Chapter 8
Auditory attention, implication for game
design
Auditory attention also plays an important role in serious games. In this chapter, we utilize the results of text response analysis from Chapter 3 and propose the indications for serious game design. We propose a 3-dimensional difficulty level model for auditory tasks, that can be used to design game levels and adjust the difficulty of games dynamically to keep a player in the flow. The proposed model suggests, the prime factor to control the difficulty of a game is the background noise level. The secondary factor is the length of the auditory message, followed by a third factor, semantic complexity. We propose the concept of mini-game format specifically aimed at training auditory attention and performance. As a format, it could be easily customized to different domains and implemented as a service or a game engine plug-in.
8.1 Introduction and related work
Attention is one of the primary factors affecting the performance of any tasks, and it has been considered to play a vital role in learning [128]. Among other kinds of attention neuroscientists and cognitive psychologists have taken interest in selective attention for a long time to understand how the brain selects one stimulus over others. As discussed in Chapter 2, the popular setting for auditory attention experiment is the dichotic listening task by simulating multi-message environment [12]. We designed an experiment and conducted with 25 healthy participants, and quantified auditory attention level by counting the correctly identified words in transcribed text responses (detail explained in Chapter 2).
98 Auditory attention, implication for game design In the serious game, different skills have been explored for different areas [129, 130]. Games improving motor skills are used for physical training [131, 132], cognitive skills are used in education [133–135], social and psychological skills are used in the treatment of patients to improve their physical and mental health [136–138]. Attention has also been the key interest in the serious game community. Serious games primarily investigated attention to help children and adults with Attention Deficit Hyperactivity Disorder (ADHD) [139]. Since then games have been considered as an alternative treatment of ADHD [140–143] to extend the attention span [144] and improve the attention [145]. Other technologies have also combined with serious games to improve the attention of children with ADHD e.g. Augmented Reality [146]. The attention of children with a movement disorder (cerebral palsy) was assessed with serious game incorporated with EEG. Games have also been designed to improve the attention of ordinary people. The attention development was studied specifically for children [147] and older adults [148]. A study was conducted to improve the attentional behavior of the school students with a computerized system [149]. A mobile game TrainBrain was also designed to improve the attention [150]. Many aspects of attention were studied with the games namely visual selective attention [151–154], knowledge acquisition and retention [155] and limited capacity model of attention [156]. A computer-based cognitive therapy software is designed, named as RehaCom, which is used by the expert to help to improve various aspects of attention and memory [157].
8.2 Results from text response analysis
The results from Chapter 3 shows how attention score is strongly depended on the background noise level, length, and semanticity of stimulus. The effects of all the three independent variables are apparent in Figure 8.1, reproduced from Chapter 3. Specifically, attention score increases with an increase in SNR and decreases with an increase in the length of the stimulus. The attention score also reduces for non-semantic auditory messages. Interestingly, the effect of semanticity vanishes as noise level increases and vise-verse, as shown in Figure 3.5a. The attention score of semantic and non-semantic messages are the same in a very noisy environment. Non-semanticity makes listener distracted and listener loses the auditory attention. Similarly, increasing noise level (or decreasing SNR) makes it harder to pay attention, while shorter sentences are easily acquired by listeners. The figure also highlights that noise has a strong impact on attention score, however Repeated measure ANOVA (see Table 8.1) reveals that not only all the three independent variables together are significantly (p << 0.0001) affecting the attention score, but also individually. The value ofη2
partial for
8.3 Discussion and indications for serious game design 99 Table 8.1 Results of Repeated measure ANOVA
Variable(s) F-ratio p − value η2partial
Noise level (SNR) F(5,120) = 238.500 ≈ 10−16 0.909
Length of stimulus F(2,48) = 95.9220 ≈ 10−16 0.800 Semanticity of stimulus F(1,24) = 115.611 ≈ 10−10 0.828 Noise*Length*Semanticity F(35,840) = 72.771 ≈ 10−16 0.752
the noise as an only factor, while 75% of the differences are due to all three factors in combination. Figure 8.2 shows the attention score of all the participants in two extreme conditions of noise e.g. -6 dB and∞ dB SNR. Participants are sorted as per their ranks in the noiseless (∞ dB) environment. It is apparent that, even though there is a common pattern of performance, each participant has a slightly different variability in performance. The individual differences of each participant can be explained by their language skills.
