience over a time period of years (Rosenbloom & Newell, 1987). Becoming an expert involves at least two distinct abilities: identifying features of the environment that are task relevant in a given context, and determining the appropriate response to these features. These two abilities pose a chicken-and-egg problem. The appropriate response cannot be determined until one knows which features are relevant, but the relevance of a feature depends on its being a reliable determinant of the task-appropriate response.
A long-term goal of this project is to understand the temporal dynamics of learning to attend to relevant features in complex visual environments, and to design tutoring systems that leverage expert knowledge to train novices more efficiently. One could ask an expert to stand over the shoulder of a novice as they, say, tried to control a flight simulator, and the expert could provide guidance such as, “Check your altimeter now.” However, with rapid fire perceptuomotor decision making, such interruptions are unlikely to be helpful. Furthermore, this type of guidance requires the constant presence of a vigilant expert, and assumes experts can verbalize their attentional strategies. Alternatively, we propose a novel perceptual learning paradigm:
1. We will record the eye movement behavior of an expert, and train a machine learning model to predict the expert’s eye movements given the current visual context.
2. We will then place the novice in an environment, and in parallel with the novice performing the task, use the expert eye-movement model to predict where the expert will fixate at each instant.
3. We will cue the novice to the location of the expert’s focus of attention using some type of salient but subtle visual cue (e.g., onset lag, motion, or brightness modulation).
From experimental work and models of visual saliency (Zhang et al., 2008), we have a basic idea what kinds of visual cues will attract attention. What we don't know is whether guiding a novice's attention to the appropriate location will facilitate learning. However, we hypothesize an affirmative answer to this question under the Guthrian view that associations are strengthened by performing them (Guthrie, 1959).
Low-Cost Brain Computer Interfaces
Description coming someday.
Online Sentiment Analysis
I currently work part-time for a division of J.D. Power and Associates. This project involves using machine learning techniques (e.g., Latent Dirichlet Allocation) to automatically search and analyze blogs for product mentions, and to then find out whether people are saying good or bad things about those products.
Decontamination of Sequential Effects
Description coming soon.
