Abstract
Part I of this chapter deals with conceptualizing emotion. By splitting up ‘emotion’ into an evaluative, expressive, behavioural, physiological, mental and phenomenological component, giving examples of emotion research theories and state-of-the-art technical systems, I will bring together an analytical and conceptual with empirical approaches to human-machine interaction to evaluate to what extent it may be logically appropriate to speak of ‘emotional machines’. I will give an overview of the mentioned components and their potential to be implemented into technical systems through functional equivalents. This concerns the technical recognition of emotion components in humans as well as the technical simulation/emulation of emotion components. Part II deals with human-machine relations and argues that human understanding of technical systems is crucial to a well-functioning society. I emphasise the importance of an in-depth human understanding of technical systems and plead for the integration of the voices and work of so-called mechanologists into cultural practices and discourse, from which the entire societal setup in which human-machine interaction takes place will benefit. Throughout the chapter I refer to some aspects of classical debates in psychology and philosophy on the relations of emotion, cognition, and consciousness. Additionally, I draw from psychologist and philosopher Gilbert Simondon’s, as well as from gestalt theorist Kurt Lewin’s writings.
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Notes
- 1.
Just one example: In a contribution to the 1975 American Journal of Psychiatry psychiatrist Richard Ketai tries to help define the terms “emotion”, “feeling”, “affect” and “mood” by comparing actual uses of the terms in psychiatrists’ writings (Ketai, 1975), to which another practicioner, Paul Kesbab, answers in a letter to the editor and receives a response from the author (Ketai, 1976). Ketai more or less argues that words are important, while Kesbab emphazises the importance of empirical work and warns that in their field one tends „to get caught up in semantics, arguing about words and descriptive terminology rather than focusing our attention on scientifically documented facts” (ibid, p. 347).
- 2.
Some basic terminological definitions: Affect is, in psychology, usually an umbrella term subsuming emotions, feelings, and moods, or, at least, some of them, although the term sometimes denotes the more passive part of psychological processes. Emotions are mostly defined as “being caused by an identifiable source, such as an event or seeing emotions in other people […] and directed at a specific object or person” Bartneck et al., (2020, p. 115), potentially including a cognitive element. Emotion is seen as a behaviour guiding and decision-making force, including motivation and intentionality; for an older review of emotion definitions see Kleinginna and Kleinginna (1981). Moods are mostly defined by their duration being longer than that of affective or emotional states, see Beedie et al. (2005). Feelings are not defined very well. Sometimes the word refers to the phenomenologically accessible. In that case, there is, in psychology, a debate on the question if the so called qualia, see Tye (2016), come before, after or simultaneously with bodily responses, see James (1884); Scarantino and Sousa (2016).
- 3.
Aristotle proposed that emotions (pathe) originate in human desires, or appetites; they are closely connected to our decision-making and our actions; and, we can be overwhelmed by them and need to evaluate, on a meta level, if we should indeed follow the impulses pathe give us. This implies a distinction between a more spontaneous and a more willful part of pathe; cf Schmitter (2016b).
- 4.
See Adolphs and Damasio (2001); Goldsmith et al. (2003); Carroll E. Izard (2009); Panksepp (2003); Pizzagalli et al. (2003). On the neuroscientific endeavor of locating the “high road” and the “low road” of stimuli processing see Gelder et al. (2011); Pessoa and Adolphs (2010); Tamietto and Gelder (2010). This problem may be solved; see Lai et al. (2012).
- 5.
For example, Affect Theory, Affective Computing, and Affect Studies all explore affect, but from very different points of view.
- 6.
This is an aspect of what the Zajonc-Lazarus debate mentioned above has been about, namely, the relation between emotion and cognition: Is there a cognitive appraisal of the bear, or, of the fear? Is the fear of the bear affective and the ‘data’ “bear” processed faster than other percepts? Is recognizing the bear structurally the same as evaluating it as dangerous?
- 7.
- 8.