Fig. 8.1 Auditory attention score verses Semanticity, Noise level and Length of stimulus.
8.3 Discussion and indications for serious game design
The results presented above are useful for applications of serious games in learning languages. However, those results may be applied to other training/instruction context, and not only because of the specific jargon typical of some domains (which represents a barrier, especially for novices). With the wide diffusion of 3D environments and natural interaction modalities, auditory attention has gained ever more relevance also in the world of serious games, as -imitating reality - a lot of information relevant to training/instruction can be conveyed by means of speech. Non-player characters (NPCs) are key mechanics in this regard [158]. The
100 Auditory attention, implication for game design
Fig. 8.2 Attention score of all the participants in two extreme conditions of noise. experimental results suggest the noise level, semanticity, and length of message are the key factors for a player’s auditory performance. These findings can be exploited in designing game levels, quantitatively and qualitatively. As Figure 8.2 shows, each participant has different performance, under the same condition, which is account for the individual’s skill sets. The results also can be used to adapt the game environment dynamically to match the player’s skills sets in order to keep him in flow [159, 160]. The difficulty of the different levels - and player performance alike - can be estimated through the quantitative information provided by the experiment.
Fig. 8.3 Difficulty level model for game design
A 3-dimensional difficulty level model can be designed for games, with the noise level, length of messages and semantic complexity of message at each dimension, as shown in Figure 8.3. At every level of noise, a player can be subjected to longer messages and semantically more difficult messages to increase the difficulty of the game. The noise level axis of the model can be treated as a primary axis of difficulty, as statistical analysis demonstrated noise as the strongest factor affecting auditory attention. The length of the
8.3 Discussion and indications for serious game design 101 message can be considered as a secondary axis to increase the difficulty of the game, as to increase the length of a message is quantitatively easier than increasing the semantic complexity of a message, which can be regarded as the last axis of difficulty.
The same concept can be used for the sake of game adaptability, as the user performance might be continuously profiled along the three axes to get reference values to assess his auditory attention level. This construct could be an enhancement to the learning/gaming analytics profilers, that are spreading also in the world of serious games [161–163]. The developed knowledge might be applied also to serious games specifically aimed at training auditory attention. This might be done by featuring player interaction with NPCs in the plot, or by developing a set of generic minigames [164], each one targeting a specific factor. We could think of a very simple format, with a customized NPC and environment/background. The target of the player, in a conversation, could be to catch the meaning or identify one or more wrong/specific words. The difficulty of the trial could increase in terms of environment noise (e.g., environmental noise and/or overlapping voices by specific "competing" NPCs), length and meaning of NPC messages. This could be implemented as a service, in a service-oriented architecture perspective (e.g., [165]), or even a game engine plug-in [166], and supported through an authoring tool for efficient development.
For game design, considering a whole scale of semantic complexity has advantages over considering only two distinct categories of semanticity (i.e., semantic/non-semantic information). Semantic complexity of a message can be treated as a complex grammatical structure of language (English as an example), which serves the purpose of the game to improve the attention of player on language. In this scenario, the player will learn to process complexly structured messages. On the other hand, if just two distinct categories of semantic and non-semantic are used, learners will practice paying attention to non-semantic sentences, which is not as useful as to learn complex grammatical structures of a language. Moreover, the game designer will have two levels of difficulty in the dimension of semanticity unlike the whole scale of semantic complexity, which can have more levels of difficulties.
The proposed 3-dimensional difficulty level model can be applied both for designing game levels and adaptivity to keep a player in the flow. Application of the model is expected to improve the auditory attention of the player, especially, but not exclusively, for learning a language. For example, international students can be targeted to improve auditory attention with the English language. A similar model can be used to design a game with the Italian language for non-Italian students to improve their auditory attention on the Italian language. Other potential applications of the proposed model could be in designing of SG for Audi-tory Processing Disorder (APD) patients (adults and children) to improve the attention on auditory stimulus. Children, an early age, can be trained with customized games to improve
102 Auditory attention, implication for game design their attention to different languages. Similar games can be designed to complement the treatment of ADHD for auditory aspects. We also propose the concept of a mini-game format specifically aimed at training auditory attention and performance. As a format, it could be easily customisable in different context domains and implemented as a service or a game engine plug-in.
