A major disagreement between proponents of discrete and dimensional approaches relates to the relative primacy
of discrete emotional systems or dimensional core affect systems in generating felt emotional states. Core affect theorists propose that ongoing core affective state (valence and arousal) is combined with evaluations or appraisals of current context/environmental conditions to generate a subjective state that can be described in discrete emotion terms (e.g. Barrett 2006
). The experience of specific emotions is thus a product
of core affect and not vice versa. Discrete/basic emotion theorists take the opposite view and argue that core affect emerges as a cognitive ‘distillation’ of the overall affective impact of the experience of specific emotions (e.g. Panksepp 2007
; see also Tellegen et al. 1999
). Given that there is psychological, neural and behavioural evidence for both types of system, an alternative view is to hypothesize that both systems may be present in humans and many animals, that they interact in some way and that they serve different functions. Such a synthesis has been proposed by, among others, Panksepp (2007)
and Izard (2007)
and we develop it further here.
We first briefly consider the causes and functions of discrete emotions. We then suggest that: (i) core affective components of discrete emotions and other affective states provide a ‘common currency’ that may function to prioritize actions, (ii) discrete emotions, generated by events, influence position in core affect space, (iii) cumulative experience of location in core affect space underlies longer term mood, and (iv) core affect mood states can, in turn, influence decision-making and discrete emotions. Thus, a bi-directional relationship between discrete emotion and core affect systems is proposed.
(a) Causes and functions of discrete emotions
Discrete emotions arise in response to anticipation or experience of rewarding or punishing events. They are thus event-focused (or object-focused) and usually short-lasting. Appraisal theorists
suggest that a process of ‘stimulus checks’ of a number of key characteristics (e.g. valence, predictability, familiarity) of eliciting circumstances generates emotions (Ellsworth & Scherer 2003
). For example, appraisal of a stimulus as intrinsically unpleasant (punishing), sudden, unpredictable and unfamiliar is likely to induce a ‘fear’ (Q4) emotion (Scherer 2001
). In humans, situations may be appraised in numerous ways depending on, for example, the characteristics of the situation and the subject's previous experiences and current motivations. Consequently, appraisals may result in many different emotional states, including varied nuances of discrete emotions (Ellsworth & Scherer 2003
). Human appraisals may involve cognitive processes such as memory (underpinning familiarity/novelty appraisals) and anticipation (underpinning appraisals of predictability), but they can also be simple, rapid and ‘automatic’ (subconscious; Zajonc 1980
; Grandjean & Scherer 2008
). It is thus conceivable that similar processes occur in animals (Desiré et al. 2002
), and act to trigger discrete emotional states which engage specific neurobehavioural systems.
These discrete emotion systems probably function to organize short-term responses to the eliciting circumstances, recruiting appropriate physiological resources, motivating relevant behaviours and thus facilitating the organism's immediate survival (e.g. Frijda 1994
; Rolls 2005
). Different discrete emotion systems have evolved to deal with different types of challenge. For example, Panksepp (1998)
postulates that a distinct ‘panic’/‘separation distress’ system functions to maintain social bonds between separated individuals by triggering vocalization and search behaviour.
(b) Discrete emotions, sensations and motivations have core affective characteristics that may function as a common currency in decision-making
Discrete emotions have an underlying valenced structure—this is what characterizes them as part of the affective/emotional system—and can thus be located in core affective space. Other states such as sensations
also share these characteristics. Physical sensations mediated by direct neural connections between the sensory apparatus (e.g. receptors for touch, taste, smell) and the brain can be inherently rewarding or punishing and hence also located in core affect space. For example, a taste may give rise to a positively valenced affective response of ‘pleasure’ that may be located at some point in the Q1 quadrant and function to influence subsequent wanting of the stimulus (Berridge 2007
Wanting states—motivation for a specific reward—can also be located in core affect space. They are determined by internal changes reflecting current physiological need, and external stimuli that have become strongly associated with reward through the animal's developmental or evolutionary past, and, in humans, can be experienced as high-arousal positive states (Q1) associated with reward-seeking behaviour.
