11.3 Hot topics: On the value of this monograph

Cannon’s notions and the set of guidelines presented in Chapter 10, illustrate that these notions, although a century old, are still of interest for current ASP. Next, I will take an opposite perspective, not a historical but a state-of-the art perspective, founded on Gross’ top 10 of hot topics on emotion research [236, p. 215].

11.3 Hot topics: On the value of this monograph

In this section, I will reflect on both the contributions of this monograph to emotions research and the lagoons it has left unexplored. Recently, in the journal Emotion Review, James J. Gross summarized his specific top 10 of hot topics on emotion research [236, p. 215]. Gross’ hot topics (indicated in italics) provide an excellent resource for a structured reflection on this monograph.

1.Investigating the antecedents of emotions, moods, and other affective processes.

Detailed analyses on antecedents of affect have been conducted in Chapter 4. The results illustrated that biosignals are indeed sensitive, reliable, and discriminating with respect to affective processes. Chapters 8 and 9 presented two studies that employed storytelling and reliving to elicit emotions in PTSD patients. The results revealed different patterns in affective responses, which can be attributed both to the method of elicitation and to the antecedents present in both studies.

2.Developing new tools for analyzing specific emotion-response components, as well as crosscomponent coherence.

A broad range of techniques and tools have been employed throughout this monograph. Appendix A provides a concise overview of the mathematical background on the statistical and machine learning techniques employed. Chapters 3 and 4 introduced statistical moments, in particular skewness and kurtosis, as new features of biosignals that enable the discrimination between emotions. Chapters 5 and 6 explored the extremely rare combination of speech and the biosignal ECG to assess affective states. It proved to be a powerful combination.

3.Examining bidirectional relations among emotional and cognitive processes ranging from sensation and perception through judgment and decision making to memory.

Throughout this monograph this issue has been mentioned several times but has not been an explicit topic of research. Instead, the studies reported in this monograph aimed to isolate affect, as much as possible, and ignored its interaction with cognitive processes, which are very hard to control in ambulatory real-world practice. This monograph treated this issue as a source of noise that the signal processing + pattern recognition pipeline had to cope with.

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11Discussion

4.Describing the functions of emotion-related processes in everyday life.

This quest begs for ethnography [150], which is hardly employed in affective computing. Chapters 8 and 9 described research conducted on people who suffer from a severe Post Traumatic Stress Disorder (PTSD), which illustrates the impact the disturbance of emotion-related processes on everyday life can have. However, what mechanisms lay at the foundation of mental disorders (e.g., PTSD) remains largely unknown.

5.Assessing patterns of stability and change in emotion and emotion regulation over the lifespan, from childhood to older age.

Such an endeavor requires longitudinal research. This is often conducted in traditional health care. However, in the context of emotion research longitudinal research is rare [416, 417]. This is a typical concern of bringing research from lab to life [674], which needs to be considered when bringing ASP technology into our everyday lives [674]; see also Chapter 4.

6.Examining instructed and spontaneous emotion regulation.

This topic has been addressed in Chapters 8 and 9. The instructions and tasks the participating patients received assured spontaneous bursts of emotion and illustrated the lack of regulation of them. However, as Gross [236] implies, such studies are indeed (too) rare and should be encouraged.

7.Analyzing individual differences in emotion-related processes, with an eye to genetic and epigenetic factors.

Genetic and epigenetic factors have not been a topic of research in this monograph. Individual differences, however, have been taken into account. Chapters 3, 4, and 7 were devoted to baseline-free ASP. Their results suggested the need for individualization. The follow-up studies presented in Chapters 5–6 indeed unveiled individual differences (i.e., environment, the personality trait neuroticism, and gender). Additionally, Section 10.3.3 denoted many more factors of importance.

8.Exploring cultural differences and similarities in emotion-related processes.

Throughout the literature, culture has been shown to be a factor of influence [56, 239, 450, 470], as was also stated in Chapter 10. However, the factor culture has not been a core topic of research in this monograph.

9.Exploring conceptual and empirical relations between emotion and emotion regulation, on the one hand, and psychological health outcomes on the other.

Health and emotion regulation are in constant interaction; consequently, they are impossible to untangle; see also Section 1.6. Therefore, health informatics was identified as one of the disciplines of computer science for which ASP is of the utmost importance. Moreover, this was the reason to conduct the two studies reported in Chapters 8 and 9. These studies unveiled how speech can be used for Computer-Aided Diagnosis (CAD) for mental health care.

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11.4Impressions / expressions: Affective Computing’s I/O

10.Assessing the impact of emotion and emotion regulation processes on physical health outcomes.

In Chapter 1 it was noted that emotions influence our cardiovascular system and, consequently, can even shorten or prolong our life. This monograph did, however, not assess the impact of emotions on physical health.

Taken together, throughout this monograph most of Gross’ 10 hot topics [236, p. 215] have been addressed, at least to some extent. However, for most topics, it is evident that a significant body of follow-up research is required to unravel the topics in more detail. Nevertheless, in sharp contrast to Solomon’s and Russell’s concerns but in line with Gross [236], I would like to say “future’s so bright, I gotta wear shades” [236, p. 212].

Perhaps more than anything else, this assessment of the current monograph illustrated its relevance and provided the necessary additional reflection upon which I conclude with Gross’ words: “This is more than enough work to keep all of us busy who are interested in emotion, so don those sunglasses and let’s get to work!” [236, p. 215]. Next, we will outline how to do this; however, from another perspective, a computer science perspective.

11.4 Impressions / expressions: Affective Computing’s I/O

In the introduction of this monograph, I already stated that at a first glance, computer science and affective sciences seem to be worlds apart; however, emotions and computers have become entangled and, in time, will inevitably embrace each other. Computer science and practice employs input/output (I/O) operations to characterize its processes. This notion can also be fruitfully utilized for affective computing and ASP, as I will illustrate here (cf. [210]).

Table 11.1 shows a matrix that provides a characterization of machinery using, what could be, standard I/O. Machinery without any I/O (i.e., –/–) at all is of no use. In constrast, machinery without either input (i.e., I) or output (i.e., O ) are common practice. However, most of us will assume both input and output (i.e., I/O ), at least to a certain extent, with most of our machinery. For example, take our standard (office) PC with its output (i.e., at least a video (the screen) and audio) and its input (i.e., at least a keyboard and a pointing device). Emerging branches of science and engineering such as AI, AmI, and affective computing, however, aim to redecorate this traditional landscape and provide intuitive I/O handling. In the case of affective computing, what does this imply?

Computer science’s notion of I/O operations can also be utilized to divide affective computing into four categories. In terms of affective computing, the output (O ) denotes the expression of affect (or emotions) and the input (I) denotes the perception, impression, or recognition of affect. This division is adapted from the four cases, as they were identified by Rosalind W. Picard’s in her thought-paper, which presented her initial thinking on affective

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