Chapter 9
Conclusions and future scope
In this chapter, we conclude our work and discuss the future perspectives of the research. As major contributions of this thesis are the experimental design, artifact removal algorithm, analysis of text and physiological responses, and collection of the database and its release in public domain. We discuss these contributions in following section.
9.1 Conclusions of the thesis
9.1.1 Experimental design and predictive tasks
The experiment designed for auditory attention on natural speech is a key to entire work. The design of the experiment was inspired by a widely used experimental setting in cog-nitive psychology. Since the objective of the dichotic listening task is to investigate the behaviour response of auditory attention and its mechanism, it is very limited to theoretical understanding. However, the theoretical bases established from dichotic listening task are used to design our experiment. Unlike two stimuli presented to a participant, we present a single auditory stimulus under different auditory conditions, which is a more general situation that we face each day. Our design of experiment includes three commonly used auditory conditions, namely; background noise level, length, and semanticity of the auditory stimulus. We generated non-semantic speech stimuli by inserting isolated words in-between in semantic sentences. This approach was adopted to mimic real-world scenario, usually faced by non-native speakers as a consequence of limited vocabulary.
We vary these auditory conditions and generate 36 different experimental conditions. We record, three physiological responses namely; EEG, GSR, and PPG. The attention score computed by counting the number of correctly identified words in transcribed text. All the physiological responses are properly labeled with auditory conditions corresponding
104 Conclusions and future scope attention score. We formulate four predictive tasks that can be carried out from the collected database. The predictive tasks are namely; attention score, semanticity, noise level, and LWR prediction. The attention score prediction from physiological responses is to estimate the level of auditory attention being paid from brain activities. Similarly, noise level and semanticity predictions are to estimate the level of noise and semanticity experienced by a person. LWR prediction is to identify, whether a person is listening, writing or resting. We release the collected dataset in the public domain, which can be used to analyse the collected signals for different auditory processing mechanism and to design a BCI system for auditory attention.
9.1.2 Text response analysis
The text response analysis shows the strong dependency of attention score on auditory conditions. ANOVA reveals the significant effects of all three independent variables (in combination and individually) on attention score. Interestingly, the construct of semanticity used, while designing the experiment to generate non-semantic stimuli shows the significant (p << 0.001) effect too. This suggests that use of unusual words (not familiar to listeners) can impact the attention of listeners on the message drastically.
With hierarchical clustering applied on p-values of a pair-wise t-test, we identified groups of experimental conditions that have a similar impact on the auditory attention. These groups help to understand, how much auditory conditions is to be changed in order to improve the auditory attention of non-native speakers.
While the general impact of auditory conditions was observed, we analysed the individual differences of the participants. We observed each individual was affected by auditory conditions differently. One with the highest average score, performed well in almost all the experimental conditions, whereas another with lowest had poor performance in all the conditions. The analysis of individual differences highlighted the importance of an individual’s language skills.
9.1.3 Artifact removal algorithm
The EEG signals recorded from the human brain are often contaminated by artifacts. The state-of-the-art algorithms based on ICA, identify the artifactual components from decomposed independent components and recover the corrected signal by removing it. However, these algorithms either need an expert to manually identify the artifactual components or need references signals carrying potential information about the artifact. Very few algorithms based on ICA use heuristics of statistical measure to pick the artifact automatically. While
9.1 Conclusions of the thesis 105 applying these algorithms, it can not be certain whether the removed component had any useful information. We proposed an algorithm based on WPD with two tuning parameters and three operating modes, which allows controlling the suppression and removal of the presumed artifact. The performance of the proposed algorithm was compared with an ICA-based algorithm. The proposed algorithm outperformed the ICA-ICA-based algorithm in four predictive tasks, formulated from our experiment. We also demonstrated that the performance of the proposed algorithm can be further improved by properly tuning the parameters.
9.1.4 Signal analysis
The collected EEG, GSR and PPG signals were analysed against the attention score, noise level, semanticity, and state of the task (LWR). The power spectrum distribution (PSD) of each electrode shows that some participants have high alpha activities while listening compare to writing and resting. However, no significant difference in PSD was observed for semanticity or noise level for an individual participant. For quantitative analysis, correlation of absolute power in six frequency bands with attention score, semanticity, noise level, and LWR was computed and shown as a topographic map of the brain. The correlation analysis complies with the findings of the literature. The power in alpha bands has a negative correlation with attention score, noise level and writing, and positive correlation with listening. Apart from the findings of literature, the positive correlation of delta band and negative correlation of low gamma band in frontal lobe with attention score was observed.