Location in core affect space thus represents a common currency
(cf. McNamara & Houston 1986
; Cabanac 1992
; Spruijt et al. 2001
) that may function to allow comparisons and trade-offs between disparate discrete emotions, sensations and motivations when behavioural decisions are being made. For example, a hungry and thirsty animal needs to be able to weigh up the relative reward values of searching to obtain food or water at any one time, and also to calculate when searching behaviour becomes too dangerous because of heightened threats from punishers such as predator attack. Emotions and motivations can only be made use of in such ‘expected utility’ type decisions if they incorporate the core affective feature of valence that can function as a common currency (cf. Cabanac 1992
(c) Discrete emotions and other states influence location in core affect space
Following from the above, discrete emotions, sensations and motivations can be conceptualized as generating short-term changes in an animal's location in core affect space. For example, the onset of feeding motivation may involve the animal moving towards an aroused, positive, seeking state (Q1). Detection of a food item may then lead to a higher arousal state of excitement with successful capture of prey perhaps leading to a temporary state of elation (Q1), followed by a lower arousal consummatory state of sensory pleasure during eating and an even lower arousal post-consummatory positive state of contentment or satisfaction (Q2). On the other hand, failure to detect or obtain a food item may initially lead to a temporary high-arousal negative state of frustration (Q4), which may then subside to a lower arousal negative state of disappointment or sadness (Q3). These trajectories, related to the acquisition of fitness-enhancing rewards (one can imagine similar trajectories related to the search for mates, positive social interactions, etc.), are illustrated in . Although, as discussed earlier, they tend to inhabit the Q3–Q1 axis, it is clear that relative success or failure can lead to states in the Q2 and Q4 quadrants too.
Figure 2. Hypothetical examples of how an organism's core affective state may change with time. The right-hand green loop represents changes during successful cycles of reward acquisition. The left-hand green loop represents changes when reward acquisition is unsuccessful. (more ...)
The appearance of a fitness-threatening stimulus can intrude into these reward acquisition cycles at any time and rapidly shift the animal's position in core affect space into the Q4 quadrant of high-arousal negative fear and anxiety states (). These states are associated with appropriate responses to danger aimed at avoiding punishers. Successful responses will result in the removal of threat and a lower arousal positive state of relief or calm (Q2). Such trajectories, related to the avoidance of fitness reduction, thus primarily inhabit the Q2–Q4 axis and, owing to their potentially life-threatening sequelae, generally assume primacy over Q1–Q3 states associated with the acquisition of fitness-enhancing rewards (e.g. Haselton & Nettle 2006
; cf. Dawkins & Krebs 1979
In this view, movement through core affect space is driven by discrete emotions, sensations and motivations, and represents the organism's experiences of success and failure in acquiring rewards and avoiding punishers. We suggest that it forms the basis for longer term mood states.
(d) The causes of longer term core affect ‘mood’ states
In humans, core affective states do not only occur in response to specific events or stimuli. They also occur in the absence of, and without being directed at, any particular object. In this case, they are usually referred to as free-floating moods. Moods are typically longer lasting than discrete emotions, sensations or motivations, and are a relatively ‘pure’ form of core affect, lacking the action tendencies and appraisal-induced responses to emotion-eliciting situations that characterize discrete emotions. They include longer term states of ‘happiness’ or ‘sadness’ and, in their more extreme forms, states such as chronic anxiety (Q4) or depression (Q3) or mania (Q1). At any one time, an individual's core affective state can be conceptualized as a combination of their longer term background mood state and their reactions to current emotion-inducing events. Thus, a chronically anxious individual may experience temporary states of positive affect under certain circumstances (e.g. when eating a particularly delicious meal) despite their ‘background’ state of anxiety. Consequently, ongoing mood states may be most easily revealed when an individual is not currently exposed to strong emotion-inducing events (which may lead to ‘ceiling-effect’ responses that mask background mood state) or when novel or ambiguous events occur whose affective salience is not immediately apparent. They can be conceptualized as the background core affect state that the individual will revert to when specific emotion-inducing events are absent.
In our view, mood states probably reflect a cumulative function of the experience of shorter term emotional episodes (e.g. discrete emotions, sensations, motivations). For example, if an animal is in an environment in which it experiences frequent threatening events, and hence its emotional state is often in the Q4 quadrant, it may develop a longer term high-arousal negative mood state that mirrors this cumulative experience. If it is frequently successful at avoiding these events, or it is in a generally safe environment, a longer term low-arousal positive mood state (Q2: ‘relaxed’/‘calm’) may result. On the other hand, if it is in a plentiful environment and successful at acquiring fitness-enhancing rewards, it is likely to exhibit a mood state that is centred on the Q1 quadrant, whereas a low-resource environment and failure to acquire rewards will lead to a predominantly Q3 mood (cf. Carver 2001
In relation to the trajectories shown in , we propose that mood states can be likened to a ‘running mean’ of positions occupied within core affect space over a preceding time period, and thus continually (albeit slowly) change as the result of novel events and experiences. This view of mood states as representing past experience chimes with the findings that, for example, chronic anxiety and depression states usually arise from exposure to specific environmental and emotional circumstances (e.g. chronic stress, major life events; e.g. Eysenck et al. 2006
