1. The study investigated the role of expertise and exposure on aesthetic judgements of websites, using both implicit and explicit ratings. 2. The researchers hypothesized that visual elements like symmetry, colorfulness and complexity would influence ratings, and that expertise and exposure would also impact judgements. 3. 59 participants rated 100 website images and had physiological data like heart rate, muscle activity and pupil size recorded as they viewed the sites. Questionnaires also assessed expertise and exposure levels.

1. Neuroaesthetic of websites:
evaluating the impact of expertise and exposure to website
on aesthetic judgment
International Association of Empirical Aesthetics congress
31 Aug. 2018
Giulio Gabrieli, Gianluca Esposito

2. BACKGROUND
☉ Aesthetic experience: psychological state, attentive focus.
☉ “Halo Effect”: are good looking websites more reliable?
☉ Relationship between visual properties and aesthetic judgment.
☉ Users’ expertise or exposure to web pages?
☉ Implicit ratings?
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
RESEARCH QUESTION

3. AIM
Aim of this study is to investigate the role of expertise and
exposure on websites’ aesthetic judgment, both with implicit and
explicit ratings.
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
HYPOTHESIS
1. Visual elements of websites (symmetry, visual complexity,
brightness, number of images and colorfulness) are related to
aesthetic appreciation
2. Expertise and exposure play a role in aesthetic judgment of
web pages

4. PHYSIOLOGY & AESTHETIC
Emotion and physiology
Researchers demonstrated the relationship between individuals’
physiological activity and emotions.
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Physiological measurements:
☉ ECG
☉ EMG
☉ EDA
☉ Pupillometry
Emotions

5. Number of Images Visual Complexity
PHYSIOLOGY & AESTHETIC
Visual properties of web pages
From previous studies, we know that there are several effective
features that can be automatically estimated, including:
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Colorfulness Symmetry Brightness

6. VISUAL COMPLEXITY
Quadratic Tree DecompositionBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Recursive X-Y partitioning
The more the number of leaves, the more complex an image is.

7. VISUAL COMPLEXITY
Quadratic Tree DecompositionBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Recursive X-Y partitioning
The more the number of leaves, the more complex an image is.

8. IMAGES AND TEXT
Space based Decomposition + Optical character recognitionBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Automatic recognition of different elements matched with text
recognition

9. IMAGES AND TEXT
Space based Decomposition + Optical character recognitionBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Automatic recognition of different elements matched with text
recognition

10. METHODS
Participants (N = 59) have been asked to rate 100 images of
websites and 50 emotional pictures (IAPS Dataset), while their
physiological activity was recorded (ECG, EMG, EDA,
Pupillometry).
Expertise and exposure levels have been assessed using a
questionnaire.
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

11. AVI14 DATASET
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

12. EXPLICIT RATINGS
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

13. IMPLICIT RATINGS
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
TRAINING
GENERATION
NEURALNETWORK

14. PIP INSTALL PYSIOLOGY
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
https://github.com/Gabrock94/Pysiology
https://pysiology.readthedocs.io/
● Electromyographic signals
● Electrocardiographic signals
● Electrodermal activity signals
Gabrieli G., Azhari A., Esposito G. (in press), PySiology: a Python Package for Physiological Feature
Extraction, Special issue of Smart Innovation, Systems and Technologies.
Gabrieli G., Azhari A., Esposito G. (2018), PySiology: a Python Package for Physiological Feature
Extraction, Italian Workshop on Neural Networks (WIRN), 13-15 June 2018, Vietri sul Mare, Italy.

15. IMPLICIT RATINGS
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
TRAINING
GENERATION
NEURALNETWORK

16. Website properties paired with ratings
MACHINE LEARNING
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
6 GLM 6 CTree
3 implicit 3 explicit 3 implicit 3 explicit
Expertise and Exposure
☉ None
☉ Expertise
☉ Exposure
Testing
☉ 5-points Likert Sc
☉ Grouped (1-3, 4-5)

17. PRELIMINARY ANALYSIS
After preliminary analysis, 5 websites have been removed because
of statistically significant explicit ratings between males and
females (gender differences).
Two websites presented significantly different ratings by high
exposed and low exposed users, while three websites received
significantly different rating by experts and non experts.
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

18. MODELS’ ACCURACY
A bit of background hereBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Symmetry : Colorfulness : Visual Complexity: brightness: n.° Images
*Avg. accuracy on 1000 tests

19. MODELS’ ACCURACY
A bit of background hereBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Symmetry : Colorfulness : Visual Complexity: brightness: n.° Images
*Avg. accuracy on 1000 tests

20. MODELS’ ACCURACY
A bit of background hereBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Symmetry : Colorfulness : Visual Complexity: brightness: n.° Images
*Avg. accuracy on 1000 tests

21. MODELS’ ACCURACY
A bit of background hereBACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Symmetry : Colorfulness : Visual Complexity: brightness: n.° Images
*Avg. accuracy on 1000 tests

22. CONCLUSION
✅ We have been able to predict average web pages’ aesthetic
from websites' visual elements
❌ We have found some differences due to expertise and
exposure on single pages’ ratings, but the effect is lost in
predictive models
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS FUTURE STUDIES
☉ More differentiate users (age, expertise, exposure)
☉ Different websites, with different visual properties

23. TAKE HOME MESSAGE
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS
Using an implementation of this model before real user testing we
can optimize the design evaluation process, reducing both time
and costs.

24. Thanks
Questions?

25. DISCUSSION - GENDER DIFFERENCES
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

26. DISCUSSION - EXPOSURE DIFFERENCES
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

27. DISCUSSION - EXPERTISE DIFFERENCES
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

28. METHODS
Participants (N = 59) have been asked to rate 100 images of
websites and 50 emotional pictures, while their physiological
activity was recorded (ECG, EMG, EDA, Pupillometry).
Expertise and exposure levels have been assessed using a
questionnaire.
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

29. ESTIMATION OF IMPLICIT RATINGS
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS

30. PIP INSTALL PRETTYWEBSITE
https://github.com/Gabrock94/PrettyWebsite
https://prettywebsite.readthedocs.io
BACKGROUND
AIM & HYPOTHESIS
PHYSIOLOGY &
AESTHETIC
METHODS
RESULTS
CONCLUSIONS