Directed by Jeff Starns, the Dynamic Memory Modeling lab explores memory processes as they unfold in time by applying computational models that predict both accuracy and response time data. The models simulate the accumulation of memory evidence as a drift diffusion process that can be linked to the firing rates of neural populations. Experiments in this lab mostly involve tasks in which participants must attribute items to specific past contexts, such as a word list or a particular speaker. Models are used to address a number of questions: What types of evidence inform memory decisions? How do people balance speed and accuracy in memory decisions? What factors lead to distortions in memory? How does context information guide memory retrieval?