; Young & Korszun 2009
At a neural level, the above proposal requires that there is cross-talk between the activity of discrete emotional systems (e.g. sub-cortical structures such as the PAG) and those that become activated across a broad range of rewarding or punishing events (e.g. BAS/BIS/FFFS). Widely dispersed neuromodulatory systems (e.g. dopaminergic, serotonergic, noradrenergic, opioidergic) appear to be prime candidates for the latter reward/punishment systems (Gray 1994
; Burgdorf & Panksepp 2006
; Berridge 2007
; Leknes & Tracey 2008
), and it is possible that they, and higher cortical areas (e.g. anterior cingulate cortex, orbitofrontal cortex), somehow integrate or distil the activity of discrete emotion systems across time in terms of overall positive and negative experience (cf. Spruijt et al. 2001
; Panksepp 2007
). Differences in the sensitivity of such systems, whether determined by genetic or experiential factors, may underlie individual predispositions to particular mood states (Corr 2008
(e) Functions of mood states: their influence on decision-making, appraisals and discrete emotions
We suggest that mood states provide information about the type of environment the organism is living in—the presence (or probability) of threats
and reward opportunities
—and how well it is coping (see also Carver 2001
; Prinz 2004
). This information plays an important role in guiding animals' decisions when appraising new situations or stimuli, especially if there is a degree of ambiguity in their potentially rewarding or punishing consequences (Davidson 1994
; Mendl et al. 2009
). For example, if an individual is living in an environment where it has experienced high levels of threat
, its mood state has a greater likelihood of being in the Q4 quadrant. In such an environment where probability of danger is high, it would make adaptive sense to appraise ambiguous stimuli such as a rustle in the grass as more likely to predict a negative event (e.g. predator), and hence to take safety-first avoidant action, in comparison to an individual living in a low threat
environment with a mood state in the Q2 quadrant for whom a negative judgement is likely to result in wasted time and energy (Nesse 2005
Different responses to ambiguity may also be observed in individuals whose background mood state is predominantly in quadrant Q1 (resulting from experience of an environment with high probability of opportunity for gaining fitness-enhancing rewards
) or Q3 (experience of low reward-opportunity
environments). Those in Q1 are likely to benefit from judging ambiguous stimuli as indicating a positive event (e.g. prey), thus facilitating reward-seeking behaviour, relative to those in Q3 mood states who may benefit from inhibiting reward-seeking behaviour in order to conserve energy until environmental conditions change (Nesse 2000
Thus, we suggest that Q1 moods are associated with decisions appropriate to high reward-opportunity environments, reflecting a high ‘expectation’ of positive events, and Q2 moods with decisions reflecting low expectation of negative events. These can be termed ‘optimistic’ biases in decision-making (e.g. judging ambiguous stimuli positively). Conversely, Q3 moods are associated with low expectation of positive events and Q4 moods with high expectation of negative events (‘pessimistic’ biases). Clearly, environments may not be as simple as this. Some complexity can be added by considering combinations of environments with low, intermediate and high probabilities of opportunity and threat to generate a broader range of predictions linking experience of environment, mood and expectations/decision-making as indicated in .
Table 1. Postulated links between prevailing environmental conditions (in terms of reward acquisition opportunities and threat of punishers), resulting predominant core affective mood state (quadrants Q1–Q4) and biases in expectation of rewarding (+ve) (more ...)
Consistent with these ideas, there is a large body of research with humans showing that background mood state does indeed appear to influence decision-making (e.g. Schwarz & Clore 1983
; Bechara et al. 2000
; Loewenstein et al. 2001
), including in ways similar to those predicted. People in negative states tend to judge ambiguous stimuli negatively (e.g. MacLeod & Byrne 1996
). They also more readily attend to threatening stimuli and recall negative memories than people in positive mood states (see Mineka et al. 1998
; Mogg & Bradley 1998
). Furthermore, there is evidence that people with a long-term tendency towards (trait) anxiety and/or current (state) anxiety (Q4) judge ambiguous stimuli as more likely to be negative, while people in states of sadness or depression (Q3) judge them as less likely to be positive in line with predictions outlined above (e.g. MacLeod et al. 1997
; Stober 2000
; MacLeod & Salaminiou 2001
). There is also evidence that people in positive moods show optimistic forms of these so-called ‘cognitive biases’ (e.g. Nygren et al. 1996
Mood state may thus act as a heuristic device influencing cognitive processes and facilitating appropriate decision-making behaviour. Because appraisals of situations/events may themselves involve cognitive processes, mood states can therefore also affect these appraisals and the resulting short-term emotional responses. The causal link between short-term discrete emotions and longer term core affect mood is thus bi-directional. It is possible to envisage positive feedback loops in which, for example, a Q4 mood state enhances anticipation of negative events and negative interpretation of ambiguity, and this leads to further negative short-term emotional experiences which, in turn, intensify the Q4 mood. Such processes are implicated in the aetiology of chronic anxiety and depression in humans (Beck 1967
). In the natural environment, they may function to help animals escape from or cope with difficult or threatening conditions (Nesse 2000
), until circumstances change and the experience of more positive events leads to a gradual alteration in mood state.