**4. Results**

One of the objectives of this paper is to examine the variation of the colour objects as preferred by users based on visual aesthetics quality, which could be either high or low on these colour objects and also user interface influences perceived colour objects usability. The perception of user data was first inspected for normality based on the equation bellow (Eq. (2)), which is a novel approach, considering the younger participants involved. The error plot did not show any extreme outliers or skewness. For the inverted bell-shaped lines, though slight skewed for red, confirmed that the data were normally distributed. During the preliminary analysis, it was observed that the variability or changes in usability apparent for the shape colour visual aesthetics was higher and apparent than the low or less shape colour objects. The number of participants recruited was not large enough, to some of the user data was simulated to obtain the same user generated data as the original. So the total amount of user data obtained amounts to 60 including that for children. The criteria for detecting and determining the kind of colour object most user preferred by users is based on 'Very likely', 'Not likely' and 'Absolutely not'. Colour objects was utilised because this would attract younger participants and make the result more methodical and generalised.

Based on the computed results above, the error rate for users' visual aesthetics is computed using the equation bellow for both emotion reaction and reasons why users prefer a particular colour object. The reasons why this occur is determined by their colour preferences, which is similar to attributing the colour objects to the

once they clicked on to observed. This particular study is more interesting to children, since their psychological reasoning is heightened by the colours and their basic love of colour is exploited. Most of the children preferred rather shape colours like red and Orange while adults prefer frail colours like cyan and blue. The divergence in error is only found in red colour objects because women and few girls tend to go for this type of colours and sometimes some red colour tend to feel less appealing to the eye due to the shade in it (high or low). Some users were mostly confused about their chooses or preferences when it comes to red colour objects. The reason is because the red colour objects rather appear either in light or heavy to the eye when observed by the user. The rest of the colours were measured in the same criteria as the mood of preferences to the user, that is, 'Very likely', 'Not likely', and 'Absolutely not'since they appear to be in-line with the entire colour objects based on the error rate (**Figure 5**).

In regards to the findings, most webpages and games interface can be tuned to these colours as this would likely attract more users and also children would be more drawn to assess such interfaces with average visual console in appearance.

$$U\_{pre} = \frac{No - of - User}{100}(1000) + U\_{pre} \tag{1}$$

and error is calculated based on:

$$error\_{rate} = U\_{pre}^{\;\;\;1000\;\;}\tag{2}$$

**5. Conclusions**

**Figure 6.**

**9**

*a*

**Table 1.**

projects based on visual colour objects project.

*The Rate of User Color Preferences based on User Perception.*

The findings in the paper presented, demonstrated that it is possible to compare and deploy groups of colour object properties, which influences high and low regard of colour objects by users. In addition, properties that are associated with statistical presentation had an impact on the perception of usability of colour object properties related to the appearance of the object as determined by users' perceived visual aesthetics. So, it is possible to resolve the issue of attribute overlay and also to establish that high and low colour objects quality perceptions occur autonomously. The paper also showed the relevance of perceived visual aesthetics for emotional user reaction and consequences of the user experience to the object they viewed. The relationship of perceived visual aesthetic and emotional aspects of users have also been studied extensively. Further challenges regarding visual aesthetics in human technology interaction that should be addressed in the future are the role of inter-individual modifications of aesthetics judgements that seem more important, as for example in comparison to the perception of usability issues regarding object colour and the consideration of visual aesthetics in interactive system design

**Colour object Very likely (%)a Not likely <sup>b</sup> (%) Absolutely not (%)c** Orange 413,450 321,210 4,154,323 Cyan 413,460 391,210 409,324 Red 3,953,430 243,230 406,231 Green 3,965,641 391,210 4,154,323 Yellow 41,345,023 321,210 406,231 Blue 413,451,022 243,230 409,324

*'Very likely' denotes how user prefers the object based on its colour. <sup>b</sup> 'Not likely' denotes how user dislike an object based on its colour. <sup>c</sup>*

