A cognitive model of how people make decisions through interaction with visual displays
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Colleges, School and Institutes
In this paper we report a cognitive model of how people make decisions through interaction. The model is based on the assumption that interaction for decision making is an example of a Partially Observable Markov Decision Process (POMDP) in which observations are made by limited perceptual systems that model human foveated vision and decisions are made by strategies that are adapted to the task. We illustrate the model by applying it to the task of determining whether to block a credit card given a number of variables including the location of a transaction, its amount, and the customer history. Each of these variables have a different validity and users may weight them accordingly. The model solves the POMDP by learning patterns of eye movements (strategies) adapted to different presentations of the data. We compare the model behavior to human performance on the credit card transaction task.
This paper has been awarded an Honorable Mention award, which means that it is ranked among the top 5% of all submissions to the SIGCHI 2017 conference. The conference received over 2400 submissions and 97 were chosen for Honorable Mention.
|Title of host publication||Proceedings of the ACM CHI’17 Conference on Human Factors in Computing Systems|
|Publication status||Published - 6 May 2017|
|Event||ACM CHI’17 Conference on Human Factors in Computing Systems - Denver, United States|
Duration: 6 May 2017 → 11 May 2017
|Conference||ACM CHI’17 Conference on Human Factors in Computing Systems|
|Period||6/05/17 → 11/05/17|