Etienne B. Roesch

The World of Emotions is not Two-Dimensional

For more than half a century, emotion researchers have attempted to establish the dimensional space that most economically accounts for similarities and differences in emotional experience. Today, many researchers focus exclusively on two-dimensional models involving valence and arousal. Adopting a theoretically based approach, we show for three languages that four dimensions are needed to satisfactorily represent similarities and differences in the meaning of emotion words. In order of importance, these dimensions are evaluationpleasantness, potency-control, activation-arousal, and unpredictability. They were identified on the basis of the applicability of 144 features representing the six components of emotions: (a) appraisals of events, (b) psychophysiological changes, (c) motor expressions, (d) action tendencies, (e) subjective experiences, and (f) emotion regulation.

Electrophysiological Correlates of Spatial Orienting Towards Angry Faces: A Source Localization Study

The goal of this study was to examine behavioral and electrophysiological correlates of involuntary orienting toward rapidly presented angry faces in non-anxious, healthy adults using a dot-probe task in conjunction with high-density event-related potentials and a distributed source localization technique. Consistent with previous studies, participants showed hypervigilance toward angry faces, as indexed by facilitated response time for validly cued probes following angry faces and an enhanced P1 component. An opposite pattern was found for happy faces suggesting that attention was directed toward the relatively more threatening stimuli within the visual field (neutral faces). Source localization of the P1 effect for angry faces indicated increased activity within the anterior cingulate cortex, possibly reflecting conflict experienced during invalidly cued trials. No modulation of the early C1 component was found for affect or spatial attention. Furthermore, the face-sensitive N170 was not modulated by emotional expression. Results suggest that the earliest modulation of spatial attention by face stimuli is manifested in the P1 component, and provide insights about mechanisms underlying attentional orienting toward cues of threat and social disapproval.

NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs

Simulating spiking neural networks is of great interest to scientists wanting to model the functioning of the brain. However, large-scale models are expensive to simulate due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present NeMo, a platform for such simulations which achieves high performance through the use of highly parallel commodity hardware in the form of graphics processing units (GPUs). NeMo makes use of the Izhikevich neuron model which provides a range of realistic spiking dynamics while being computationally efficient. Our GPU kernel can deliver up to 400 million spikes per second. This corresponds to a real-time simulation of around 40 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.

Neural correlates of emotional responses to music: An EEG study

This paper presents an EEG study into the neural correlates of music-induced emotions. We presented participants with a large dataset containing musical pieces in different styles, and asked them to report on their induced emotional responses. We found neural correlates of music-induced emotion in a number of frequencies over the pre-frontal cortex. Additionally, we found a set of patterns of functional connectivity, defined by inter-channel coherence measures, to be significantly different between groups of music-induced emotional responses.

Sustainable computational science: the ReScience initiative

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

Psychophysics of emotion: the QUEST for emotional attention.

To investigate the mechanisms involved in automatic processing of facial expressions, we used the QUEST procedure to measure the display durations needed to make a gender decision on emotional faces portraying fearful, happy, or neutral facial expressions. In line with predictions of appraisal theories of emotion, our results showed greater processing priority of emotional stimuli regardless of their valence. Whereas all experimental conditions led to an averaged threshold of about 50 ms, fearful and happy facial expressions led to significantly less variability in the responses than neutral faces. Results suggest that attention may have been automatically drawn by the emotion portrayed by face targets, yielding more informative perceptions and less variable responses. The temporal resolution of the perceptual system (expressed by the thresholds) and the processing priority of the stimuli (expressed by the variability in the responses) may influence subjective and objective measures of awareness, respectively.

