1. Introduction

The relationship between emotions and physiological responses has garnered considerable attention across multiple disciplines, including psychology, neuroscience, and medicine. Emotions are fundamental to human experience, influencing decision-making, behavior, and social interactions. From everyday stressors to complex emotional states like anxiety and joy, emotions shape both our internal physiological landscape and external responses to the world. Understanding the underlying mechanisms that link physiological signals to emotional experiences is not only of academic interest but also holds significant implications for mental health treatments, human-computer interaction, and biofeedback technology.

Recent advancements in measuring emotions through physiological signals have been well-documented in contemporary literature. For instance, Saganowski et al. provide a systematic overview of the use of wearables to measure emotions in everyday life [1], utilizing a variety of physiological indicators such as heart rate variability (HRV), electroencephalography (EEG), and electrodermal activity (EDA). These methods offer valuable insights into emotional states by tracking autonomic nervous system responses and neural activities, enabling more accurate emotion detection. Despite the progress, challenges remain in terms of generalizability across individuals and contexts, as well as in the refinement of algorithms that can reliably map physiological signals to discrete emotional states.

When it comes to the relationship between physiological responses and emotional experiences, the literature reflects diverse theoretical perspectives. Pace-Schott et al. review how physiological sensations often accompany emotional experiences [2], offering insights into the bidirectional nature of these processes. In line with the James-Lange theory, physiological changes can initiate emotional states, such as increased heart rate leading to feelings of anxiety. Conversely, the Cannon-Bard theory proposes that emotions and physiological responses occur simultaneously but independently. Other models posit that emotions arise from the brain's interpretation of physiological changes in context, while more recent work highlights the dynamic and reciprocal interaction between emotions and physiological signals. Despite ongoing debate, the complexity and variability in these processes underscore the need for further empirical research to elucidate these relationships fully.

Given the growing interest in understanding the relationship between emotions and physiological responses, it is essential to synthesize the existing literature to clarify how these processes are interlinked. The current research aims to accomplish two key objectives. First, it will summarize the current methodologies employed in measuring emotions via physiological responses, offering a critical evaluation of their strengths and limitations. Second, it will examine the established links between physiological and emotional responses, providing an analysis of how these interactions have been studied across various emotional states. By viewing these topics, this research seeks to contribute to a more integrated understanding of the dynamic interplay between physiological processes and emotions, ultimately guiding future research in this field.

2. Measurement methods of emotions using physiological signals

2.1. HRV

HRV is a widely used metric for assessing emotional states through the analysis of time intervals between consecutive heartbeats. This measure reflects the balance between the sympathetic and parasympathetic branches of the autonomic nervous system (ANS), which are closely linked to emotional regulation. HRV has been extensively applied in scenarios such as stress detection, anxiety monitoring, and relaxation studies. Its integration into wearable devices makes HRV particularly useful for real-time emotion monitoring in natural settings [3, 4].

HRV is known for its sensitivity to emotional changes, offering reliable insights into stress, arousal, and relaxation. Its ease of integration into wearable devices further enhances its applicability for continuous and non-invasive emotion monitoring [3]. However, HRV’s effectiveness can be compromised by individual differences, as it varies significantly across populations. Additionally, HRV is highly context-sensitive, with factors such as physical activity and environmental conditions potentially confounding its interpretation [4].

2.2. EEG

EEG is a technique that measures the brain's electrical activity, capturing neural oscillations associated with various emotional states. By placing electrodes on the scalp, EEG records brain waves that are closely tied to cognitive and emotional processes. This method is extensively used in affective computing, gaming, and research on emotional responses to stimuli, making it a valuable tool for real-time emotion recognition systems [5, 6].

EEG’s high temporal resolution allows for precise tracking of rapid changes in brain activity, providing detailed insights into the neural basis of emotions. This method is particularly effective in real-time applications where understanding emotional responses is critical [5]. The main drawback of EEG lies in its limited spatial resolution, which makes it challenging to identify the specific brain regions responsible for particular emotional states. EEG data interpretation is complex, requiring sophisticated algorithms and expertise to accurately link brain activity with emotions [6].

