A Bayesian Account of Reconstructive Memory
Version of Record online: 30 JAN 2009
Copyright © 2009 Cognitive Science Society, Inc.
Topics in Cognitive Science
Volume 1, Issue 1, pages 189–202, January 2009
How to Cite
Hemmer, P. and Steyvers, M. (2009), A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science, 1: 189–202. doi: 10.1111/j.1756-8765.2008.01010.x
- Issue online: 30 JAN 2009
- Version of Record online: 30 JAN 2009
- Received 23 October 2008; received in revised form 9 November 2008; accepted 11 November 2008
- Long-term memory;
- Prior knowledge;
- Bayesian models;
- Reconstructive memory
It is well established that prior knowledge influences reconstruction from memory, but the specific interactions of memory and knowledge are unclear. Extending work by Huttenlocher et al. (Psychological Review, 98  352; Journal of Experimental Psychology: General, 129  220), we propose a Bayesian model of reconstructive memory in which prior knowledge interacts with episodic memory at multiple levels of abstraction. The combination of prior knowledge and noisy memory representations is dependent on familiarity. We present empirical evidence of the influences of prior knowledge at multiple levels of abstraction, showing that the reconstruction of familiar objects is influenced toward the specific prior for that object, while unfamiliar objects are influenced toward the overall category.