A framework for computational models of human memory

Matthew A. Kelly; Robert L. West

A framework for computational models of human memory

Matthew A. Kelly, Robert L. West

College of Information Sciences and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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 language	English (US)
Title of host publication	FS-17-01
Subtitle of host publication	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
Publisher	AI Access Foundation
Pages	376-381
Number of pages	6
ISBN (Electronic)	9781577357940
State	Published - 2017
Event	2017 AAAI Fall Symposium - Arlington, United States Duration: Nov 9 2017 → Nov 11 2017

Publication series

Name	AAAI Fall Symposium - Technical Report
Volume	FS-17-01 - FS-17-05

Other

Other	2017 AAAI Fall Symposium
Country/Territory	United States
City	Arlington
Period	11/9/17 → 11/11/17

All Science Journal Classification (ASJC) codes

General Engineering

Cite this

Kelly, M. A., & 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 A. ; 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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title = "A framework for computational models of human memory",

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.",

author = "Kelly, {Matthew A.} and West, {Robert L.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 2017 AAAI Fall Symposium ; Conference date: 09-11-2017 Through 11-11-2017",

year = "2017",

language = "English (US)",

series = "AAAI Fall Symposium - Technical Report",

publisher = "AI Access Foundation",

pages = "376--381",

booktitle = "FS-17-01",

address = "United States",

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Kelly, MA & 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 A.; 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 proceeding › Conference contribution

TY - GEN

T1 - A framework for computational models of human memory

AU - Kelly, Matthew A.

AU - West, Robert L.

PY - 2017

Y1 - 2017

N2 - 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.

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

AN - SCOPUS:85044448181

T3 - AAAI Fall Symposium - Technical Report

SP - 376

EP - 381

BT - FS-17-01

PB - AI Access Foundation

T2 - 2017 AAAI Fall Symposium

Y2 - 9 November 2017 through 11 November 2017

ER -

Kelly MA, 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).

A framework for computational models of human memory

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