Multivariate pattern analysis of electroencephalography data reveals information predictive of charitable giving

Neural correlates of empathy in donation decisions: Insights from EEG and machine learning

Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait empathy influence prosocial behavior. Addressing this knowledge gap is important to understand empathy-driven prosocial behavior. We employed an EEG experiment combined with interpretable machine learning methods to probe these questions. We found that empathic concern (EC) played the most pivotal role in donation decision. Behaviorally, EC negatively moderates the effect of perceived closeness and deservedness of charity projects on the willingness to donate. The machine learning results indicate that EC significantly predicts late positive potential (LPP) and beta-band activity during donation information processing. Further regression analysis results indicate that EC, rather than other dimensions of trait empathy, can positively predict LPP amplitude and negatively predict beta-band activity. These results indicated that participants with higher EC scores may experience heightened emotional arousal and the vicarious experience of others' emotions while processing donation information. Our work adds weight to understanding the relationship between trait empathy and prosocial behavior and provides electrophysiological evidence.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

Cognitive flexibility: neurobehavioral correlates of changing one's mind

Behavioral and cognitive flexibility allow adaptation to a changing environment. Most tasks used to investigate flexibility require switching reactively in response to deterministic task-response rules. In daily life, flexibility often involves a volitional decision to change behavior. This can be instigated by environmental signals, but these are frequently unreliable. We report results from a novel "change your mind" task, which assesses volitional switching under uncertainty without the need for rule-based learning. Participants completed a two-alternative choice task, and following spurious feedback, were presented with the same stimulus again. Subjects had the opportunity to repeat or change their response. Forty healthy participants completed the task while undergoing a functional magnetic resonance imaging scan. Participants predominantly repeated their choice but changed more when their first response was incorrect or when the feedback was negative. Greater activations for changing were found in the inferior frontal junction, anterior insula (AI), anterior cingulate, and dorsolateral prefrontal cortex. Changing responses were also accompanied by reduced connectivity from the AI and orbitofrontal cortices to the occipital cortex. Using multivariate pattern analysis of brain activity, we predicted with 77% reliability whether participants would change their mind. These findings extend our understanding of cognitive flexibility in daily life by assessing volitional decision-making.

Self-interest is intuitive during opportunity (in)equity: Evidence from multivariate pattern analysis of electroencephalography data

Fairness is a remarkable preference for human society, involving both outcome and opportunity equity. Most previous studies have explored whether fairness itself or self-interest is intuitive during outcome (in)equity. However, intuition during outcome (in)equity can be affected by both fairness level and actual payoff. Since opportunity (in)equity is only affected by the fairness level, we explored only intuition during fairness by measuring event-related potential responses to opportunity (in)equity. Participants played a social non-competitive two-person choice game with advantage opportunity inequity (AI), opportunity equity (OE), and disadvantage opportunity inequity (DI). The behavioral results suggested an opportunity inequity bias, with greater feelings of fairness and pleasantness during OE than during AI and DI. However, multivariate pattern analysis of the event-related potential (ERP) data suggested that AI, OE, and DI can be significantly distinguished from each other in relatively early windows overlapping with early positive negativity (EPN), and AI and DI can be significantly further distinguished during a relatively late window overlapping with late positive potential (LPP). Moreover, the conventional ERP analysis found that EPN amplitudes were more negative for AI than for OE and DI, as well as for OE than for DI, suggesting a pleasure bias for increased self-interest. LPP amplitudes were greater for DI than for AI and OE, suggesting enhanced sensitivity to DI. These results suggest that self-interest is intuitive during opportunity (in)equity.

Predicting individual muscle fatigue tolerance by resting-state EEG brain network

OBJECTIVE

Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored.

APPROACH

Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance.

MAIN RESULTS

Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network.

SIGNIFICANCE

Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.

