Cognitive Modelling: From GOMS to Deep Reinforcement Learning

Jussi P.P. Jokinen; Antti Oulasvirta; Andrew Howes

doi:10.1145/3491101.3503771

Cognitive Modelling: From GOMS to Deep Reinforcement Learning

Jussi P.P. Jokinen, Antti Oulasvirta, Andrew Howes

Computer Science

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

Abstract

This course introduces computational cognitive modeling for researchers and practitioners in the field of HCI. Cognitive models use computer programs to model how users perceive, think, and act in human-computer interaction. They offer a powerful approach for understanding interactive tasks and improving user interfaces. This course starts with a review of classic architecture based models such as GOMS and ACT-R. It then rapidly progresses to introducing modern modelling approaches powered by machine learning methods, in particular deep learning, reinforcement learning (RL), and deep RL. The course is built around hands-on Python programming using notebooks.

Original language	English
Title of host publication	CHI EA '22
Subtitle of host publication	Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
Editors	Simone Barbosa, Cliff Lampe, Caroline Appert, David A. Shamma
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	1-3
Number of pages	3
ISBN (Electronic)	9781450391566
DOIs	https://doi.org/10.1145/3491101.3503771
Publication status	Published - 28 Apr 2022
Event	CHI '22: CHI Conference on Human Factors in Computing Systems - New Orleans, United States Duration: 29 Apr 2022 → 5 May 2022

Publication series

Name	CHI: Conference on Human Factors in Computing Systems

Conference

Conference	CHI '22
Country/Territory	United States
City	New Orleans
Period	29/04/22 → 5/05/22

Bibliographical note

Publisher Copyright:
© 2022 Owner/Author.

Keywords

cognitive architectures
Cognitive modeling
computational rationality
cooperative intelligence
deep learning
reinforcement learning
user interface optimization

ASJC Scopus subject areas

Human-Computer Interaction
Computer Graphics and Computer-Aided Design
Software

Access to Document

10.1145/3491101.3503771

Cite this

Jokinen, J. P. P., Oulasvirta, A., & Howes, A. (2022). Cognitive Modelling: From GOMS to Deep Reinforcement Learning. In S. Barbosa, C. Lampe, C. Appert, & D. A. Shamma (Eds.), CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-3). Article 121 (CHI: Conference on Human Factors in Computing Systems). Association for Computing Machinery (ACM). https://doi.org/10.1145/3491101.3503771

Jokinen, Jussi P.P. ; Oulasvirta, Antti ; Howes, Andrew. / Cognitive Modelling : From GOMS to Deep Reinforcement Learning. CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. editor / Simone Barbosa ; Cliff Lampe ; Caroline Appert ; David A. Shamma. New York : Association for Computing Machinery (ACM), 2022. pp. 1-3 (CHI: Conference on Human Factors in Computing Systems).

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abstract = "This course introduces computational cognitive modeling for researchers and practitioners in the field of HCI. Cognitive models use computer programs to model how users perceive, think, and act in human-computer interaction. They offer a powerful approach for understanding interactive tasks and improving user interfaces. This course starts with a review of classic architecture based models such as GOMS and ACT-R. It then rapidly progresses to introducing modern modelling approaches powered by machine learning methods, in particular deep learning, reinforcement learning (RL), and deep RL. The course is built around hands-on Python programming using notebooks.",

keywords = "cognitive architectures, Cognitive modeling, computational rationality, cooperative intelligence, deep learning, reinforcement learning, user interface optimization",

author = "Jokinen, {Jussi P.P.} and Antti Oulasvirta and Andrew Howes",

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year = "2022",

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day = "28",

doi = "10.1145/3491101.3503771",

language = "English",

series = "CHI: Conference on Human Factors in Computing Systems",

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Jokinen, JPP, Oulasvirta, A & Howes, A 2022, Cognitive Modelling: From GOMS to Deep Reinforcement Learning. in S Barbosa, C Lampe, C Appert & DA Shamma (eds), CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems., 121, CHI: Conference on Human Factors in Computing Systems, Association for Computing Machinery (ACM), New York, pp. 1-3, CHI '22, New Orleans, Louisiana, United States, 29/04/22. https://doi.org/10.1145/3491101.3503771

Cognitive Modelling: From GOMS to Deep Reinforcement Learning. / Jokinen, Jussi P.P.; Oulasvirta, Antti; Howes, Andrew.
CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. ed. / Simone Barbosa; Cliff Lampe; Caroline Appert; David A. Shamma. New York: Association for Computing Machinery (ACM), 2022. p. 1-3 121 (CHI: Conference on Human Factors in Computing Systems).

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

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Jokinen JPP, Oulasvirta A, Howes A. Cognitive Modelling: From GOMS to Deep Reinforcement Learning. In Barbosa S, Lampe C, Appert C, Shamma DA, editors, CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery (ACM). 2022. p. 1-3. 121. (CHI: Conference on Human Factors in Computing Systems). doi: 10.1145/3491101.3503771

Cognitive Modelling: From GOMS to Deep Reinforcement Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this