*The percentage likes and dislike of colour object as preferred by users.*

*Towards an Optimisation of Visual Aesthetics for User Interaction*

*DOI: http://dx.doi.org/10.5772/intechopen.89713*

*'Absolute not' denotes how user totally and not likely to choose an object based on its colour.*

The table (**Table 1**) shows the percentage of like and dislike of each colour objects observed by participants based on Eq. (2). This is to reduce the error in computations and predisposition to error in data due the novel approach adopted. The result also showed the variations of usability as well as aesthetics and the predicted impact on the perception of all colour objects (**Figure 6**). Objects with colours associated with a high degree of usability and attractiveness received better ratings than others. The result also showed that emotions of users as regard to the colour objects, revealed that the effect of usability was greater than the one of visual aesthetics for all the valence and emotional or biological feelings involved. Therefore, the colour objects of high regard and appealing design was experienced as most satisfying, while the colour objects of low regard and least attractiveness was most exasperating for children. Both factors contributed to these emotions additively.

**Figure 5.**

*Graph indicating error in users' emotional rating and colour preferences of each objects they viewed. (a) Error rate based on users' emotional experience, and (b) Error rate based on users' colour preferences.*

## *Towards an Optimisation of Visual Aesthetics for User Interaction DOI: http://dx.doi.org/10.5772/intechopen.89713*


*a 'Very likely' denotes how user prefers the object based on its colour. <sup>b</sup>*

*'Not likely' denotes how user dislike an object based on its colour. <sup>c</sup>*

*'Absolute not' denotes how user totally and not likely to choose an object based on its colour.*

#### **Table 1.**

once they clicked on to observed. This particular study is more interesting to children, since their psychological reasoning is heightened by the colours and their basic love of colour is exploited. Most of the children preferred rather shape colours like red and Orange while adults prefer frail colours like cyan and blue. The divergence in error is only found in red colour objects because women and few girls tend to go for this type of colours and sometimes some red colour tend to feel less appealing to the eye due to the shade in it (high or low). Some users were mostly confused about their chooses or preferences when it comes to red colour objects. The reason is because the red colour objects rather appear either in light or heavy to the eye when observed by the user. The rest of the colours were measured in the same criteria as the mood of preferences to the user, that is, 'Very likely', 'Not likely', and 'Absolutely not'since they appear to be in-line with the entire colour

In regards to the findings, most webpages and games interface can be tuned to these colours as this would likely attract more users and also children would be more drawn to assess such interfaces with average visual console in appearance.

*errorrate* ¼ *Upre*

The table (**Table 1**) shows the percentage of like and dislike of each colour objects observed by participants based on Eq. (2). This is to reduce the error in computations and predisposition to error in data due the novel approach adopted. The result also showed the variations of usability as well as aesthetics and the predicted impact on the perception of all colour objects (**Figure 6**). Objects with colours associated with a high degree of usability and attractiveness received better ratings than others. The result also showed that emotions of users as regard to the colour objects, revealed that the effect of usability was greater than the one of visual aesthetics for all the valence and emotional or biological feelings involved. Therefore, the colour objects of high regard and appealing design was experienced as most satisfying, while the colour objects of low regard and least attractiveness was

most exasperating for children. Both factors contributed to these emotions

*Graph indicating error in users' emotional rating and colour preferences of each objects they viewed. (a) Error*

*rate based on users' emotional experience, and (b) Error rate based on users' colour preferences.*

<sup>100</sup> ð Þþ <sup>1000</sup> *Upre* (1)

<sup>1000</sup> (2)

*Upre* <sup>¼</sup> *No* � *of* � *User*

objects based on the error rate (**Figure 5**).

*Human 4.0 - From Biology to Cybernetic*

and error is calculated based on:

additively.

**Figure 5.**

**8**

*The percentage likes and dislike of colour object as preferred by users.*

**Figure 6.**

*The Rate of User Color Preferences based on User Perception.*
