Predicting decision-making in virtual environments: an eye movement analysis with household products
[EN] Understanding consumer behavior is crucial for increasing the likelihood of product success. Virtual Reality head-mounted displays incorporating physiological techniques such as eye-tracking offer novel opportunities to study user behavior in decision-making tasks. These methods reveal unconsci...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/204403 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/204403 |
| Access Level: | acceso abierto |
| Palabra clave: | Virtual reality Extended reality User experience Eye movements Gaze bias Decision making Product evaluation Statistical learning ESTADISTICA E INVESTIGACION OPERATIVA EXPRESION GRAFICA EN LA INGENIERIA |
| Sumario: | [EN] Understanding consumer behavior is crucial for increasing the likelihood of product success. Virtual Reality head-mounted displays incorporating physiological techniques such as eye-tracking offer novel opportunities to study user behavior in decision-making tasks. These methods reveal unconscious or undisclosed consumer responses. Yet, research into gaze patterns during virtual product evaluations remains scarce. In this context, an experiment was conducted to investigate users¿ gaze behavior when evaluating their preferences for 64 virtual prototypes of a bedside table. Here, 24 participants evaluated and selected their preferred design through eight repeated tasks of an 8-AFC, with individual evaluations conducted for each design to ensure the reliability of the findings. Several eye-tracking metrics were computed (i.e., gaze time, visits, and time to first gaze), statistical tests were applied, and a Long Short-Term Memory model was created to recognize decisions based on attentional patterns. Our results revealed that the Gaze Cascade Model was replicated in virtual environments and that a correlation between product liking and eye-tracking metrics exists. We recognize subjects¿ decisions with a 90% accuracy, based on their eye patterns during the three seconds before their decision. The results suggest that eye-tracking can be an effective tool for decision-making prediction during product assessment in virtual environments. |
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