Memory Lab














Current Research

Our research seeks to understand basic processes in recognition memory and reasoning, using methods rooted in signal detection theory and neuroscience (EEG). We view computational models as essential tools for interpreting data.  Our newest work has focused on eyewitness identifications using signal detection models.


Principal Investigator

  Caren Rotello



I am not accepting new students.

Alumni and past lab members:

Neil Macmillan now plays his violin on the coast of Maine.


Tina Chen, Ph.D., is now a Lecturer at IUPUI.

Angela Pazzaglia, Ph.D., is now Senior Economist at Bates White Economic Consulting.





Chad Dube, Ph.D., is now an Associate Professor at University of South Florida.

  Aycan Kapucu, Ph.D., is now an Assistant Professor at Ege University (Turkey).

Mungchen (Will) Wong, Ph.D., is now Associate Director of Data Analytics at WebMD.

Min Zeng, MS, now a manager at Nielson.

 Davide Bruno, Ph.D., now a Lecturer at Liverpool John Moores University.

Ruthanna Gordon, Ph.D., now an Associate at Booz Allen Hamilton.

   John Reeder, Ph.D., now Associate Professor and Department Chair at Simmons College

Michael Verde, Ph.D., now Lecturer at University of Plymouth



Current publications are listed on my People page.


ROC Stats

Sampling distributions of ROC statistics (Macmillan, Rotello, & Miller, 2004, Perception & Psychophysics)

Single-point sensitivity estimates (d', A', H-F, percent correct, etc):

statistical properties: standard errors and bias in the estimate(Verde, Macmillan, & Rotello, 2006, Perception & Psychophysics)

problems comparing the estimates over conditions that differ in response bias (Rotello, Masson, & Verde, 2008, Perception & Psychophysics)

How to fit a confidence-based ROC with SDT (spreadsheet described by Pazzaglia, Dube, & Rotello, 2013): Download the .zip file to the right.  It contains an Excel sheet with complete instructions.

Source ROC fits (Hautus, Macmillan, & Rotello, 2008, Psychonomic Bulletin & Review)

Gaussian Distributions, Type I error

Gaussian, Power

Rectangular distribution, power

Rectangular distributions, Type I error


Supporting documents for Dube, Rotello, and Heit (2011, Psychological Review)