How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data

Abstract

This study examines how quickly we can predict users' ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users' ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regarding user perceptions on visual aesthetics towards the same websites. We conduct an experiment with 23 experienced users in online shopping, capture gaze behavior and through employing machine learning we examine how fast we can accurately predict their ratings. The findings show that after 25 s we can predict ratings with an error rate ranging from 9% to 11% depending on which facet of visual aesthetic is examined. Furthermore, within the first 15 s we can have a good and sufficient prediction for simplicity and colorfulness, with error rates 11% and 12% respectively. For diversity and craftsmanship, 20 s are needed to get a good and sufficient prediction similar to the one from 25 s. The findings indicate that we need more than 10 s of viewing time to be able to accurately capture perceptions on visual aesthetics. The study contributes by offering new ways for designing systems that will take into account users' gaze behavior in an unobtrusive manner and will be able inform researchers and designers about their perceptions of visual aesthetics.

Modeling how different user groups perceive webpage aesthetics

“…5 It is important not to confuse the presentation time of a given stimuli and the needed processing time of the human visual system. Previous works have presented stimuli for only 50 ms or even less [22,44,48,51] and evaluations of these shortly presented stimuli were quite stable, but it is unlikely that the cognitive processing of these stimuli only takes a few milliseconds [34]. With respect to very quickly made aesthetic webpage evaluations, Bolte et al [4] noted that it takes several hundred milliseconds to form first impressions, about 600-800 ms; which is not very far from the 500 ms onset used in the dataset we have analyzed in this paper.…”

What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

“…The second group of applications of eye tracking enhanced with artificial intelligence is in emotion recognition. First of the considered studies focused on predicting which of several emotions was being felt by the participants [23] and the second predicted the aesthetic impression of a website [24]. Others were predicting reactions to advertising [25], predicting perceived face attractiveness [26], recognizing affect [27] and recommending paintings which the participants would like [28].…”

“…Task Additional Parameters [64] attention estimation EEG, head movement [38] identifying children with ASD questionnaire, age, gender [56] predicting dwell time in a museum face expression, body movement, interaction trace logs [27] affect recognition EEG, ECG [8] predicting students' performance and effort EEG, face videos, arousal data from wristband [71] predicting take-over time head position, body posture, simulation data [30] predicting liking a video infrared thermal image, heart rate, face expression [32] predicting user confidence Time [25] predicting reaction to ads gender, age, survey, time, ad parameters, behavior connected with an ad (e.g., sharing) [13] predicting readability text features [17] predicting SAT score Time [36] predicting the emotion of an observed person EEG, empatica bracelet [32] predicting social plane of interaction EEG, accelerometer, audio, video [33] detecting user confusion mouse actions, distance of the user's head from the screen [72] predicting mental workload Reaction time [42] detecting people with dyslexia age, text characteristics [74] predicting reduced driver alertness EEG [19] predicting learning curve perceptual speed, verbal working memory, visual working memory, locus of control [37] classifying emotions in pictures image [89] predicting eye movement distance between the object and the distractor [41] predicting Parkinson symptoms' development age, sex, duration of the disease [23] emotion estimation head movement, body movement, audio, video of the face…”

Attempts to Attract Eyesight in E-Commerce May Have Negative Effects

“…The timing of showing a highlighted product and website aesthetics may also be of importance. Pappas et al [16] studied how quickly users will have formed their first impression of a website. The findings indicated that 10 s of viewing time were enough to be able to accurately capture perceptions on visual aesthetics of a website.…”