The Event Related Potential (ERP) analysis of the EEG signal was found interesting. Since ERP analysis is conducted for each individual (similar for spectrum analysis), almost all participants show the P3 component for listening, dominating in the frontal lobe. A similar analysis was observed for noisy and non-semantic stimulus. As the analysis of ERPs with latency and amount of deviation for each event needs an intensive study, this has been reserved for future work.
9.1.5 Release of database in public domain
Widely available datasets for physiological responses are for either for diseases, mental disorders or for the imaginary motor movement and emotion recognition. There is a lack of dataset of auditory attention on natural speech. The physiological responses collected for the auditory attention on natural speech, is a unique dataset. We release the dataset in public domain for the scientific use. We also release the results and codes to reproduce the results of predictive modeling carried out in the thesis, including deeplearning approach discussed in Chapter 7.
106 Conclusions and future scope
9.2 Future scope of the work
The work presented in this thesis can be extended in many directions. The following subsections are the few suggestive directions.
Experiment paradigm
The methodology used to design and conduct the experiment for auditory attention on natural speech was solely based on non-native English speakers. A similar experiment can be designed for native speakers of any language. In the experiment, the construct of non-semanticity was a use of isolated words in-between a semantic sentence, which shows the desired effects. However, it can be extended to semantic complexity. The semantic complexity could use the more complex structured sentences instead for non-semantic. Use of dataset
The primary objective to release the dataset in the public domain is to encourage researchers to improve on the predictive tasks with advanced machine learning and deep learning approaches. The dataset could also be used to analyse the physiological responses to various auditory events. An intensive study of ERP could reveal some of the surprising findings of the brain responses to auditory processing. Furthermore, the data can be used to design a novel BCI system, which works with auditory attention level.
Artifact removal algorithm
The proposed artifact removal algorithm is based on WPD. The design of the algorithm aims to suppress the artifacts in general. Future work can focus to design a specific wavelet to identify and remove the particular kind of artifact (e.g. eye blinking, ocular or muscular artifact). By designing such wavelet, it can be Incorporated with our algorithm with tuning parameters.
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Appendix A
Participant-wise results
Spectral aanlysis
Power Spectral Density of each electrode of All the participants for Listening, Writing and Resting, arranged in 10-20 system are shown here
122 Participant-wise results
S3 S4
S5 S6
123
S9 S11
S12 S13
124 Participant-wise results
S16 S17
S18 S19
125
S22 S23
S24 S25
Appendix B
Publications
Published
1. Bajaj N., Bellotti F., Berta R., De Gloria A. (2016), A Neuroscience Based Approach to Game Based Learning Design. In: Bottino R., Jeuring J., Veltkamp R. (eds) Games and Learning Alliance. GALA 2016. Lecture Notes in Computer Science, vol 10056. Springer, Cham
2. Bajaj N., Bellotti F., Carrion, J.R., Berta R., De Gloria A. (2018, December), Auditory Attention, Implications for Serious Game Design. In International Conference on Games and Learning Alliance (pp. 201-209). Springer, Cham.
Submitted to journals
1. Nikesh Bajaj, Francesco Bellotti , Jesus Requena Carrion , Riccardo Berta and Alessan-dro De Gloria, Analysis of factors affecting the auditory attention of non-native speak-ers in learning environments, Journal: Interactive Learning Environments
2. Nikesh Bajaj, Jesus Requena Carrion, Francesco Bellotti, Riccardo Berta and Alessan-dro De Gloria, Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks. Journal of Biomedical Signal Processing and Control
3. Nikesh Bajaj, Francesco Bellotti , Jesus Requena Carrion , Riccardo Berta and Alessan-dro De Gloria„ A database of physiological signals for analysing auditory processing of natural speech. IEEE Ttransactions on Neural Systems and Rehabilitation Engineering.
128 Publications
In preparation
1. Nikesh Bajaj, Francesco Bellotti , Jesus Requena Carrion , Riccardo Berta and Alessan-dro De Gloria„ Predictive of auditory tasks with CNN and LSTM.
