Cognitive Modeling: From GOMS to Deep Reinforcement Learning

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

doi:10.1145/3613905.3636278

Cognitive Modeling: From GOMS to Deep Reinforcement Learning

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

Research output: Contribution to conference › Paper › Scientific › peer-review

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 modeling approaches powered by machine learning methods, in particular deep reinforcement learning. The course is built around hands-on Python programming using notebooks.

Original language	English
Number of pages	2
DOIs	https://doi.org/10.1145/3613905.3636278
Publication status	Published - 11 May 2024
MoE publication type	Not Eligible
Event	ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Honolulu, USA, Honolulu, United States Duration: 11 May 2024 → 16 May 2024 https://chi2024.acm.org/

Conference

Conference	ACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated title	ACM CHI
Country/Territory	United States
City	Honolulu
Period	11/05/2024 → 16/05/2024
Internet address	https://chi2024.acm.org/

Keywords

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

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10.1145/3613905.3636278

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title = "Cognitive Modeling: From GOMS to Deep Reinforcement Learning",

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 modeling approaches powered by machine learning methods, in particular deep reinforcement learning. The course is built around hands-on Python programming using notebooks.",

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

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

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year = "2024",

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T1 - Cognitive Modeling: From GOMS to Deep Reinforcement Learning

AU - Jokinen, Jussi P.P.

AU - Oulasvirta, Antti

AU - Howes, Andrew

PY - 2024/5/11

Y1 - 2024/5/11

N2 - 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 modeling approaches powered by machine learning methods, in particular deep reinforcement learning. The course is built around hands-on Python programming using notebooks.

AB - 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 modeling approaches powered by machine learning methods, in particular deep reinforcement learning. The course is built around hands-on Python programming using notebooks.

KW - cognitive architectures

KW - Cognitive modeling

KW - computational rationality

KW - cooperative intelligence

KW - reinforcement learning

KW - user interface optimization

UR - http://www.scopus.com/inward/record.url?scp=85194177697&partnerID=8YFLogxK

U2 - 10.1145/3613905.3636278

DO - 10.1145/3613905.3636278

M3 - Paper

AN - SCOPUS:85194177697

T2 - ACM SIGCHI Annual Conference on Human Factors in Computing Systems

Y2 - 11 May 2024 through 16 May 2024

ER -

Cognitive Modeling: From GOMS to Deep Reinforcement Learning

Abstract

Conference

Keywords

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