See e.g. Barthélémy (2013).
- 9.
„La vie n’est pas une substance distincte de la matière; elle suppose des processus d’intégration et de différenciation qui ne peuvent en aucune manière être donnés par autre chose que des structures physiques.“ Simondon (2005, p. 162).
- 10.
Technological physiological signal recognition and interpretation of human physiology is, of course, already happening, too, see e.g. Shu et al. (2018).
- 11.
For the study of unintended incentives, see specification gaming dealing with “a [system’s, JB] behaviour that satisfies the literal specification of an objective without achieving the intended outcome” (Kraknova et al., 2020).
- 12.
Although they themselves might be based on principles.
- 13.
For example: if a mother threatens her child with the policeman, it is not the actual social and legal situation having an effect on the child, but the child’s belief in a quasi-social fact.This is echoed in later work on the social construction of reality by Alfred Schütz (1972), Peter Berger and Thomas Luckmann (1966). Schütz considers his approach to be very close to gestalt theoretical writings in that both are a kind of phenomenological psychology and oppose the psychology of association, see Schütz (1990, pp. 109, 116).
- 14.
Furthermore, Lewin distinguishes between an analysis of dynamical and of historical causes for a psychological event, of which, for his purposes the dynamical one is the important one. The event can be either systematically “traced back to the dynamic characteristics of the momentary situation”, in which the “‘cause’ of the event consists in the properties of the momentary life space or of certain integral parts of it” (ibid., p. 30); or, it can be explained historically by how it has come into being (Lewin, 1936, p. 30 f.).
- 15.
One could speculate or try to psychoanalyze why some scholars believe in a strong artificial intelligence and why some do not. There are usually hints in their writings revealing certain images of what is essential to being a human, and, on what they feel could be threatened or could be won by the possibility of conscious machines.
- 16.
They really only become ‘signs’ by our interpreting them, cf. Bellon et al. 2023.
- 17.
A contemporary mechanologist would be trained in cybernetics and information theory; they would have hands-on familiarity with, and in-depth encyclopaedic knowledge of technical objects, exceeding the knowledge or mode of access of an operator or owner of a technical object (Simondon, 2017, p. 160). Any spokesperson for technics with this level of skill could be called a psychologist or sociologist of machines (see ibid.). They are needed, because, if technical objects do not have appropriate representations in a culture or if technical objects are integrated into cultural practices without mechanologists’ mediation, society misses out on a complete representation of reality, resulting in an inability to understand processes that shape, amongst other things, the mode of human existence. Mechanologists with their encyclopaedic knowledge therefore contribute to a form of humanism, as “every encyclopaedism is a humanism, if by humanism one means the will to return the status of freedom to what has been alienated in man, so that nothing human should be foreign to man […] this rediscovery can take place in different ways, and each age recreates a humanism that is to a certain extent always appropriate to its circumstances, because it takes aim at the most severe aspect of alienation that a civilization contains or produces.” (Simondon, 2012, p. 117 f.) To our age, characterized by a paradigm of information, the alienation we need to fight is one that is produced by information processing procedures. An up-to-date example are neural networks able to produce deepfakes (Mirsky & Lee, 2020) which, if the technology is not really understood by most human participants of a society, has the potential to enormously alienate humans from reality.
- 18.
GAN inventors Goodfellow et al. also state that image classifying neural networks are not actually „learning the true underlying concepts that determine the correct output label. Instead, these algorithms have built a Potemkin village that works well on naturally occurring data, but is exposed as fake when one visits points in space that do not have high probability in the data distribution.“ (Goodfellow, Shlens et al., 2014, p. 2)
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Bellon, J. (2023). Emotion Components and Understanding in Humans and Machines. In: Misselhorn, C., Poljanšek, T., Störzinger, T., Klein, M. (eds) Emotional Machines. Technikzukünfte, Wissenschaft und Gesellschaft / Futures of Technology, Science and Society. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-37641-3_2
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