Learning is an adaptation that helps organisms navigate, survive, and reproduce in a changing environment. It enables organisms to produce behaviors promoting the avoidance of dangers and the procurement of rewards through the prediction and anticipation of impending threats and rewards in the environment. A central influence on learning is represented by emotions. Emotions are event-focused, two-step, rapid processes involving (a) a relevance-based emotion elicitation mechanism that (b) shapes a multicomponential emotional response, encompassing action tendency, autonomic reaction, expression, and subjective feeling (Sander, 2013). Emotions are generally highly adaptive in that they allow for responding flexibly to environmental contingencies by decoupling stimulus and response (Scherer, 1994), thus facilitating action (e.g., approach or avoidance behaviors) in situations relevant to the organism, as well as modulating cognitive processes, such as attention, learning, memory, and decision-making (see, e.g., Brosch, Scherer, Grandjean, & Sander, 2013; Sander, 2013). In that sense, learning and emotion are closely intertwined phenomena that are critical in enhancing organisms’ survival and well-being. Importantly, a pivotal process in which learning and emotion inextricably interact is emotional learning, which refers to the process whereby a stimulus acquires an emotional value (e.g., Phelps, 2006) or whereby a stimulus’ emotional value is updated.
Emotional learning is mainly studied by means of Pavlovian conditioning (Pavlov, 1927; Phelps, 2006). It consists of one of the most fundamental forms of learning in the animal kingdom, which is ubiquitous across a large variety of species ranging from simple (e.g., fruit flies and marine snails) to more complex (e.g., rats and humans) organisms (LeDoux, 1994).
In Pavlovian conditioning, the organism learns to associate an environmental stimulus (the conditioned stimulus) with a motivationally significant outcome (the unconditioned stimulus).
Through single or repeated contingent pairing with the unconditioned stimulus, the conditioned stimulus acquires a predictive and emotional value, and comes to elicit a preparatory response (the conditioned response; Pavlov, 1927; Rescorla, 1988b).
Pavlovian conditioning has substantially contributed to advancing our knowledge of learning, memory, and emotion, along with their complex interactions and neurobiological underpinnings (Büchel, Morris, Dolan, & Friston, 1998; Dunsmoor, Murty, Davachi, & Phelps, 2015; Dunsmoor, Niv, Daw, & Phelps, 2015; LaBar & Cabeza, 2006; LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998; LeDoux, 2000, 2012, 2014; Phelps, 2006; Phelps & LeDoux, 2005).
Pavlovian aversive conditioning has notably helped outline the psychological and brain mechanisms responsible for the development, expression, and modification of fear and
defensive responses, as well as assess whether animal models of fear can be applied to humans (Delgado, Olsson, & Phelps, 2006; Phelps & LeDoux, 2005). This research has highlighted the fundamental role of the amygdala in the acquisition, storage, expression, and extinction of conditioned threat responses and memories (e.g., Büchel et al., 1998; LaBar & Cabeza, 2006;
LaBar et al., 1998; LeDoux, 2000; Phelps, 2006; Phelps, Delgado, Nearing, & LeDoux, 2004;
Phelps & LeDoux, 2005), as well as the involvement of the ventromedial prefrontal cortex in the retention of extinction learning (Phelps et al., 2004). These findings thereby substantiated that the neural substrates underlying aversive learning are highly conserved across species and that animal models of fear learning can largely be translated to humans (Delgado et al., 2006;
Phelps & LeDoux, 2005). Additionally, Pavlovian aversive conditioning processes are considered to represent a crucial mechanism in the etiology, maintenance, treatment, and relapse of fear-related clinical disorders, such as anxiety disorders and specific phobias, hence serving as a valid laboratory or experimental model thereof (Lissek et al., 2005; Milad & Quirk, 2012; Mineka & Zinbarg, 2006; Seligman, 1971).
Interestingly, whereas Pavlovian aversive conditioning has drawn a large interest in the study of emotion, the role of Pavlovian appetitive conditioning has, however, been rarely investigated systematically in humans by comparison (e.g., Martin-Soelch, Linthicum, &
Ernst, 2007). Animal research on Pavlovian appetitive conditioning has indeed been mainly related to basic learning processes rather than to emotion (e.g., Hull, 1943), animal models of positive emotions being scarcer than animal models of fear for instance (but see, e.g., Berridge
& Robinson, 2003). Moreover, Pavlovian appetitive conditioning has been suggested to be more complex to study in humans, thus explaining this asymmetry. This complexity is in particular exemplified by the difficulty in finding appropriate appetitive stimuli that are able to elicit physiological responses that are similarly intense to the ones elicited by the aversive unconditioned stimuli, such as electric stimulations used in Pavlovian aversive conditioning (Hermann, Ziegler, Brimbauer, & Flor, 2000; Martin-Soelch et al., 2007) and/or a possible lack of sensitivity of the psychophysiological measures commonly used to systematically detect appetitive conditioned responses (Stussi, Delplanque, Coraj, Pourtois, & Sander, 2018).
