Abstract
The research demonstrates the use of hidden Markov models (HMMs) in analyzing fixation data recorded by an eye-tracker. The visual activity was registered while performing pairwise comparisons of simple marketing messages. The marketing information was presented in a form of digital leaflets appearing on a computer screen and differed in the components’ arrangement and graphical layout. Better variants were selected by clicking on them with a mouse. A simulation experiment was performed to determine best HMMs in terms of information criteria. Seven selected models were presented in detail, four of them graphically illustrated and thoroughly analyzed. The identified hidden states along with predicted transition and emission probabilities allowed for the description of possible subjects’ visual behavior. Hypotheses about relations between these strategies and marketing message design factors were also put forward and discussed.
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The research was partially financially supported by Polish National Science Centre Grant No. 2017/27/B/HS4/01876.
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Grobelny, J., Michalski, R. (2020). Investigating Human Visual Behavior by Hidden Markov Models in the Design of Marketing Information. In: Cassenti, D. (eds) Advances in Human Factors and Simulation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 958. Springer, Cham. https://doi.org/10.1007/978-3-030-20148-7_22
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