Erik M. Altmann

Episodic Memory for External Information Degree Type: Ph.D. in Computer Science
Advisor(s): Bonnie John
Graduated: August 1996

Abstract:

People make use of hidden external information, first recalling that it exists and then finding it. This dissertation investigates the memory phenomena involved in recalling that external information exists. We present data in which a programmer navigates to hidden features in a real-world task environment. We then present a model that accounts for this navigation by encoding and using simple episodic memories for having seen a feature. The model inherits constraints from its underlying cognitive architecture, which specify that learning is passive and pervasive, and that it creates simple memories that depend on the feature itself being present as a cue. The nature of these memories requires the model to recall features to its mind’s eye as cues in order to retrieve them. This retrieval process requires domain knowledge: familiarity with features in order to imagine them, and an idea of when it would be useful to recall having seen them. Recalling that a hidden feature exists prompts the model to scroll to that feature. Thus the model’s access to external information is a function of passively-encoded episodic memories, and retrieval of these memories using knowledge. As a claim applied to people, this appears to overlap with a recently published theory of long-term working memory. This theory proposes that experts, for example in chess, use long-term memory to expand their working memory in their domain of expertise. We propose a ubiquitous episodic long-term working memory, in which people store information about features with little effort, and from which they retrieve this information when it is relevant.

Thesis Committee:
Bonnie John (Chair)
John Anderson
Jim Morris
Clayton Lewis (University of Colorado, Boulder)

James Morris, Head, Computer Science Department
Raj Reddy Dean, School of Computer Science

Keywords:
Cognitive science, Soar, episodic memory, psychology of programming, human-computer interaction, artificial intelligence