2.3. EDA

EDA measures skin conductance levels, which fluctuate with sweat gland activity linked to the sympathetic nervous system’s response to emotional arousal. EDA is particularly useful in stress and anxiety research, as well as in lie detection and emotion recognition systems. It is most effective in assessing the intensity of emotional arousal and autonomic nervous system activation [3, 4].

EDA offers a direct measurement of emotional arousal, making it a reliable indicator of emotional intensity. The simplicity of EDA sensors allows for their integration into wearable devices, enabling continuous monitoring in various environments [3]. However, EDA’s sensitivity to environmental factors, such as temperature and humidity, can affect skin conductance and potentially confound results. EDA primarily measures arousal, making it difficult to differentiate between emotions with similar arousal levels [4].

Table 1. A summary of each method.

2.4. Facial electromyography (EMG)

Facial electromyography (EMG) involves measuring electrical activity in facial muscles, which is closely linked to emotional expressions. This method is particularly effective in detecting micro-expressions that are often not visible to the naked eye. Facial EMG is used in research on emotional expressions, human-computer interaction, and facial recognition systems, offering detailed insights into the subtle dynamics of emotional responses [3, 6].

Facial EMG’s high sensitivity allows it to detect even the smallest facial muscle movements, providing a detailed analysis of emotional expressions. When combined with other physiological measures like EEG, facial EMG enhances the accuracy of emotion recognition systems [6]. The main limitation of facial EMG is its intrusive nature, as it requires the placement of electrodes on the face, which can influence the naturalness of emotional expressions. Additionally, interpreting facial EMG data can be complex and requires advanced processing techniques [3].

2.5. Multimodal approaches

Multimodal approaches combine multiple physiological and behavioral indicators, such as HRV, EEG, EDA, and facial EMG, to improve the accuracy and reliability of emotion recognition. As shown in Table 1, these methods leverage the strengths of individual signals while compensating for their limitations. Multimodal systems are employed in advanced emotion recognition applications, including autonomous vehicles, gaming, and affective computing, providing a comprehensive view of emotional states by integrating data from various sources [6, 7].

Multimodal approaches enhance the accuracy of emotion recognition by reducing the ambiguity of individual signals. They are more robust to variations in individual responses and environmental conditions, making them suitable for a wide range of applications [7]. However, the complexity of multimodal systems requires the integration and synchronization of multiple data streams, which can be technically challenging and resource-intensive. The use of multiple sensors and advanced processing algorithms can also increase the cost and complexity of these systems, limiting their accessibility [6].

Figure 1. A summary of each theory.

3. Theoretical perspectives on the relationship between emotions and physiological responses

3.1. Physiological responses triggering emotional experiences (James-Lange theory)

The James-Lange theory posits that emotional experiences are a direct consequence of physiological responses to external stimuli. According to this theory (Figure 1), the body reacts to a stimulus with physiological changes-such as an increased heart rate or sweating-and the brain subsequently interprets these changes as a specific emotion, such as fear or excitement [8]. This sequential relationship implies that without the physiological response, the emotional experience would not occur.

Empirical studies have provided support for this theory, showing that artificially inducing physiological states can lead to corresponding emotional experiences [9]. For example, when participants were subjected to induced tachycardia, they often reported feelings of anxiety, even in the absence of a stressor. This evidence aligns with the idea that physiological responses are primary triggers in the formation of emotions.

However, the James-Lange theory has also been criticized for oversimplifying the complexity of emotional experiences. For instance, this theory is critiqued by highlighting the role of higher cognitive processes in emotional experiences, suggesting that emotions are not merely the result of physiological responses but also involve complex cognitive evaluations and interpretations [10]. This critique underscores the limitations of the James-Lange theory in fully capturing the intricate nature of emotional experiences.

3.2. Emotional experiences triggering physiological responses

In contrast to the James-Lange theory, the idea that emotional experiences can trigger physiological responses suggests that the experience of emotion itself can be the primary driver of physiological changes. This perspective emphasizes that emotional processing in the brain can lead to direct physiological effects. Emotional stimuli could elicit physiological changes such as increased heart rate and sweating [8, 11]. These studies suggest that emotional experiences can activate the autonomic nervous system, leading to physiological changes even in the absence of a preceding physiological trigger (Figure 1).

