Past and Current Projects

Factors Influencing Learning and Memory Retention

The way we study material influences how well we retain it. Psychologists have established that spaced practice leads to better retention of declarative knowledge (facts such as foreign language vocabulary) than massed practice. However, the exact relationship between spacing and retention depends in a significant way on the duration of time over which the material must be retained. We are exploring existing and novel computational models to explain a range of data on massed versus spaced practice.

We have developed a Multiscale Context Model (MCM) that predicts the optimal spacing of study in a wide range of experiments, over retention intervals ranging from minutes to a year. The model makes surprising and counterintuitive predictions that we are currently testing.

In other projects, we are developing mechanistic accounts that explain other phenomena surrounding the conditions under which individuals are likely to learn and retain material. For example, when an individual is tested and then told the correct answer, their learning is better than when they merely study the material. We explain this finding with an error correct learning account in which a better error signal is obtained when the individual produces a response, even if incorrect. We've also addressed the counterintuitive result that when individuals are willing to guess an answer, even when they are wrong, they will learn the material with less study than when they are unwilling to venture a guess. We have developed a model that links the strength of learning to willingness to guess (or confidence in a guess).

For a more elaborate description of our work on memory modeling, click here.


Rob Lindsey (Computer Science, Colorado)
Warren Fernandes (ECE, Colorado)
Michael Howe (Computer Science, Colorado, now at UT Austin)
Owen Lewis (Applied Math, Colorado; now at MIT)
James Michaelis (Computer Science, Colorado, now at RPI)
Kenshiro Nakagawa (Computer Science, Colorado)


Nicholas Cepeda (Psychology, York)
Sean Kang (Psychology, UCSD)
Hal Pashler (Psychology, UCSD)
Ed Vul (Psycology, UCSD)