The representation of emotional experience from imagined scenarios.

Abstract

One of the key unresolved issues in affective science is understanding how the subjective experience of emotion is structured. Semantic space theory has shed new light on this debate by applying computational methods to high-dimensional data sets containing self-report ratings of emotional responses to visual and auditory stimuli. We extend this approach here to the emotional experience induced by imagined scenarios. Participants chose at least one emotion category label among 34 options or provided ratings on 14 affective dimensions while imagining two-sentence hypothetical scenarios. A total of 883 scenarios were rated by at least 11 different raters on categorical or dimensional qualities, with a total of 796 participants contributing to the final normed stimulus set. Principal component analysis reduced the categorical data to 24 distinct varieties of reported experience, while cluster visualization indicated a blended, rather than discrete, distribution of the corresponding emotion space. Canonical correlation analysis between the categorical and dimensional data further indicated that category endorsement accounted for more variance in dimensional ratings than vice versa, with 10 canonical variates unifying change in category loadings with affective dimensions such as valence, arousal, safety, and commitment. These findings indicate that self-reported emotional responses to imaginative experiences exhibit a clustered structure, although clusters are separated by fuzzy boundaries, and variable dimensional properties associate with smooth gradients of change in categorical judgments. The resultant structure supports the tenets of semantic space theory and demonstrates some consistency with prior work using different emotional stimuli.

Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition

“…Although the direct connection to music might not be as apparent as in other brain areas, it is still worth further exploration. In our study, the results of the ECA module revealed the importance of high-frequency bands (beta and gamma) in distinguishing music composition states, similar to findings in emotional recognition research [ 45 , 49 , 50 ], where high-frequency bands performed slightly better than low-frequency bands (theta and alpha). This result emphasizes the close association between music and emotions, as well as the significant driving role of emotions in music composition and experience.…”

Self-Relevance Moderates the Relationship between Depressive Symptoms and Corrugator Activity during the Imagination of Personal Episodic Events

“…These stimuli have been shown to elicit discrete affective experiences that parallel the emotional space of other modalities (e.g., movies, music, facial expressions, etc.) while also facilitating more self-relevant emotional states [ 46 ]. For the present study, we selected 45 scenarios based on normative affect ratings, such that the stimulus set consisted of 15 happy, 15 neutral, and 15 sad scenarios.…”

Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain

“…Despite substantial advances in the study of the interplay between perception and emotion, the majority of research has focused on unimodal emotional stimuli in typically developed individuals only [e.g., ( 21 , 22 )] making it challenging to discern whether and where in the brain emotional instances are represented using an abstract, rather than a sensory-dependent, code. In people with typical development, a fearful scream is likely to give rise to the mental imagery of someone in the act of screaming, and they are able to depict in their mind a specific facial expression and body posture, perhaps even a certain context, with a rich and dynamic representation close to what is commonly experienced in daily life ( 23 , 24 ). Also, this process of mental imagery is associated with a specific pattern of brain activity in relation to the different sensory channels [see, for instance, ( 25 , 26 )].…”