Investigating affect in algorithmic composition systems

There has been a significant amount of work implementing systems for algorithmic composition with the intention of targeting specific emotional responses in the listener, but a full review of this work is not currently available. This gap creates a shared obstacle to those entering the field. Our aim is thus to give an overview of progress in the area of these affectively driven systems for algorithmic composition. Performative and transformative systems are included and differentiated where appropriate, highlighting the challenges these systems now face if they are to be adapted to, or have already incorporated, some form of affective control. Possible real-time applications for such systems, utilizing affectively driven algorithmic composition and biophysical sensing to monitor and induce affective states in the listener are suggested.

Changes in music tempo entrain movement related brain activity.

The neural mechanisms of music listening and appreciation are not yet completely understood. Based on the apparent relationship between the beats per minute (tempo) of music and the desire to move (for example feet tapping) induced while listening to that music it is hypothesised that musical tempo may evoke movement related activity in the brain. Participants are instructed to listen, without moving, to a large range of musical pieces spanning a range of styles and tempos during an electroencephalogram (EEG) experiment. Event-related desynchronisation (ERD) in the EEG is observed to correlate significantly with the variance of the tempo of the musical stimuli. This suggests that the dynamics of the beat of the music may induce movement related brain activity in the motor cortex. Furthermore, significant correlations are observed between EEG activity in the alpha band over the motor cortex and the bandpower of the music in the same frequency band over time. This relationship is observed to correlate with the strength of the ERD, suggesting entrainment of motor cortical activity relates to increased ERD strength.

Still feeling it: the time course of emotional recovery from an attentional perspective

Emotional reactivity and the time taken to recover, particularly from negative, stressful, events, are inextricably linked, and both are crucial for maintaining well-being. It is unclear, however, to what extent emotional reactivity during stimulus onset predicts the time course of recovery after stimulus offset. To address this question, 25 participants viewed arousing (negative and positive) and neutral pictures from the International Affective Picture System (IAPS) followed by task-relevant face targets, which were to be gender categorized. Faces were presented early (400–1500 ms) or late (2400–3500 ms) after picture offset to capture the time course of recovery from emotional stimuli. Measures of reaction time (RT), as well as face-locked N170 and P3 components were taken as indicators of the impact of lingering emotion on attentional facilitation or interference. Electrophysiological effects revealed negative and positive images to facilitate face-target processing on the P3 component, regardless of temporal interval. At the individual level, increased reactivity to: (1) negative pictures, quantified as the IAPS picture-locked Late Positive Potential (LPP), predicted larger attentional interference on the face-locked P3 component to faces presented in the late time window after picture offset. (2) Positive pictures, denoted by the LPP, predicted larger facilitation on the face-locked P3 component to faces presented in the earlier time window after picture offset. These results suggest that subsequent processing is still impacted up to 3500 ms after the offset of negative pictures and 1500 ms after the offset of positive pictures for individuals reacting more strongly to these pictures, respectively. Such findings emphasize the importance of individual differences in reactivity when predicting the temporality of emotional recovery. The current experimental model provides a novel basis for future research aiming to identify profiles of adaptive and maladaptive recovery.

Emotion and Anticipation in an Enactive Framework for Cognition (Response to Andy Clark)

