Holographic declarative memory: Using distributional semantics within ACT-R

Matthew A. Kelly, David Reitter

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distributional semantics models can process millions of data points to infer semantic similarities from language data or to infer product recommendations from patterns of user preferences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on iterated decisions with initially unknown payoffs. The model we propose provides a flexible, scalable alternative to ACT-R's declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition.

Original languageEnglish (US)
Title of host publicationFS-17-01
Subtitle of host publicationArtificial Intelligence for Human-Robot Interaction; FS-17-02: Cognitive Assistance in Government and Public Sector Applications; FS-17-03: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; FS-17-04: Human-Agent Groups: Studies, Algorithms and Challenges; FS-17-05: A Standard Model of the Mind
PublisherAI Access Foundation
Number of pages6
ISBN (Electronic)9781577357940
StatePublished - 2017
Event2017 AAAI Fall Symposium - Arlington, United States
Duration: Nov 9 2017Nov 11 2017

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-17-01 - FS-17-05


Other2017 AAAI Fall Symposium
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • General Engineering


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