A cognitive model of how people make decisions through interaction with visual displays

Xiuli Chen, Sandra Starke, Christopher Baber, Andrew Howes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationCHI '17
Subtitle of host publicationProceedings of the 2017 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages1205-1216
Number of pages12
ISBN (Electronic)9781450346559
DOIs
Publication statusPublished - 6 May 2017
EventACM CHI’17 Conference on Human Factors in Computing Systems - Denver, United States
Duration: 6 May 201711 May 2017

Conference

ConferenceACM CHI’17 Conference on Human Factors in Computing Systems
Country/TerritoryUnited States
CityDenver
Period6/05/1711/05/17

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