Nakamura et al. also support this perspective by showing that emotional processing in the amygdala can directly influence physiological responses [12]. Their findings suggest that emotional experiences themselves are powerful enough to initiate changes in bodily states, thereby reinforcing the idea that emotions can trigger physiological responses.

3.3. Simultaneous origin of emotions and physiological responses (Cannon-Bard theory)

The Cannon-Bard theory proposes that emotional experiences and physiological responses occur simultaneously and independently in response to a stimulus. According to this theory (Figure 1), when a person encounters an emotionally charged situation, the brain processes the stimulus and concurrently triggers both the subjective experience of the emotion and the corresponding physiological response [13].

Seeing a snake might simultaneously cause a person to feel fear and experience an accelerated heart rate, but these responses are independent rather than sequential. Research supports this theory by demonstrating that emotional stimuli can evoke both subjective feelings and physiological changes without one causing the other [14]. This simultaneous activation has been observed in studies involving brain imaging, where regions such as the amygdala play a critical role in both emotional and physiological responses, further supporting the Cannon-Bard theory. Bigot et al. highlight the role of parallel processing in the brain, where emotional and physiological responses are initiated simultaneously by shared stimuli [15].

3.4. Complex interactions between emotions and physiological responses

Another perspective is that emotions and physiological responses are dynamically interconnected, influencing each other in a bidirectional and complex manner. This theory posits that emotions can modulate physiological responses and vice versa, creating a feedback loop that evolves over time (Figure 1). Jerath and Beveridge discuss how conscious efforts to alter physiological states [16], such as through deep breathing exercises, can reduce anxiety, suggesting that physiological changes can actively shape emotional experiences. Conversely, chronic emotional states [17], such as prolonged stress, can lead to persistent physiological changes, which can further reinforce the emotional state and contribute to a cycle of negative emotional and physiological health.

Quadt et al. also support this theory by demonstrating the complex [18], bidirectional relationship between emotions and physiological responses. Their research indicates that these interactions are not linear but involve multiple feedback loops, highlighting the intricate nature of the emotion-physiology relationship.

3.5. The role of awareness and interpretation in linking physiology and emotion

Some theories emphasize the role of cognitive interpretation and awareness in shaping the relationship between physiological states and emotions. The Schachter-Singer two-factor theory, for example, suggests that the same physiological response can lead to different emotions depending on how the individual interprets the situation [19].

This theory posits that emotional experiences result from a combination of physiological arousal and cognitive interpretation (Figure 1). Empirical studies have demonstrated that participants who experienced similar physiological arousal reported different emotions depending on the context or information provided, supporting the idea that cognition plays a critical role in emotional experience [20]. Idrees highlights how individuals’ awareness and interpretation of their physiological states can significantly influence their emotional outcomes. This suggests that cognitive processes are integral to the emotion-physiology relationship, as the brain's interpretation of bodily states can shape the emotional experience.

4. Conclusion

This research has explored the intricate relationship between emotions and physiological responses, focusing on the methodologies for emotion measurement and the theoretical perspectives on how these processes interact. Theoretical models, including the James-Lange and Cannon-Bard theories, along with more recent approaches emphasizing complex interactions and cognitive interpretations, underscore the multifaceted nature of this relationship.

The insights gained from this research highlight the significance of integrating physiological measurements into the study of emotions, offering potential applications in mental health, human-computer interaction, and biofeedback technologies. By deepening our understanding of the bidirectional and dynamic interactions between emotions and physiological responses, this research contributes to advancing both theoretical knowledge and practical applications in the field. Despite these advancements, the research field still faces substantial gaps. There is no definitive conclusion on the precise mechanisms governing the emotion-physiology relationship. The variability in individual responses, the influence of external factors, and the complexity of these interactions continue to challenge researchers. This underscores the need for more comprehensive and longitudinal studies to further elucidate these dynamics. Future research should focus on refining existing measurement techniques and developing more models for variability across individuals and contexts. Additionally, interdisciplinary efforts are needed to explore how these physiological-emotional interactions can be leveraged for therapeutic interventions and enhancing user experiences in technology-driven environments. By addressing these challenges, future studies can provide a more comprehensive understanding of how emotions and physiological responses are intertwined, ultimately benefiting both scientific inquiry and practical applications.