Accordingly, developing and validating sensitive psychophysiological indicators of human Pavlovian appetitive conditioning is important to eventually remedy the relative scarcity of knowledge in the study thereof.
In general, research on Pavlovian conditioning has sought to uncover the general principles of learning, delineating in particular the key role of two computational learning
signals in associative learning: prediction error and stimulus’ associability (e.g., Mackintosh, 1975; Niv & Schoenbaum, 2008; Pearce & Hall, 1980; Rescorla & Wagner, 1972; Schultz, Dayan, & Montague, 1997). Prediction error corresponds to the discrepancy between the actual and the predicted outcome. It is a critical signal in driving learning: Organisms learn when there is a prediction error (Rescorla & Wagner, 1972). Prediction errors arise when the actual outcome is not predicted or more than predicted by the conditioned stimulus in presence (i.e., positive prediction error), thus triggering excitatory learning that increases the conditioned stimulus’ predictive value, or when the actual outcome is omitted or less than predicted by the conditioned stimulus (i.e., negative prediction error), thereby eliciting inhibitory learning diminishing the conditioned stimulus’ predictive value. By contrast, no learning takes place when the observed outcome is perfectly predicted by the conditioned stimulus, its predictive value remaining unchanged as a result. Neural correlates of prediction-error signals have been observed in midbrain dopaminergic neurons (especially for reward prediction error, Schultz et al., 1997; but see M. Matsumoto & Hikosaka, 2009), the striatum (e.g., Delgado, Li, Schiller,
& Phelps, 2008; Li, Schiller, Schoenbaum, Phelps, & Daw, 2011; O’Doherty, Dayan, Friston, Critchley, & Dolan, 2003), and the amygdala (e.g., Boll, Garner, Gluth, Finsterbusch, &
Büchel, 2013; see also Niv & Schoenbaum, 2008). As for stimulus’ associability, it refers to the amount of attention paid to the conditioned stimulus as a function of the extent to which it is a reliable predictor of the outcome (e.g., Mackintosh, 1975; Le Pelley, 2004; Li et al., 2011;
Pearce & Hall, 1980). Associability modulates learning by influencing the conditioned stimulus’ effectiveness to be established as a predictive signal of the outcome, with stimuli that are better attended to (i.e., with a high associability) being more easily associated with the outcome (Le Pelley, 2004; Mackintosh, 1975; Pearce & Hall, 1980). The computations of associability have been reported to principally involve the amygdala (Boll et al., 2013; Li et al., 2011; M. Matsumoto & Hikosaka, 2009).
Nonetheless, this line of research has generally omitted to consider the relative importance of the stimuli at stake for the organism. Although early learning theorists initially posited that all stimuli can be associated with equal ease regardless of their nature (e.g., Pavlov, 1927; Watson & Rayner, 1920), certain associations have been revealed to be more easily formed and maintained than others (Garcia & Koelling, 1966; Öhman & Mineka, 2001;
Seligman, 1970, 1971), thus reflecting the existence of learning biases. Surprisingly, mechanisms underlying such preferential emotional learning remain yet unclear. Influential theoretical models put forward to account for these preferential associations, such as the
preparedness (Seligman, 1970, 1971) and fear module (Öhman & Mineka, 2001) theories, adopt an evolutionary perspective according to which organisms are biologically prepared to preferentially associate stimuli that have threatened survival across evolution with naturally aversive events. Consistent with this view, a series of empirical studies have shown that
“evolutionarily prepared” threat stimuli – such as snakes, angry faces, or outgroup faces – are more readily and persistently associated with an aversive outcome than threat-irrelevant stimuli – such as birds, happy faces, or ingroup faces (e.g., Atlas & Phelps, 2018; Ho & Lipp, 2014;
Öhman & Dimberg, 1978; Öhman, Fredrikson, Hugdahl, & Rimmö, 1976; Öhman & Mineka, 2001; Olsson, Ebert, Banaji, & Phelps, 2005). Extending preparedness theory, Öhman and Mineka (2001) further proposed that the preferential processing of, and emotional learning to, evolutionary threat-relevant stimuli would be specifically subserved by an evolved fear module centered on the amygdala in the human brain, allowing the organism to readily detect and react to these stimuli. Thus, the preparedness and fear module theories emphasize the importance of negative stimuli carrying threat-related information from phylogenetic origin in emotional learning, suggesting that preferential emotional learning is underlain by an evolved threat-specific mechanism.
