MANIFESTACIONES CLÍNICAS

With respect to the conceptualisation of different kinds of emotions, two dominant approaches have emerged: discrete and dimensional accounts. The former posits the existence of distinct, separable emotional states (i.e. states that are nameable and therefore may be readily labelled when experienced (Adolphs, 2002)). These states are considered to be distinct in terms of both their experiential aspect, and their origin. For example, a person receives some piece of good news, is asked about their emotional state at that moment, and may declare ‘I am happy’. In particular, some proponents of the discrete approach have advocated a small number of ‘ba-sic’ emotional states (Ekman & Friesen, 1971), biologically constituted and therefore pancultural, that have distinctive and innate adaptive functions that are conducive to survival (Tomkins, 1962). In other words, in response to a vast range of different environmental and/ or interpersonal scenarios, a set of qualitatively separate emo-tional states have arisen as adaptive ‘mechanisms’ that offer (or at least once offered) some type of immediate benefit relevant for survival. More recently, proponents of the discrete approach to emotion have conceded that emotions are context-dependent and highly variable across individuals, but emphasised that they are nonetheless, by nature, strategic, embodied responses to external stimuli, best describable by discrete emotion labels because of their functional coherence (Eickers et al., 2017).

This argument has received support from functional localisation studies in cognitive

neuroscience, which have demonstrated some level of specialisation in different brain areas for discrete emotions (Barrett, Gendron, & Huang, 2009). For example, the ex-perience of fear tends to be particularly associated with increased blood-oxygen-level dependent (BOLD) activity centred around the amygdala (LeDoux, 2003), while sim-ilar research has demonstrated selective processing of disgust at the insula (Wright, He, Shapira, Goodman, & Liu, 2004). However, contradictory findings suggest that the specificity of particular neural structures for individual emotional states may have been overemphasised, and that discrete emotional states may arise from a shared, distributed network (Schienle et al., 2002).

Furthermore, discrete accounts of emotion, have been criticised for failing to ade-quately describe the variability and gradation characteristic of emotional expression (Barrett, 2009) and, particularly with respect to the musical domain, for being overly simplistic and neglecting more nuanced ‘aesthetic’ emotions, for example ‘nostalgia’

and ‘transcendence’ (Zentner, Grandjean, & Scherer, 2008). The logical endpoint of this argument is thus: as the number of discrete emotional states accepted to exist is inflated further and further, so too is the amount of labels necessary to de-scribe these states, and therefore the practical utility of these labels is diminished (since the differences between emotional experiences (from a similar type) may be as relevant and meaningful as the distinctions between emotions). Additionally, the assumption of universality of categorical emotion descriptors may be problematic, since it is not always clear what constitutes a ‘category’. For example, such descrip-tors are not necessarily uniform cross-culturally (Russell, 1991). For example, unlike English, Luganda typically makes no formal distinction between the concepts of sad-ness and anger (Leff, 1973). Research suggests that this discrepancy exists not only linguistically but also phenomenologically – when prompted to describe a personal

event involving anger, Bugandan adolescents were far more likely than Americans to report having cried in response (Davitz, 1969). Therefore, even so-called ‘basic’ emo-tion categories might not always provide a valid descripemo-tion of individuals’ emoemo-tional states.

By contrast, dimensional accounts conceptualise emotional states as non-discrete phenomena, with individual emotions instead describable as points along two or more continua. For example, Russell’s (1980) widely influential ‘circumplex’ model characterised emotion in terms of two dimensions: valence and arousal. According to this model, any possible emotional state may be mapped on a two-dimensional plane with ‘arousal’ and ‘valence’ axes, as illustrated in 6. Alternatives to this model have typically added one or more additional dimensions, in order to more effectively disam-biguate between different emotional states. For example, Mehrabian (1996) advanced the three-dimensional Pleasure-Arousal-Dominance (PAD) model, in which the latter dimension captures the degree of dominance or submissiveness that one feels, as part of the emotional state. This is useful for distinguishing between emotions that would occur in very similar Arousal/ Valence space; for example, anger and fear are typi-cally associated with negative valence and high arousal, but anger is characterised as dominant while fear is submissive (Broekens, 2012). Even if additional dimensions such as dominance do not correspond to the acitvity of any particular phsyiological system, Broekens (2012) argues that they can be useful in theoretical terms, or for computational modelling purposes. In contrast to the claims made by discrete ac-counts, such models suggest that all possible affective states are likely underpinned by a common neurophysiological network (Posner, Russell, & Peterson, 2005). On a neurochemical level, L¨ovheim (2012) posited that a variety of ‘distinct’ emotional states might be explainable in terms of a three-dimensional model, considering the

relative activity of three monoamine neurotransmitters: dopamine, noradrenaline and serotonin. Cognitively, L¨ovheim (2012) posited that the activity or inactivity of each of these neurotransmitters approximately corresponds to the dimensions ‘ac-tivation, vigilance and attention’, ‘self-confidence, inner strength and satisfaction’

and ‘reward, motivation and reinforcement’, respectively. The first of these dimen-sions is roughly analogous to arousal, whereas the latter two can be considered as subdivisions of valence.

