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Affective preference measurement of product appearance based on event-related potentials

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Abstract

The product appearance plays an important role in users’ purchase decision, while the emotions elicited by the product appearance are subtle, low intensity and not easy to obtain. Event-related potentials (ERPs) can reflect people’s psychological activities and effectively identify the characteristics of brain’s affective process. In the present study, the affective preference measurement of humidifier appearance was conducted based on the theory of affective design and event-related potentials. Firstly, the collected humidifier pictures were screened to be the experimental stimuli through multidimensional scaling analysis and cluster analysis. During the affective preference measurement experiment, the participants were asked to judge the affective preference level (liked vs. neutral vs. disliked) of humidifier pictures with the mouse button clicking (left, middle, right), and the electroencephalograph signals were recorded at the same time. ERPs results showed that the frontal and central N1 and frontal, frontal–central and central late positive potential can be taken as the indexes to measure participants’ affective preference of humidifier appearance. In conclusion, the participants’ affective preference level of humidifier appearance can be measured by ERPs. The experiment results can be used to guide product design, by measuring the brain activities of different prototypes at the phase of product development we can roughly infer which one is the user preferred or not preferred.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 71471033, 71171041). We thank the participants from the Northeastern University. Finally, thank the editor and anonymous reviewers for their valuable comments and advice.

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Correspondence to Xue-shuang Wang.

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Guo, F., Wang, Xs., Liu, Wl. et al. Affective preference measurement of product appearance based on event-related potentials. Cogn Tech Work 20, 299–308 (2018). https://doi.org/10.1007/s10111-018-0463-5

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