Undeniably, anticipation plays a crucial role in cognition. By what means, to what extent, and what it achieves remain open questions. In a recent BBS target article, Andy Clark depicts an integrative model of the brain that builds on hierarchical Bayesian models of neural processing (Rao and Ballard, 1999; Friston, 2005; Brown et al., 2011), and their most recent formulation using the free-energy principle borrowed from thermodynamics (Feldman and Friston, 2010; Friston, 2010; Friston et al., 2010). Hierarchical generative models of cognition, such as those described by Clark, presuppose the manipulation of representations and internal models of the world, in as much detail as is perceptually available. Perhaps surprisingly, Clark acknowledges the existence of a “virtual version of the sensory data” (p. 4), but with no reference to some of the historical debates that shaped cognitive science, related to the storage, manipulation, and retrieval of representations in a cognitive system (Shanahan, 1997), or accounting for the emergence of intentionality within such a system (Searle, 1980; Preston and Bishop, 2002). Instead of demonstrating how this Bayesian framework responds to these foundational questions, Clark describes the structure and the functional properties of an action-oriented, multi-level system that is meant to combine perception, learning, and experience (Niedenthal, 2007). As pointed out by Clark, extreme models within this framework reduce experience to a mere by-product of the relationship between neural anticipatory signal and motor commands. Rightfully, Clark is uncertain of the radical proposal that we might “do away with the need to appeal to goals and rewards” (p. 59), and attempts to reinstate some aspects of emotional experience in the form of a frame of reference against which is construed this action-oriented predictive framework. We submit that this argument falls short of momentum in accounting for a rich phenomenology. Emotional experience simply cannot be reduced to a frame of reference: embodiment and embeddedness are at the core of the organism’s identity in its lived world, and fundamental aspects of emotional experience (Niedenthal, 2007). These features of experience situate the organism in the environment, which perceives and interacts with its immediate surrounding. This situatedness can only arise from concentric cycles of operations that combine integrated levels of anticipation with enactive processes of interaction with the environment. Anticipation not only takes place in the brain, which is the preferred level of perspective in the models introduced by Clark, but also at the many levels contained within the body, and between the body and the environment (Kurthen, 2007). In such a framework, the brain is a necessary but not sufficient part of the enactive organism, and emotion takes a central place as the scaffolding to awareness, especially in action-oriented perspectives (Frijda et al., 1989). By enactive processes, we refer to the closed-loop operations that settle the organism in the environment, which are grounded in, and shaped by the interaction itself, and that lead to the organism acting in ways optimal for adaptation and survival, supporting perception, learning, and experience. Within this framework, anticipation evidently plays a critical role in mitigating the interface with the environment. This situation relies on both the existence of natural constraints in the environment, and on the organism’s sensitivity to these constraints, which can either be based on their explicit description (i.e., a model of the world), or on the implicit, natural relationships that exist within the unitary system composed of both the organism and the environment. In the former case, prediction of future events occurs via the explicit manipulation of the description of the world against some metric of time. In contrast to this “weak” form of anticipation, which requires an expensive degree of energy to be sustained over time, a systemic form of anticipation may arise from the natural dispositions of both components of the system: a so-called “strong” anticipation based on the delayed feedback between the physical elements of the system, the properties of their synchronization and the strength of the coupling between them (Stepp and Turvey, 2010). The example of the oil drop in the saline solution that successfully exits a maze by following an appropriate Ph gradient illustrates this latter situation (Lagzi et al., 2010; http://www.youtube.com/watch?v=RXgP8rq_wfA). Arguably the oil drop does not possess or manipulate a model of the world to achieve this feat, but the interaction over time of the Ph of the oil drop with that of the saline solution leads the oil drop to move in the appropriate direction. In this context, enactive processes contrast sharply with the models described by Clark in that they assume an implicit, cheaper (energy-wise), representation-lean reference to the future, as the natural, bidirectional relation between the organism and the environment unfolds over time. We thus submit that these models may be more immune to most informational bottlenecks evident in light of the requirements for surviving in the world, as exemplified by the historical debates we referred to earlier, and may therefore be more adequate than other models to account for cognition. At the level of the (embodied) brain, enactive processes may interface with the environment through several mechanisms including the synchronization of neural assemblies and large-scale integration of information (Engel et al., 2001; Varela et al., 2001). Much is needed to characterize these mechanisms. In conclusion, we would like to formulate a word of caution. It is a mistake to conclude that, because model x can account for data y (epistemic concerns), therefore it must provide an accurate description of the inner workings of the brain (ontological description). Unless the model describes all the complexities of the embodied brain, embedded in the environment, one is almost always going to be making a conflation mistake. An extreme example might be representing “emotion” using a real number and, because this model can account for some data, wrongfully conclude that there must be an equivalent to the real number in the brain. Arguably, Clark’s review of a wide-range of data to justify hierarchical generative models falls into this category.