Figure 6: Illustration of Russell’s (1980) ‘circumplex’ model of emotion. Various emotions are plotted according to their constituent arousal and valence dimensions, with the resulting mapping forming an approximate circle. Reconstructed from Rus-sell (1980).

In recent years there has been something of a shift towards dimensional representation of emotions, particularly in the field of affective computing (Gunes, 2010), although

the approach is not without disadvantages. The principle drawback to dimensional conceptualisation of emotion is that emotions that are highly proximal in terms of the resultant dimensional space (e.g. anger and fear) may be in practice quite distinct (Larsen and Diener, 1992; Barrett, 1998). For example, physiological modifications pertaining to anger differ only quantitatively from those associated with happiness, and yet anger does not in practice constitute an elevated or more intense form of happiness (Sartre, 2002). That is, reduction to a limited set of dimensions appears to disregard qualitative variance between different emotions, which may be rather important. In addition, it could be argued that an aforementioned disadvantage of discrete emotions – that they may not be well agreed upon cross-culturally – is also a problem for dimensional accounts. Specifically, the relevance of different dimensions may not be universal, and it is difficult to know whether a given dimensional model is valid cross-culturally. For example, in the case of Luganda mentioned previously, dimensional models might struggle to explain the grouping of anger and sadness, since the ‘arousal’ dimension should distinguish these emotions quite clearly.

It is worth noting that dimensional and discrete accounts of emotion need not be mutually exclusive – for example, categorical emotion labels may be conceptualised in terms of Russell’s (1980) four quadrants of arousal/ valence space (Thayer, 1989).

According to this type of framework, emotions are not necessarily ‘natural kinds’, but instead perceptual ‘gestalts’ (Bach, 2012), in which a discrete emotion labels refers to a particular set of parameters in a multidimensional space (Barrett, 2006).

That is, emotional ‘meaning’ emerges and is characterised and referred to using discrete emotional labels, even though on a purely physiological level its instanti-ation is a configurinstanti-ation of continuous parameters. Thus, Barrett (2006) considers that the ‘common-sense’ conceptualisation of emotions as distinct entities may not

correspond well with the underlying reality. This perspective is reconcilable with Darwinian ideas about the evolutionary utility of so-called ‘basic’ emotions. Instead of a set of discrete emotions being directly relevant to survival, specific configurations of underlying, dimensional parameters – according to one’s environment – happen to be especially relevant to survival. Because of this, there is a need to refer to these states, and therefore linguistic labels are created to describe them. One might therefore expect to see cultural and/ or geographical differences in which emotions are most relevant and, concomitantly, how they are referred to. Summarising this position, discrete emotions provide a convenient way to talk about emotions, even if they are not an accurate reflection of the underlying mechanisms responsible for their production and/or perception. However, because there is nothing sacrosanct about this way of referring to emotions, the exact terms, or groupings of terms used should be expected to vary across individuals or cultural groups. Therefore, emo-tion percepemo-tion might be interpreted as a more general phenomenon of categorical perception, in which continuous signals are interpreted with respect to cognitively superimposed stepwise boundaries. Considering the example of colour perception, the underlying external reality consists of light, either omitted or reflected, with continuously-varying wavelengths. No physical property of these stimuli determines the grouping of distinct categories such as ‘red’ or ‘green’, and in fact these groupings vary culturally – for example, the Russian language makes a mandatory distinction between light blue and dark blue, creating a direct perceptual advantage for Russian speakers in colour discrimination, relative to native English speakers (Winawer et al., 2007).

Indeed, Barrett (1998) has argued that whether emotions are thought of as discrete or dimensional varies between individuals, and therefore that any nomothetic account

of emotion might unfairly discard inter-individual variability in affective experience.

As evidence of this, Barrett reported that, even individuals from the same cultural background differed when instructed to group various emotions together on the ba-sis of similarity. Specifically, some individuals grouped similarly-valenced emotions together (corresponding more closely with predicted groupings from the dimensional account), while others grouped emotions that were similar in terms of arousal (cor-responding with predicted groupings from discrete accounts).

To conclude, Barrett’s position can be consolidated with the notion of ‘basic emo-tions’, by accepting that evolutionary response trends might form another basis for particular emotions to be generally distinguished. Patterns in both conceptualisation and physiological and behavioural responses play a role in defining emotions, and it is common in many cultures to give special status to a smaller set of emotions that are recognised from an early age onwards, despite the variations and differences that may exist within each of the categories.