Model-based Evaluation of Recall-based ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
Model-based Evaluation of Recall-based Interaction Techniques
Author(s) :
Gori, Julien [Auteur]
Institut des Systèmes Intelligents et de Robotique [ISIR]
Fruchard, Bruno [Auteur]
Technology and knowledge for interaction [LOKI]
Bailly, Gilles [Auteur]
Institut des Systèmes Intelligents et de Robotique [ISIR]
Institut des Systèmes Intelligents et de Robotique [ISIR]
Fruchard, Bruno [Auteur]
Technology and knowledge for interaction [LOKI]
Bailly, Gilles [Auteur]
Institut des Systèmes Intelligents et de Robotique [ISIR]
Conference title :
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2024)
City :
Honolulu, HI
Country :
Etats-Unis d'Amérique
Start date of the conference :
2024-05-11
Publisher :
ACM
Publication date :
2024-05-11
English keyword(s) :
memory
interaction technique
model-based evaluation
maximum likelihood
experimental design
interaction technique
model-based evaluation
maximum likelihood
experimental design
HAL domain(s) :
Informatique [cs]/Interface homme-machine [cs.HC]
English abstract : [en]
This article tackles two challenges of the empirical evaluation of interaction techniques that rely on user memory, such as hotkeys, here coined Recall-based interaction techniques (RBITs): (1) the lack of guidance to ...
Show more >This article tackles two challenges of the empirical evaluation of interaction techniques that rely on user memory, such as hotkeys, here coined Recall-based interaction techniques (RBITs): (1) the lack of guidance to design the associated study protocols, and (2) the difficulty of comparing evaluations performed with different protocols. To address these challenges, we propose a model-based evaluation of RBITs. This approach relies on a computational model of human memory to (1) predict the informativeness of a particular protocol through the variance of the estimated parameters (Fisher Information) (2) compare RBITs recall performance based on the inferred parameters rather than behavioral statistics, which has the advantage of being independent of the study protocol. We also release a Python library implementing our approach to aid researchers in producing more robust and meaningful comparisons of RBITs.Show less >
Show more >This article tackles two challenges of the empirical evaluation of interaction techniques that rely on user memory, such as hotkeys, here coined Recall-based interaction techniques (RBITs): (1) the lack of guidance to design the associated study protocols, and (2) the difficulty of comparing evaluations performed with different protocols. To address these challenges, we propose a model-based evaluation of RBITs. This approach relies on a computational model of human memory to (1) predict the informativeness of a particular protocol through the variance of the estimated parameters (Fisher Information) (2) compare RBITs recall performance based on the inferred parameters rather than behavioral statistics, which has the advantage of being independent of the study protocol. We also release a Python library implementing our approach to aid researchers in producing more robust and meaningful comparisons of RBITs.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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