Vagal Tone Differences in Empathy Level Elicited by Different Emotions and a Co-Viewer

The physiological measurement and evaluation of empathy of video content

The COVID-19 pandemic has led to a surge in video content consumption, but measuring viewers' empathy towards the content has been limited to subjective evaluations or attached physiological apparatus. In this study, we introduced a novel non-contact physiological method for measuring empathy towards video content by assessing the synchronization of facial micromovements between the subject and object (i.e., person) within the media. We recorded facial micromovements and heart rate variability (HRV) remotely using a camera while 62 subjects watched one video each, designed and validated to elicit one of four two-dimensional emotions: pleasant-aroused, pleasant-relaxed, unpleasant-aroused, and unpleasant-relaxed. We also collected the subjects' self-assessed emotions and empathy using a questionnaire. The results confirmed that the stimuli effectively induced the intended arousal in the subjects, as evidenced by both self-reported emotions and HRV responses that suggested higher arousal was associated with stronger activity in the sympathetic nervous system. A closer examination of HRV indicators, such as SDNN and Total Power values, showed an amplification during the unpleasant state. We interpret this as the body's dynamic response to stressors, underlining the autonomic nervous system's proactive role in responding to such stimuli. In a broader context, our results emphasized that while subjects showcased augmented empathy during aroused conditions, the introduction of stressors, represented by unpleasant content, led to a dampening of this empathetic response. This findings demonstrate the potential of non-contact physiological methods for measuring empathy toward video content.

Empathy of Marine Aquaculture on Aquatic Products Consumption

The prolonged COVID-19 has caused a global lockdown and greatly impacts the supply chain and consumers' consumption behavior of aquatic products. Specifically, consumers' increasing awareness of environmental protection drives the marine aquaculture enterprises to supply green products and establish empathy with consumers in a bid to achieve sustainable consumption. This paper conducts a study on the international green marketing paths of marine aquatic products through PLS-SEM analysis of the 407 valid samples collected from the questionnaire survey. The findings indicate that empathy marketing has a significant positive effect on consumers' purchase intention; subjective norm has a significant positive effect on consumers' purchase intention. Additionally, perceived behavior control has a significant mediating effect between consumers' subjective norm and purchase intention; the consumption environment has a significant positive moderating effect on individual consumption intention. The study suggests that sustainable development can be further propelled by means of coordinating the consumers' awareness of ecological environment protection and their enhanced consumption intention.

Non-Contact Measurement of Empathy Based on Micro-Movement Synchronization

Tracking consumer empathy is one of the biggest challenges for advertisers. Although numerous studies have shown that consumers’ empathy affects purchasing, there are few quantitative and unobtrusive methods for assessing whether the viewer is sharing congruent emotions with the advertisement. This study suggested a non-contact method for measuring empathy by evaluating the synchronization of micro-movements between consumers and people within the media. Thirty participants viewed 24 advertisements classified as either empathy or non-empathy advertisements. For each viewing, we recorded the facial data and subjective empathy scores. We recorded the facial micro-movements, which reflect the ballistocardiography (BCG) motion, through the carotid artery remotely using a camera without any sensory attachment to the participant. Synchronization in cardiovascular measures (e.g., heart rate) is known to indicate higher levels of empathy. We found that through cross-entropy analysis, the more similar the micro-movements between the participant and the person in the advertisement, the higher the participant’s empathy scores for the advertisement. The study suggests that non-contact BCG methods can be utilized in cases where sensor attachment is ineffective (e.g., measuring empathy between the viewer and the media content) and can be a complementary method to subjective empathy scales.

An Empathy Evaluation System Using Spectrogram Image Features of Audio

Watching videos online has become part of a relaxed lifestyle. The music in videos has a sensitive influence on human emotions, perception, and imaginations, which can make people feel relaxed or sad, and so on. Therefore, it is particularly important for people who make advertising videos to understand the relationship between the physical elements of music and empathy characteristics. The purpose of this paper is to analyze the music features in an advertising video and extract the music features that make people empathize. This paper combines both methods of the power spectrum of MFCC and image RGB analysis to find the audio feature vector. In spectral analysis, the eigenvectors obtained in the analysis process range from blue (low range) to green (medium range) to red (high range). The machine learning random forest classifier is used to classify the data obtained by machine learning, and the trained model is used to monitor the development of an advertisement empathy system in real time. The result is that the optimal model is obtained with the training accuracy result of 99.173% and a test accuracy of 86.171%, which can be deemed as correct by comparing the three models of audio feature value analysis. The contribution of this study can be summarized as follows: (1) the low-frequency and high-amplitude audio in the video is more likely to resonate than the high-frequency and high-amplitude audio; (2) it is found that frequency and audio amplitude are important attributes for describing waveforms by observing the characteristics of the machine learning classifier; (3) a new audio extraction method is proposed to induce human empathy. That is, the feature value extracted by the method of spectrogram image features of audio has the most ability to arouse human empathy.