Abstract
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.
Original language | English |
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Title of host publication | Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems |
Publisher | ACM |
Pages | 1295-1306 |
Number of pages | 12 |
ISBN (Electronic) | 978-1-4503-4655-9 |
DOIs | |
Publication status | Published - 9 May 2017 |
MoE publication type | A4 Article in a conference publication |
Event | ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Colorado Convention Center, Denver, United States Duration: 6 May 2017 → 11 May 2017 Conference number: 35 https://chi2017.acm.org/ |
Conference
Conference | ACM SIGCHI Annual Conference on Human Factors in Computing Systems |
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Abbreviated title | ACM CHI |
Country | United States |
City | Denver |
Period | 06/05/2017 → 11/05/2017 |
Internet address |
Keywords
- Approximate Bayesian computation
- cognitive models
- computational rationality
- inverse modeling