Inferring Cognitive Models from Data using Approximate Bayesian Computation

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Date
2017-05-09
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Language
en
Pages
12
1295-1306
Series
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
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.
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Keywords
Approximate Bayesian computation, cognitive models, computational rationality, inverse modeling
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Citation
Kangasrääsiö , A , Athukorala , K , Howes , A , Corander , J , Kaski , S & Oulasvirta , A 2017 , Inferring Cognitive Models from Data using Approximate Bayesian Computation . in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems . ACM , pp. 1295-1306 , ACM SIGCHI Annual Conference on Human Factors in Computing Systems , Denver , Colorado , United States , 06/05/2017 . https://doi.org/10.1145/3025453.3025576