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From Eye Movement to Cognition: Toward a General Framework of Inference Comment on Liechty et al., 2003

Published online by Cambridge University Press:  01 January 2025

Gary Feng
Gary Feng*
Affiliation:
Duke University
*
Requests for reprints should be sent to Gary Feng, Department of Psychology, Social and Health Sciences, Box 90085, Durham NC 27708-0085. E-Mail:garyfeng@duke.edu
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Abstract

Liechty, Pieters & Wedel (2003) developed a hidden Markov Model (HMM) to identify the states of an attentional process in an advertisement viewing task. This work is significant because it demonstrates the benefits of stochastic modeling and Bayesian estimation in making inferences about cognitive processes based on eye movement data. One limitation of the proposed approach is that attention is conceptualized as an autonomous random process that is affected neither by the overall layout of the stimulus nor by the visual information perceived during the current fixation. An alternative model based on the input-output hidden Markov model (IOHMM; Bengio, 1999) is suggested as an extension of the HMM. The need for further studies that validate the HMM classification results is also discussed.

Keywords

hidden Markov modelstochastic modelingBayesian estimationHMM classification
Type
Commentary
Information
Psychometrika , Volume 68 , Issue 4 , December 2003 , pp. 551 - 556
Copyright
Copyright © 2003 The Psychometric Society

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