Neuronal Processes Involved in Subjective Feeling Emergence: Oscillatory Activity During an Emotional Monitoring Task

Risk, Trust, and Emotion in Online Pharmacy Medication Purchases: Multimethod Approach Incorporating Customer Self-Reports, Facial Expressions, and Neural Activation

BACKGROUND

Online pharmacies are used less than other e-commerce sites in Germany. Shopping behavior does not correspond to consumption behavior, as online purchases are predominantly made for over-the-counter (OTC) medications.

OBJECTIVE

The objective of this study was to understand the purchasing experiences of online pharmacy customers in terms of critical factors for online pharmacy adoption.

METHODS

This study examined the perceived risk, perceived trust, and emotions related to purchasing medications online and, consequently, the purchase intention toward online pharmacies. In a within-subjects design (N=37 participants), 2 German online pharmacies with different perceptions of risk and trust were investigated for their main business, namely OTC and prescription drugs. The results of a preliminary study led to 1 online pharmacy with high and 1 with significantly low self-reported risk by the prestudy sample. Emotions were measured with a multimethod approach during and after the purchase situation as follows: (1) neural evaluation processes using functional near-infrared spectroscopy, (2) the automated direct motor response during the use of the online pharmacy via facial expression analysis (FaceReader), and (3) subjective evaluations through self-reports. Following the shopping experiences at both pharmacies for both product types, risk, trust, and purchase intention toward the pharmacies were assessed using self-assessments.

RESULTS

The 2 online pharmacies were rated differently in terms of risk, trust, emotions, and purchase intention. The high-risk pharmacy was also perceived as having lower trust and vice versa. Significantly stronger negative emotional expressions on customers' faces and different neural activations in the ventromedial prefrontal cortex and dorsomedial prefrontal cortex were measured when purchasing prescription drugs from the high-risk pharmacy than from the low-risk pharmacy, combined with OTC medications. In line with this, customers' self-ratings indicated higher negative emotions for the high-risk pharmacy and lower negative emotions for the low-risk pharmacy. Moreover, the ratings showed lower purchase intention for the high-risk pharmacy.

CONCLUSIONS

Using multimethod measurements, we showed that the preceding neural activation and subsequent verbal evaluation of online pharmacies are reflected in the customers' immediate emotional facial expressions. High-risk online pharmacies and prescription drugs lead to stronger negative emotional facial expressions and trigger neural evaluation processes that imply perceived loss. Low-risk online pharmacies and OTC medications lead to weaker negative emotional facial expressions and trigger neural evaluation processes that signify certainty and perceived reward. The results may provide an explanation for why OTC medications are purchased online more frequently than prescription medications.

The role of mindfulness on theta inter-brain synchrony during cooperation feedback processing: An EEG-based hyperscanning study

Mindfulness appears to improve empathy and understanding in relationships, which are necessary for successful cooperation. However, the impact of mindfulness on cooperation has not been fully studied. This study used hyperscanning technique to examine the effect of mindfulness on the inter-brain synchrony of interacting individuals during the cooperative tasks. Forty-one dyads were randomly assigned to a mindfulness group or a non-mindfulness group. Dyads of the mindfulness group performed a short mindfulness exercise following a 15-minute mindfulness audio guidance. Dyads of the non-mindfulness group were instructed to rest quietly with their eyes closed. Then, simultaneously and continuously EEG was recorded from all dyads when they completed a computer-based cooperative game task. Reaction times (RTs) and success rates were used to indicate the behavioral performance, and phase locking value (PLV) was used to indicate the inter-brain synchrony. The results showed that (1) Greater theta inter-brain synchrony during the cooperative computer game tasks was observed in the mindfulness group than in the non-mindfulness group; (2) Greater theta inter-brain synchrony was observed in the successful cooperation conditions as compared to those in the failure cooperation conditions; (3) Greater theta inter-brain synchrony was observed at the frontal region as compared to those at the parietal-occipital region in the successful cooperation condition. The results expand the neural basis of the effects of mindfulness on cooperation feedback processing.

Unraveling the Development of an Algorithm for Recognizing Primary Emotions Through Electroencephalography

The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-based emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent (SD) approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, new insights regarding subject-independent (SI) approximation have been discussed, although the results were not conclusive.

