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Inferring Cognitive Models from Data using Approximate Bayesian Computation

Published: 02 May 2017 Publication History

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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cover image ACM Conferences
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
May 2017
7138 pages
ISBN:9781450346559
DOI:10.1145/3025453
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 02 May 2017

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Author Tags

  1. approximate bayesian computation
  2. cognitive models in hci
  3. computational rationality
  4. inverse modeling

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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