Objectives. Multiple facets of human emotions underlie diverse and sparse neural mechanisms. Amongst many models of emotions, the circumplex model of emotion is one of a significant theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expression of one's internal mental state still lacks to consider another, the third dimension of time. Here, we report an exploratory attempt to build a three-axial model of human emotion to model our sense of anticipatory excitement, "Waku-Waku (in Japanese)," when people are predictively coding upcoming emotional events.Approach. Electroencephalography (EEG) was recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of valence of an upcoming picture, either positive, negative, or unknown. While seeing an image, participants judged its emotional valence during the task, and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes.Main Results. A three axial model was built to quantify Waku-Waku. As was expected, this model revealed considerable contribution of the third dimension over the classical twodimension model. Distinctive EEG components were identified. Furthermore, a novel brainemotion interface is proposed and validated within the scope of limitations.Significance. The proposed notion may shed new light on the theories of emotion and supports multiplex dimensions of emotion. With an introduction of the cognitive domain for a braincomputer-interface, we propose a novel brain-emotion-interface. Limitations and potential applications are discussed.

The value of an item is learned through the decision-making sequence. The learning process has been investigated separately in the contexts of internally guided decision-making (IDM, e.g., preference judgment) and externally guided decision-making (EDM, e.g., gambling task). Regarding EDM, learning processes of item values have been explained by reinforcement learning theory. The amplitude of feedback-related negativity (FRN) is known to reflect prediction error, which modulates the degree of value updating. Recently, as with the EDM, the reinforcement learning-like mechanism is thought to explain value updating in IDM (choice-induced preference change: CIPC). This study used the blind choice paradigm to investigate whether the FRN is associated with CIPC, or not. In this paradigm, participants blindly choose the more preferred one form the two equally preferred items, and then feedback indicating the chosen item. Results showed that the FRN-like component was observed but not related to CIPC. These results suggest that the FRN-like component does not reflect the degree of value updating but reflects a participant s estimation about how much their preference is reflected in the feedback.

Objective. Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one’s internal mental state still lacks a notion of the third dimension of time. Here, we report an exploratory attempt to build a three-axis model of human emotion to model our sense of anticipatory excitement, ‘Waku-Waku’ (in Japanese), in which people predictively code upcoming emotional events. Approach. Electroencephalography (EEG) data were recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of the valence of an upcoming picture: positive, negative, or unknown. While seeing an image, the participants judged its emotional valence during the task and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes. Main results. A three-axis model was built to quantify Waku-Waku. As expected, this model revealed the considerable contribution of the third dimension over the classical two-dimensional model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface was proposed and validated within the scope of limitations. Significance. The proposed notion may shed new light on the theories of emotion and support multiplex dimensions of emotion. With the introduction of the cognitive domain for a brain-computer interface, we propose a novel brain-emotion interface. Limitations of the study and potential applications of this interface are discussed.

Recent studies highlight interoception as the key to processing healthy emotion and pathophysiology of affective disorders such as major depressive disorder (MDD). It has been reported that interoceptive responses are impaired in MDD and in healthy individuals with a high depressive risk (HDR). However, it is unclear how individual differences in HDR relate to neurophysiological underpinnings for interoceptive and emotional reactions under different degrees of certainty. We examined whether HDR mediates the relationship between a neuro-physiological marker for interoception, heartbeat-evoked potential (HEP), and an index for cardiac reactivity, heart rate (HR) or heart rate variability (HRV). In a concurrent EEG-ECG experiment, 26 healthy participants underwent an emotion-evoking picture evaluation task. One of three differential auditory tones associated with a level of certainty preceded display of a pleasant or unpleasant picture. The results showed attenuated HRV activity for certain cues in HDR individuals. Neural and physiological reactions to uncertain, unpleasant pictures were enhanced by the depressive risk. These results suggest reduced responses in prediction processing and enlarged precision-weighted prediction error for unpleasant pictures. Finally, HDR significantly mediated the HEP and HR relationship for unexpected unpleasant stimuli. Our study thus provides evidence that interoceptive predictive coding in the cardiac domain is altered by HDR, and suggests that investigating the heart-brain interaction related to predictive coding will offer insights into how to estimate the level of depressive risk.

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