A study of unconscious emotional and cognitive responses to tourism images using a neuroscience method

Abstract

Purpose -This applied neuroscience study aims to understand how direct and unconscious emotional and cognitive responses underlie travel destination preferences. State-of-the-art neuroscience tools and methods were used, including stationary eye tracking and brain scanning electroencephalography (EEG) to assess emotional and cognitive responses to destination images and assets. To the researchers' knowledge, this study is the first applied neuroscience study in tourism research and thus opens a new path of research and enquiry to this area. This paper is an attempt to understand specific mental processes in human tourism behaviours, and it is suggest that unconscious emotional and cognitive responses are natural processes that need to be studied and understood, not as special cases, but embedded as natural parts of tourism research.Design/methodology/approach -To better understand consumers' unconscious responses to possible travel destinations, a 3 Â 5 factorial design was run with the factors being stimulus type (images, printed names and videos) and travel destination (Dubai, Abu Dhabi, Hong Kong, New York and London). Eyetracking calibration was done with a nine-point fixation test and the EEG calibration was done using functional localizer tests based on the ABM B-ALERT calibration process. This calibration procedure allows reliable tracking of emotional and cognitive responses over time. Thirty Emirati (nationals of the UAE) participants, consisting of equal numbers of males and females (15) were recruited from the UAE and signed informed consent. Each participant was positioned in front of an eye tracker and computer screen, and brainscanning equipment was mounted; then, each participant underwent eye-tracking and neuroimaging calibration procedures. A Tobii T60XL eye tracker and an ABM X-10 EEG brain scanner, both running iMotions v5.1 in a Windows 7 environment, were used.Findings -General emotional and cognitive differences were identified between the channels through which travel destinations are presented. Words about and names of travel destinations cause higher cognitive

Deep Learning for EEG-Based Preference Classification in Neuromarketing

“…We classified these predictive features based on the EEG signal types: (1) rhythms; and (2) transient activities. [17] Viewing brands and products ERP P300 BCI [11] Watching TV ads EEG Alpha (for emotional state) EMG, BCI and GSR [36] Viewing brands and images ERP N200 and P300 BCI [37] Watching TV ads EEG Alpha band frontal asymmetry BCI [35] Watching TV ads EEG Theta and gamma Heart rate, BCI and GSR [28] Watching TV ads EEG Asymmetrical increase in theta and alpha in PSD BCI [38] Viewing brand names ERP N400 BCI and EOG for eye movement [18] Viewing products and prices ERP FN400, LPC, and P200 BCI [39] Viewing products EEG Alpha, beta, theta, gamma, and delta BCI, Eye tracking [40] Viewing products ERP P300 BCI [41] Watching TV ads EEG Theta and alpha Heart rate, BCI and GSR [3] Viewing products ERSP and ERP Theta, N200, and FRN BCI [24] Watching ads (movie trailers) EEG (64 electrodes) Beta and gamma oscillations BCI and EOG for eye movement (2 electrode) [20] Watching TV ads dense-array EEG Three epochs: 200-350, 350-500, and 500-800 BCI [42] Viewing brand names ERP LPP BCI [43] Viewing product images ERP N200, LPP, and PSW BCI [30] Watching ad videos EEG Theta and alpha Heart rate, BCI and GSR [22] Viewing product images EEG Delta, theta, alpha, beta, and gamma BCI [29] Viewing product images EEG Alpha BCI [44] Viewing ads of food products EEG Delta, theta, alpha, beta, and gamma BCI [32] Viewing products and prices EEG Theta BCI [31] Tasting drinks EEG Alpha BCI [33] Viewing and touching products EEG Alpha and theta BCI [27] Viewing product images ERSP, ERP Theta, beta and N200 BCI [45] Viewing tourism images, videos and words EEG Delta, theta, alpha, beta and gamma BCI and GSR…”

“…The researchers used eye tracking for choosing the preferred product. Michael et al [45] used the same approach (EEG with eye tracking) to investigate the emotional reactions of tourism preferences by using different stimuli (words, images, and video). The authors observed that the images had higher affective responses than those of words in travel decision-making driven by the unconscious preference.…”

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“…Todos los GIFs ofrecieron puntuaciones positivas en la Valencia y valores negativos en la variable Activación (Estudio 1), situándose en el cuarto cuadrante del modelo circumplejo. En este cuadrante el consumidor se halla en un estado de calma y relajación, ya que el neocortex disminuye su nivel de activación para ahorrar energía, al tiempo que el área cerebral correspondiente a las emociones cobra mayor importancia (Michael, Ramsoy, Stephens y Kotsi, 2019;Modica, et al, 2018).…”

The perception of travellers and the World Heritage Site image

“…Th e concept of destination image consists of two components: cognitive, that refers to the knowledge and beliefs about destination's attributes, and aff ective, that captures tourist's feelings toward a destination (Beerli & Martin, 2004a;Konecnik & Gartner, 2007;Papadimitriou, Kaplanidou, & Apostolopoulou, 2015;Akgün, Senturk, Keskin, & Onal, 2019). Baloglu & McCleary (1999) examined four Mediterranean countries' image destinations and confi rmed the relationship and interconnection between the cognitive and aff ective components (Baloglu, 2000;Beerli & Martín, 2004b;Lin, Morais, Kerstetter, & Hou, 2007;Michael, Ramsoy, Stephens, & Kotsi, 2019). In order to measure image of tourism destination Russel, Ward & Prat (1981) suggested that cognitive destination image can be measured using structured technique or multi-attribute approach that is based on destination-specifi c factors, and the aff ective destination image using four bipolar scales (arousing-sleepy, pleasant-unpleasant, exciting-gloomy, relaxing-distressing).…”