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Does the Research Question Structure Impact the Attention Model? User Study Experiment

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Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

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Abstract

The main purpose of this work is to analyse the influence of questions given in perceptual experiments on the reliability of received results. The problem have been investigated in literature, however in a form of theoretical discussion rather than quantitative evaluation. Therefore in the paper in include quantitative evaluation and results visualization. Nevertheless the main novelty is employment of human cognitive style for research questions problem. The analysis was supported with FRIS\(\textregistered \) [7] that is a psychometric model and also an inventory for cognitive styles measurement. We conduct the user study in order to investigate the way the observers understand research question given during the experiment execution. Therefore two kinds of questions were employed to the experiment: the one not suggesting a response or the one suggesting an answer. In researches we focused on the images’ objects that attach observer’s attention dependent on a structure of a given question. The obtained results were submitted to statistical analysis that proved that correctness of experimental results is highly correlated to the way the research question is designed. The questions that suggesting the answer affects the outcome of the perceptual experiment and make them unreliable. The results confirmed that the human cognitive style is highly correlated with the research questions interpretation.

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Acknowledgement

We would like to give a special thanks to Marcelina Ginalska for helping us in conducting perceptual experiment.

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Correspondence to Malwina Dziśko .

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Dziśko, M., Lewandowska, A., Samborska-Owczarek, A. (2018). Does the Research Question Structure Impact the Attention Model? User Study Experiment. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_42

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_42

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