Electrophysiological evidence for inhibition hypothesis of micro-expressions based on tensor component analysis and Physarum network algorithm

The inhibition hypothesis advocated by Ekman (1985) states when an emotion is concealed or masked, the true emotion is manifested as a micro-expression (ME) which is a fleeting expression lasting for 40 to 500 ms. However, research about the inhibition hypothesis of ME from the perspective of electrophysiology is lacking. Here, we report the electrophysiological evidence obtained from an electroencephalography (EEG) data analysis method. Specifically, we designed an ME elicitation paradigm to collect data of MEs of positive emotions and EEG from 70 subjects, and proposed a method based on tensor component analysis (TCA) combined with the Physarum network (PN) algorithm to characterize the spatial, temporal, and spectral signatures of dynamic EEG data of MEs. The proposed TCA-PN methods revealed two pathways involving dorsal and ventral streams in functional brain networks of MEs, which reflected the inhibition processing and emotion arousal of MEs. The results provide evidence for the inhibition hypothesis from an electrophysiological standpoint, which allows us to better understand the neural mechanism of MEs.

An Action-Independent Role for Midfrontal Theta Activity Prior to Error Commission

Error-related electroencephalographic (EEG) signals have been widely studied concerning the human cognitive capability of differentiating between erroneous and correct actions. Midfrontal error-related negativity (ERN) and theta band oscillations are believed to underlie post-action error monitoring. However, it remains elusive how early monitoring activity is trackable and what are the pre-response brain mechanisms related to performance monitoring. Moreover, it is still unclear how task-specific parameters, such as cognitive demand or motor control, influence these processes. Here, we aimed to test pre- and post-error EEG patterns for different types of motor responses and investigate the neuronal mechanisms leading to erroneous actions. We implemented a go/no-go paradigm based on keypresses and saccades. Participants received an initial instruction about the direction of response to be given based on a facial cue and a subsequent one about the type of action to be performed based on an object cue. The paradigm was tested in 20 healthy volunteers combining EEG and eye tracking. We found significant differences in reaction time, number, and type of errors between the two actions. Saccadic responses reflected a higher number of premature responses and errors compared to the keypress ones. Nevertheless, both led to similar EEG patterns, supporting previous evidence for increased ERN amplitude and midfrontal theta power during error commission. Moreover, we found pre-error decreased theta activity independent of the type of action. Source analysis suggested different origin for such pre- and post-error neuronal patterns, matching the anterior insular cortex and the anterior cingulate cortex, respectively. This opposite pattern supports previous evidence of midfrontal theta not only as a neuronal marker of error commission but also as a predictor of action performance. Midfrontal theta, mostly associated with alert mechanisms triggering behavioral adjustments, also seems to reflect pre-response attentional mechanisms independently of the action to be performed. Our findings also add to the discussion regarding how salience network nodes interact during performance monitoring by suggesting that pre- and post-error patterns have different neuronal sources within this network.

The neuroscience of social feelings: mechanisms of adaptive social functioning

Social feelings have conceptual and empirical connections with affect and emotion. In this review, we discuss how they relate to cognition, emotion, behavior and well-being. We examine the functional neuroanatomy and neurobiology of social feelings and their role in adaptive social functioning. Existing neuroscience literature is reviewed to identify concepts, methods and challenges that might be addressed by social feelings research. Specific topic areas highlight the influence and modulation of social feelings on interpersonal affiliation, parent-child attachments, moral sentiments, interpersonal stressors, and emotional communication. Brain regions involved in social feelings were confirmed by meta-analysis using the Neurosynth platform for large-scale, automated synthesis of functional magnetic resonance imaging data. Words that relate specifically to social feelings were identfied as potential research variables. Topical inquiries into social media behaviors, loneliness, trauma, and social sensitivity, especially with recent physical distancing for guarding public and personal health, underscored the increasing importance of social feelings for affective and second person neuroscience research with implications for brain development, physical and mental health, and lifelong adaptive functioning.

Classification of Complex Emotions Using EEG and Virtual Environment: Proof of Concept and Therapeutic Implication

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training.

Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film Clips

In the present article, a novel emotional complexity marker is proposed for classification of discrete emotions induced by affective video film clips. Principal Component Analysis (PCA) is applied to full-band specific phase space trajectory matrix (PSTM) extracted from short emotional EEG segment of 6 s, then the first principal component is used to measure the level of local neuronal complexity. As well, Phase Locking Value (PLV) between right and left hemispheres is estimated for in order to observe the superiority of local neuronal complexity estimation to regional neuro-cortical connectivity measurements in clustering nine discrete emotions (fear, anger, happiness, sadness, amusement, surprise, excitement, calmness, disgust) by using Long-Short-Term-Memory Networks as deep learning applications. In tests, two groups (healthy females and males aged between 22 and 33 years old) are classified with the accuracy levels of [Formula: see text] and [Formula: see text] through the proposed emotional complexity markers and and connectivity levels in terms of PLV in amusement. The groups are found to be statistically different ( p << 0.5) in amusement with respect to both metrics, even if gender difference does not lead to different neuro-cortical functions in any of the other discrete emotional states. The high deep learning classification accuracy of [Formula: see text] is commonly obtained for discrimination of positive emotions from negative emotions through the proposed new complexity markers. Besides, considerable useful classification performance is obtained in discriminating mixed emotions from each other through full-band connectivity features. The results reveal that emotion formation is mostly influenced by individual experiences rather than gender. In detail, local neuronal complexity is mostly sensitive to the affective valance rating, while regional neuro-cortical connectivity levels are mostly sensitive to the affective arousal ratings.

Studying appraisal-driven emotion processes: taking stock and moving to the future

Appraisal theories of emotion, and particularly the Component Process Model, have claimed over the past three decades that the different components of the emotion process (action tendencies, physiological reactions, expressions, and feeling experiences) are essentially driven by the results of multi-level cognitive appraisals and that the feeling component constitutes a central integration and representation of these processes. Given the complexity of the proposed architecture of emotion generation, comprehensive experimental tests of these predictions are difficult to perform and thus evidence has been slow to appear. Complementing earlier work on self-reported appraisal, a massive amount of empirical results from studies with experimental designs based on appraisal manipulation, using electroencephalographic and electromyographic measures, now confirms many of the theoretical predictions with respect to the effect of different appraisal checks, their interactions, and their exact timing. A major issue for future research is the nature of the coherence or synchronisation of the appraisal-driven components in the unfolding emotion process. It is suggested that interdisciplinary multi-team research will be needed to face the theoretical and methodological challenges of experimentally investigating the dynamics of the emotion process.

An Appraisal-Driven Componential Approach to the Emotional Brain

This article suggests that methodological and conceptual advancements in affective sciences militate in favor of adopting an appraisal-driven componential approach to further investigate the emotional brain. Here we propose to operationalize this approach by distinguishing five functional networks of the emotional brain: (a) the elicitation network, (b) the expression network, (c) the autonomic reaction network, (d) the action tendency network, and (e) the feeling network, and discuss these networks in the context of the affective neuroscience literature. We also propose that further investigating the “appraising brain” is the royal road to better understand the elicitation network, and may be key to revealing the neural causal mechanisms underlying the emotion process as a whole.

Quality prediction of synthesized speech based on tensor structured EEG signals

This study investigates quality prediction methods for synthesized speech using EEG. Training a predictive model using EEG is challenging due to a small number of training trials, a low signal-to-noise ratio, and a high correlation among independent variables. When a predictive model is trained with a machine learning algorithm, the features extracted from multi-channel EEG signals are usually organized as a vector and their structures are ignored even though they are highly structured signals. This study predicts the subjective rating scores of synthesized speeches, including their overall impression, valence, and arousal, by creating tensor structured features instead of vectorized ones to exploit the structure of the features. We extracted various features to construct a tensor feature that maintained their structure. Vectorized and tensorial features were used to predict the rating scales, and the experimental result showed that prediction with tensorial features achieved the better predictive performance. Among the features, the alpha and beta bands are particularly more effective for predictions than other features, which agrees with previous neurophysiological studies.

Two Sides of Emotion: Exploring Positivity and Negativity in Six Basic Emotions across Cultures

We employ a novel paradigm to test whether six basic emotions (sadness, fear, disgust, anger, surprise, and happiness; Ekman, 1992) contain both negativity and positivity, as opposed to consisting of a single continuum between negative and positive. We examined the perceived negativity and positivity of these emotions in terms of their affective and cognitive components among Korean, Chinese, Canadian, and American students. Assessing each emotion at the cognitive and affective levels cross-culturally provides a fairly comprehensive picture of the positivity and negativity of emotions. Affective components were rated as more divergent than cognitive components. Cross-culturally, Americans and Canadians gave higher valence ratings to the salient valence of each emotion, and lower ratings to the non-salient valence of an emotion, compared to Chinese and Koreans. The results suggest that emotions encompass both positivity and negativity, and there were cross-cultural differences in reported emotions. This paradigm complements existing emotion theories, building on past research and allowing for more parsimonious explanations of cross-cultural research on emotion.

Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific 'affective' and 'cognitive' processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0-500 msec) increase in gamma power (61-69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1-12 sec) suppression of low frequency power (2.3-24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans.

In sync: gamma oscillations and emotional memory

Emotional experiences leave vivid memories that can last a lifetime. The emotional facilitation of memory has been attributed to the engagement of diffusely projecting neuromodulatory systems that enhance the consolidation of synaptic plasticity in regions activated by the experience. This process requires the propagation of signals between brain regions, and for those signals to induce long-lasting synaptic plasticity. Both of these demands are met by gamma oscillations, which reflect synchronous population activity on a fast timescale (35-120 Hz). Regions known to participate in the formation of emotional memories, such as the basolateral amygdala, also promote gamma-band activation throughout cortical and subcortical circuits. Recent studies have demonstrated that gamma oscillations are enhanced during emotional situations, coherent between regions engaged by salient stimuli, and predict subsequent memory for cues associated with aversive stimuli. Furthermore, neutral stimuli that come to predict emotional events develop enhanced gamma oscillations, reflecting altered processing in the brain, which may underpin how past emotional experiences color future learning and memory.

Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words

This article presents norms of valence/pleasantness, activity/arousal, power/dominance, and age of acquisition for 4,300 Dutch words, mainly nouns, adjectives, adverbs, and verbs. The norms are based on ratings with a 7-point Likert scale by independent groups of students from two Belgian (Ghent and Leuven) and two Dutch (Rotterdam and Leiden-Amsterdam) samples. For each variable, we obtained high split-half reliabilities within each sample and high correlations between samples. In addition, the valence ratings of a previous, more limited study (Hermans & De Houwer, Psychologica Belgica, 34:115-139, 1994) correlated highly with those of the present study. Therefore, the new norms are a valuable source of information for affective research in the Dutch language.

The Nature and Dynamics of Relevance and Valence Appraisals: Theoretical Advances and Recent Evidence

Appraisal theories of emotion have had a strong impact on the development of theory and experimental research in the domain of the affective sciences. While there is generally a high degree of convergence between theorists in this tradition, some central issues are open to debate. In this contribution three issues have been chosen for discussion: (a) varieties of relevance detection, (b) varieties of valence appraisal, and (c) sequential-cumulative effects of appraisal results. In addressing these issues, new theoretical ideas are suggested and an update of recent research on the sequence of appraisal processes is provided. Special emphasis is placed on nonverbal signatures of appraisal processes.

Cross-frequency dynamics of neuromagnetic oscillatory activity: two mechanisms of emotion regulation

Hemodynamic and electromagnetic neuroimaging suggests a sequence of intraregional and interregional activity during emotion processing. Oscillatory activity within trials may offer insight into neural mechanisms involved in this process. MEG was measured in 24 subjects during passive viewing of neutral and unpleasant pictures and emotion regulation by cognitive reappraisal. Regulation prompted more gamma increase and alpha decrease to picture onset than did passive viewing. Source analysis confirmed differential modulation of these power changes in occipitoparietal regions during passive viewing and larger power changes in prefrontal regions during regulation. Local coupling of alpha phase to gamma amplitude within a medial prefrontal region and long-range synchrony of medial prefrontal, occipital, and temporoparietal regions index mechanisms of prefrontal top-down contribution to emotion processing.

Neuroscience findings are consistent with appraisal theories of emotion; but does the brain “respect” constructionism?

I reject Lindquist et al.'s implicit claim that all emotion theories other than constructionist ones subscribe to a “brain locationist” approach. The neural mechanisms underlying relevance detection, reward, attention, conceptualization, or language use are consistent with many theories of emotion, in particular componential appraisal theories. I also question the authors' claim that the meta-analysis they report provides support for the specific assumptions of constructionist theories.

