Automatic brain categorization of discrete auditory emotion expressions
Seamlessly extracting emotional information from voices is crucial for efficient interpersonal communication. However, it remains unclear how the brain categorizes vocal expressions of emotion beyond the processing of their acoustic features. In our study, we developed a new approach combining electroencephalographic recordings (EEG) in humans with an oddball frequency tagging paradigm to automatically tag neural responses to specific emotion expressions. Participants were presented with a periodic stream of heterogeneous non-verbal emotional vocalizations belonging to five emotion categories (Anger, Disgust, Fear, Happiness, Sadness) at 2.5 Hz. Importantly, unbeknown to the participant, a specific emotion category appeared at an oddball presentation rate at 0.83 Hz that would elicit an additional response in the EEG spectrum only if the brain discriminates the target emotion category from other emotion categories and generalizes across heterogeneous exemplars of the target emotion category. Stimuli were matched across emotion categories for harmonicity-to-noise ratio, spectral center of gravity, pitch, envelope, and early auditory peripheral processing via the simulated output of the cochlea. Additionally, participants were presented with a scrambled version of the stimuli with identical spectral content and periodicity but disrupted intelligibility. We observed that in addition to the responses at the general presentation frequency (2.5 Hz) in both intact and scrambled sequences, a peak in the EEG spectrum at the oddball emotion presentation rate (0.83 Hz) and its harmonics emerged in the intact sequence only. The absence of response at the oddball frequency in the scrambled sequence in conjunction to our stimuli matching procedure suggests that the categorical brain response elicited by a specific emotion is at least partially independent from low-level acoustic features of the sounds. Further, different topographies were observed when fearful or happy sounds were presented as an oddball that supports the idea of different representations of distinct discrete emotions in the brain. Our paradigm revealed the ability of the brain to automatically categorize non-verbal vocal emotion expressions objectively (behavior-free), rapidly (in few minutes of recording time) and robustly (high signal-to-noise ratio), making it a useful tool to study vocal emotion processing and auditory categorization in general in populations where brain recordings are more challenging.