Neural sensitivity to helping outcome predicts helping decision in real life

Prosocial helping behavior is a highly valued social practice across societies, but the willingness to help others varies among persons. In our opinion, that willingness should be associated with the sensitivity to helping outcome at the individual level - that is, increasing as a function of positive outcome sensitivity but decreasing as a function of negative outcome sensitivity. To examine this possibility, we asked participants to make helping decisions in a series of hypothetical scenarios, which provided outcome feedback (positive/negative) of those decisions. Event-related potential (ERP) response to helping outcome was recorded, such that the feedback-related negativity (FRN) and P300 were supposed to reflect the sensitivity to negative outcome and positive outcome, respectively. After the formal task, participants were asked if they would like to donate money to a charity. Consistent with our hypothesis, we found that compared to those who were not willing to donate, the participants who donated money (22 of 41 individuals) showed a smaller FRN but a larger P300. Among these participants, the amount of donation was negatively correlated with FRN response to negative outcome, but positively correlated with P300 response to positive outcome. These findings support the importance of helping outcome sensitivity to prosocial behavior.

Reduced salivary oxytocin after an empathic induction task in Intimate Partner Violence perpetrators: Importance of socio-affective functions and its impact on prosocial behavior

Intimate Partner Violence (IPV) has been linked to difficulties in socio-affective functions. Nevertheless, the underlying psychobiological mechanisms that might be responsible for them remain unclear. Oxytocin (OXT) stands out as an important hormone that may favor the salience of social information, due to its relevance in empathy and prosocial behavior. Thus, the study of salivary OXT (sOXT) may provide further information about potential impairments in social cognition in IPV perpetrators. This study analyzed the effects of an empathic induction task, performed through negative emotion-eliciting videos, on endogenous sOXT levels, mood state, and emotional perception in 30 IPV perpetrators compared to 32 controls. Additionally, we explored their performance on prosocial behavior after the empathic induction task, using Hare's donation procedure. Lower sOXT levels were found in IPV perpetrators after the task compared to controls, along with a general decreasing tendency in their sOXT levels. Additionally, IPV perpetrators exhibited no change in their mood state and perceived others' emotions as more positive and less intense. Moreover, the mood state response and alexithymia traits, respectively, positively and negatively predicted the sOXT levels after the empathic induction task in the entire sample. Finally, we did not observe a lower appearance of prosocial behaviors in IPV perpetrators; however, higher sOXT levels after the empathic induction task were found in subjects who donated when considering the whole sample. In sum, IPV perpetrators exhibited differences in their sOXT levels when empathizing, compared to controls, with alexithymia and the emotional response potentially explaining the sOXT levels after the task. Furthermore, prosocial behavior was more related to these sOXT levels than to IPV. As our knowledge about the emotional processing of IPV perpetrators increases, we will be better able to develop and include coadjutant treatments in current psychotherapeutic programs, in order to focus on their emotional needs, which, in turn, would reduce the future risk of recidivism.

Subcortical-Cortical Functional Connectivity as a Potential Biomarker for Identifying Patients with Functional Dyspepsia

The diagnosis of functional dyspepsia (FD) presently relies on the self-reported symptoms. This study aimed to determine the potential of functional brain network features as biomarkers for the identification of FD patients. Firstly, the functional brain Magnetic Resonance Imaging data were collected from 100 FD patients and 100 healthy subjects, and the functional brain network features were extracted by the independent component analysis. Then, a support vector machine classifier was established based on these functional brain network features to discriminate FD patients from healthy subjects. Features that contributed substantially to the classification were finally identified as the classifying features. The results demonstrated that the classifier performed pretty well in discriminating FD patients. Namely, the accuracy of classification was 0.84 ± 0.03 in cross-validation set and 0.80 ± 0.07 in independent test set, respectively. A total of 15 connections between the subcortical nucleus (the thalamus and caudate) and sensorimotor cortex, parahippocampus, orbitofrontal cortex were finally determined as the classifying features. Furthermore, the results of cross-brain atlas validation showed that these classifying features were quite robust in the identification of FD patients. In summary, the current findings suggested the potential of using machine learning method and functional brain network biomarkers to identify FD patients.