In contrast, we offer here a different view by suggesting that preferential emotional learning is not specific to evolutionary threat-related stimuli but can extend to all stimuli that are relevant to the organism’s concerns, such as their needs, goals, motives, values, or well-being (Frijda, 1986, 1988). Deriving from appraisal theories of emotion (e.g., Sander, Grafman,
& Zalla, 2003; Sander, Grandjean, & Scherer, 2005, 2018), this alternative model holds that such preferential learning is driven by a general mechanism of relevance detection as opposed to a threat-specific mechanism (Stussi, Brosch, & Sander, 2015; Stussi, Ferrero, Pourtois, &
Sander, 2019; Stussi, Pourtois, & Sander, 2018). Relevance detection is a rapid evaluation process, which enables the organism to appraise, detect, and determine whether a stimulus encountered in the environment is relevant to the their concerns (Frijda, 1986, 1988; Pool, Brosch, Delplanque, & Sander, 2016; Sander et al., 2003, 2005). According to this model, threat-relevant stimuli from evolutionary origin are preferentially processed and learned not because they have been associated with threat through evolution, but because they are highly relevant to the organism’s survival. More specifically, the relevance detection model predicts that stimuli that are detected as relevant to the organism’s concerns benefit from preferential emotional learning, independently of their valence and evolutionary status per se. Accordingly, relevance detection may provide a promising theoretical framework to move beyond the
fear-centered view positing that only threat-related stimuli are preferentially learned, and thereby foster new insights into the understanding of emotional learning in humans.
An overview of the major aims and structure of this thesis
The purpose of this thesis is to investigate the links between the appraisal processes involved in emotion elicitation and the basic mechanisms underlying learning in humans. More precisely, our goal is to empirically assess whether relevance detection is a general determinant of emotional learning in humans, as well as establish and characterize its role therein. To do so, we conducted a series of experiments in healthy adult participants that aimed to systematically test the theoretical prediction deriving from appraisal theories of emotion, which states that stimuli that are detected as highly relevant to the organism’s concerns are preferentially learned during Pavlovian conditioning, independently of their intrinsic valence and evolutionary status per se. These experiments had the following objectives: (a) to examine whether, similar to evolutionary threat-relevant stimuli, positive stimuli that are biologically relevant to the organism are likewise preferentially conditioned to threat during Pavlovian aversive conditioning (Studies 1 and 2), (b) to characterize the influence of the stimulus’
affective relevance on Pavlovian aversive learning (Studies 1 and 2), (c) to assess whether preferential Pavlovian aversive conditioning extends to stimuli detected as relevant to the organism’s concerns beyond biological and evolutionary considerations (Study 3), (d) to investigate the role of inter-individual differences in the organism’s concerns in preferential Pavlovian aversive conditioning (Studies 2 and 3), and (e) to test and validate a new psychophysiological measure of Pavlovian appetitive conditioning in humans that could be subsequently used to investigate the generality of a relevance detection mechanism in appetitive learning (Study 4).
In this perspective, the present thesis is structured as follows: In the theoretical part (chapter 2), we first define and delimit the concept of emotional learning as used in the context of this thesis. We subsequently present the basic principles of Pavlovian conditioning, the main paradigms used to study Pavlovian conditioning in humans, and the major formal models of Pavlovian conditioning. We then introduce the notion of preferential emotional learning and the dominant theoretical models thereof, namely the preparedness and fear module theories, and review evidence in the Pavlovian conditioning literature supporting and challenging the core assumption of these models, which posit that only stimuli that have posed threats to survival across evolution are preferentially conditioned to threat. Afterward, we elaborate the relevance detection framework based on appraisal theories, which provides an alternative
model to the biological preparedness perspective. Finally, we delineate the thesis objectives specified above in more detail.
In the experimental part (chapter 3), we report the series of experiments performed to assess the role of relevance detection in human emotional learning. In brief, Study 1 investigated across three experiments whether, similar to biologically threat-relevant stimuli (angry faces and snakes), positive emotional stimuli (baby faces and erotic stimuli) are more readily associated with an aversive event (electric stimulation) during Pavlovian aversive conditioning than neutral stimuli with less relevance (neutral faces and colored squares). Study 2 examined whether, similar to angry faces, preferential Pavlovian aversive conditioning may be observed to happy faces, and the role of inter-individual differences therein. Study 3 assessed whether preferential Pavlovian aversive conditioning can occur to initially neutral stimuli devoid of any inherent biological evolutionary significance, but acquiring goal-relevance through experimental manipulation, and whether such preferential learning is modulated by inter-individual differences in achievement motivation. Study 4 investigated whether the postauricular reflex – a vestigial muscle microreflex that is potentiated by pleasant stimuli relative to neutral and unpleasant stimuli – may provide a valid psychophysiological indicator of Pavlovian appetitive conditioning in humans.
To conclude, in the general discussion (chapter 4) we integrate the findings of our empirical studies within the theoretical framework outlined in the theoretical part. There, we seek to discuss the contribution of this work to the conceptualization of the basic mechanisms underlying emotional learning in humans and the role of relevance detection in the modulation of cognitive functions. We also outline the limitations of this thesis and elaborate on potential new avenues for future research in this area.