A framework for computational models of human memory

Matthew Kelly, Robert L. West

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

1 Citation (Scopus)

Abstract

We present analysis of existing memory models, examining how models represent knowledge, structure memory, learn, make decisions, and predict reaction times. On the basis of this analysis, we propose a theoretical framework that characterizes memory modelling in terms of six key decisions: (1) choice of knowledge representation scheme, (2) choice of data structure, (3) choice of associative architecture, (4) choice of learning rule, (5) choice of time variant process, and (6) choice of response decision criteria. This framework is both descriptive and prescriptive: we intend to both describe the state of the literature and outline what we believe is the most fruitful space of possibilities for the development of future memory models.

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
Pages376-381
Number of pages6
ISBN (Electronic)9781577357940
StatePublished - Jan 1 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

Other

Other2017 AAAI Fall Symposium
CountryUnited States
CityArlington
Period11/9/1711/11/17

Fingerprint

Data storage equipment
Knowledge representation
Data structures

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Kelly, M., & West, R. L. (2017). A framework for computational models of human memory. In FS-17-01: Artificial 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 (pp. 376-381). (AAAI Fall Symposium - Technical Report; Vol. FS-17-01 - FS-17-05). AI Access Foundation.
Kelly, Matthew ; West, Robert L. / A framework for computational models of human memory. FS-17-01: Artificial 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. AI Access Foundation, 2017. pp. 376-381 (AAAI Fall Symposium - Technical Report).
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Kelly, M & West, RL 2017, A framework for computational models of human memory. in FS-17-01: Artificial 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. AAAI Fall Symposium - Technical Report, vol. FS-17-01 - FS-17-05, AI Access Foundation, pp. 376-381, 2017 AAAI Fall Symposium, Arlington, United States, 11/9/17.

A framework for computational models of human memory. / Kelly, Matthew; West, Robert L.

FS-17-01: Artificial 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. AI Access Foundation, 2017. p. 376-381 (AAAI Fall Symposium - Technical Report; Vol. FS-17-01 - FS-17-05).

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

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AB - We present analysis of existing memory models, examining how models represent knowledge, structure memory, learn, make decisions, and predict reaction times. On the basis of this analysis, we propose a theoretical framework that characterizes memory modelling in terms of six key decisions: (1) choice of knowledge representation scheme, (2) choice of data structure, (3) choice of associative architecture, (4) choice of learning rule, (5) choice of time variant process, and (6) choice of response decision criteria. This framework is both descriptive and prescriptive: we intend to both describe the state of the literature and outline what we believe is the most fruitful space of possibilities for the development of future memory models.

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M3 - Conference contribution

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Kelly M, West RL. A framework for computational models of human memory. In FS-17-01: Artificial 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. AI Access Foundation. 2017. p. 376-381. (AAAI Fall Symposium - Technical Report).