Appraisal frames of pleasant and unpleasant pictures alter emotional responses as reflected in self-report and facial electromyographic activity

Emotional pictures elicit responses across experiential, behavioral and physiological systems. The magnitude of these responses can be modulated by altering one's interpretation of emotional stimuli. Previous studies have indicated that appraisal frames affect subsequent interpretations of upcoming stimuli so as to alter self-reported emotions, ERP activity and autonomic responses. No studies to date have examined the effect of appraisal frames on expressive behaviors as measured by facial EMG. This study aims to test the hypothesis that appraisal frames can alter both emotional experience and facial expression and attempts to examine their effect on the temporal unfolding of facial expressions. Participants (N=20) were exposed to 125 pairs of appraisal frames (neutral or negative/positive) followed by neutral, unpleasant, or pleasant pictures reflecting five conditions: unpleasant-negative, unpleasant-neutral, pleasant-positive, pleasant-neutral and neutral-neutral. Results indicate that the unpleasant-negative compared to the unpleasant-neutral condition led to greater self-reported unpleasantness and arousal, as well as greater corrugator activity, and the pleasant-positive compared to the pleasant-neutral condition led to greater self-reported pleasantness and zygomaticus activity; modulation of facial responses became evident 0.5-1.0s after stimulus onset. These results suggest that appraisal frames effectively alter both emotional experience and facial expressions.

The temporal dynamics of two response-focused forms of emotion regulation: experiential, expressive, and autonomic consequences

This study examines the early affective consequences of two close forms of suppression. Participants (N=37) were shown negative, positive, and neutral pictures and cued either to attend to the pictures, or to perform expressive or physiological suppression (i.e., reduce body reactions). Continuous measures of experience, expressivity, and autonomic responses showed that both suppression strategies produced rapid response modulation. Common effects of the two strategies included a transient increase in negative feeling, a durable decrease in positive feeling, and a decrease in expressivity, cardiovascular activity, and oxygenation. The two strategies were significantly different only in response to positive stimuli, with physiological suppression showing a larger decrease in experience intensity and blood pressure. These results suggest a strong overlap between the two suppression strategies in terms of their early impact on emotional responses.

Emotions are emergent processes: they require a dynamic computational architecture

Emotion is a cultural and psychobiological adaptation mechanism which allows each individual to react flexibly and dynamically to environmental contingencies. From this claim flows a description of the elements theoretically needed to construct a virtual agent with the ability to display human-like emotions and to respond appropriately to human emotional expression. This article offers a brief survey of the desirable features of emotion theories that make them ideal blueprints for agent models. In particular, the component process model of emotion is described, a theory which postulates emotion-antecedent appraisal on different levels of processing that drive response system patterning predictions. In conclusion, investing seriously in emergent computational modelling of emotion using a nonlinear dynamic systems approach is suggested.

The Future of Psychology: Connecting Mind to Brain

Psychological states such as thoughts and feelings are real. Brain states are real. The problem is that the two are not real in the same way, creating the mind-brain correspondence problem. In this article, I present a possible solution to this problem that involves two suggestions. First, complex psychological states such as emotion and cognition can be thought of as constructed events that can be causally reduced to a set of more basic, psychologically primitive ingredients that are more clearly respected by the brain. Second, complex psychological categories like emotion and cognition are the phenomena that require explanation in psychology, and, therefore, they cannot be abandoned by science. Describing the content and structure of these categories is a necessary and valuable scientific activity.

Visual awareness, emotion, and gamma band synchronization

What makes us become aware? A popular hypothesis is that if cortical neurons fire in synchrony at a certain frequency band (gamma), we become aware of what they are representing. We tested this hypothesis adopting brain-imaging techniques with good spatiotemporal resolution and frequency-specific information. Specifically, we examined the degree to which increases in event-related synchronization (ERS) in the gamma band were associated with awareness of a stimulus (its detectability) and/or the emotional content of the stimulus. We observed increases in gamma band ERS within prefrontal-anterior cingulate, visual, parietal, posterior cingulate, and superior temporal cortices to stimuli available to conscious awareness. However, we also observed increases in gamma band ERS within the amygdala, visual, prefrontal, parietal, and posterior cingulate cortices to emotional relative to neutral stimuli, irrespective of their availability to conscious access. This suggests that increased gamma band ERS is related to, but not sufficient for, consciousness.