Algoritmo adaptativo

Two main conclusions can be made so far: first, VR can be a useful medium for cognitive training due to the positive effects of immersion in subjects’ cognitive skills, and second, arousal and valence can have significant effects in cognition. In light of these conclusions, this section discusses the findings of Study 3 re- garding the adaptation engine, as well as the implications about why and how affective states can be used to adapt game-based cognitive training programs.

Video games have often been used to motivate subjects in low engagement educational or cognitive tasks [125, 160]. Since challenge is one of the most important aspects of video games [44], many researchers have suggested the im-

difficulty levels directly influence the players’ affective states and consequently, their cognitive performance. When challenges and skills are balanced, players enter in a flow state [45] (see Fig. 2.4), also called optimal experience, that can help them to achieve a better cognitive performance [8]. In line with these findings, this research has demonstrated that players have a better WM per- formance when they are in an optimal affective state, feeling successful and overcoming difficult challenges.

Due to the effects of arousal and valence on cognitive skills, it is important to include affective state metrics in the adaptation engine in order to provide more effective cognitive training. As suggested by Mishra et al [98], closed- loop video games should include physiological and behavioural cues to achieve a more personalised cognitive training. This is especially relevant for game-based cognitive training since video games often involve emotional processes [24] and the pace of progression strongly depends on the player’s skills. The adaptation engine implemented in Memory Break used affect and performance metrics for adaptation and decision making. Although the affect recognition system did not perform well, making wrong adaptation decisions, the performance-based deci- sion layer corrected some of these erroneous decisions. These findings are in line with the research by Bontchev and Vassileva [25] who showed that adaptation accuracy can significantly improve when combining performance and affective metrics. Even though their research focused on computer games for educational purposes, their findings are reasonable as the adaptation engine would have a better picture of the player’s responses. Whilst the performance decision layer keeps track of the player’s progression, the affect decision layer appraises the player’s affective responses, which can provides insights about how the player reacts to new challenges. This is important in gaming experiences since players can perceive challenges differently depending on their motivation [75, 58]. Since flow strongly depends on motivation and affective states [24], only performance- based adaptation is not enough to provide a tailored WM training that sustain players in an optimal affective state. This is supported by Lazarus’ cognitive- motivational theory [83], which states that the subjects’ motivation and goals in a situation must be known to understand their reactions.

The adaptive version of Memory Break in Study 3 had positive effects in par- ticipants’ WM performance. However, only participants with low WM playing the adaptive version significantly improved their WM performance within Mem-

ory Break as well as their AOST score. These findings indicate that difficulty

adaptation can be more beneficial for subjects with low WM capacity.

The design and implementation of the adaptation engine presented some challenges and limitations worth discussing. The most important limitation was the poor performance of the affect recognition system, which had a clas-

sification accuracy below the chance level of 50%. This could be explained by the novelty effect [123] and lower levels of self-reported arousal and valence of participants in Study 3 compared to those in Study 2. Another reason is the subject-independent approach used in the affect recognition system, which caused great accuracy variability between subjects. According to Parsons and Reinebold [119], subject-independent methods are harder to create than subject- dependent methods as they have to work for all subjects. Although subject- dependent methods are more reliable because they are exclusively created for each subject, they are also more expensive as they have to be calibrated for each subject. Due to the intensification of affective states in VR, developing automatic affect recognition systems in this medium can add some difficulty to this complex problem. Since VR is a relatively new medium, there can be important novelty effects on some participants.

The misclassification of arousal and valence involved an important challenge about how to deal with them. The buffers created to keep a time series of the previous predictions smoothed the impact of these misclassifications. Adap- tation decisions were only taken if the engine was certain about the detected affective state (buffer mean >.05). Afergan et al [3] took a similar approach, averaging the previous 8 classification predictions to calculate a confidence value to make adaptation decisions. Since arousal and valence are continuous dimen- sions [138], it is important to keep track of how subjects’ emotions progress in time. In addition, the creation of static thresholds is not a good solution to divide data for classification that is likely to have a great variability between subjects. The static threshold set up in Study 2 to divide arousal and valence into 2 classes for classification were problematic since not all subjects in studies 2 and 3 used the same range of values with the Affective Slider. This could be solved normalising participants’ self-reported affective states.

Finally, the performance-based decision layer could be substantially im- proved, adding more meaningful features such as reaction times or errors made. The static threshold to differentiate good and bad scores was again problematic due to high variability between subjects in skills and expectations on what a good performance is. Montani et al [103] suggested a continuous calibration of the game’s difficulty level depending on the player’s current performance to ac- count for performance variability. Therefore, it is important to account for the variance of subjects’ self-reported variables, preferences and motivations. Fur- ther research needs to be done to address this important problem in affective computing research.

7.2

Contributions

The research presented in this PhD thesis revolves around three elements or variables: affective states, WM and VR. Previous research has demonstrated that adaptation in games for cognitive training can lead to cognitive improve- ments. This research explored the employment of HMD-based VR video games for WM training as well as the effects of immersion and affective states on WM performance. An adaptation engine was designed, implemented and tested to dynamically adjust the difficulty of the game according to the player’s affective states and performance. The results demonstrated that affect and performance- based adaptation in VR games can have positive effects on the player’s WM performance

This research contributes to the areas of affective gaming and game-based cognitive training in five ways. First, high levels of immersion often re- ported in VR have a positive impact on player’s WM performance

and intensify their affective states. Second, positive affective states

help subjects to achieve a better WM performance, although arousal

can have detrimental effects if it is too high. Third, a new methodology for

affect recognition in gesture-based VR gamingusing physiological and be-

havioural signals. This methodology proposed and demonstrated the feasibility of employing the HMD to measure the player’s affective states. Fourth, a novel

adaptation engine composed by affect and performance metricsto dy-

namically adjust the difficulty of the game. This adaptation engine aims to keep the player in an optimal affective state to improve the WM performance. Last, difficulty adaptation can have positive effects on WM performance,

being particularly pronounced on subjects with low WM capacity.

To the best of the researcher’s knowledge, this is the first time that affect and performance metrics have been used for the adaptation of a game-based WM training program in VR.

The contributions of this PhD have demonstrated that VR is an effective medium for cognitive training as it improves subject’s WM performance due to the high levels of immersion and engagement experienced. The findings reported also suggest that affect and performance metrics should be used to adapt the game in real-time to provide a tailored adaptation. Due to the effects of arousal and valence on subject’s cognition, this research proposes the incorporation affective state metrics in the adaptive engine of a game-based cognitive training programs to improve its effectiveness.

Finally, this thesis suggests further research to investigate the effects of adap- tive games in VR for cognitive training on subject’s WM. Since adaptive games are more beneficial for subjects with low WM capacity, as shown in this thesis,

further research should focus on subjects diagnosed with Dyslexia or ADHD as they suffer of low WM capacity [78].

7.3

Limitations

This section discusses some of the limitations of the research reported in this thesis. Suggestions are provided to address these limitations in future works.