Ana P. Pinheiro

The self-voice plays a fundamental role in communication and identity yet remains a relatively neglected topic in psychological science. As AI-generated and digitally manipulated voices become more common, understanding how individuals perceive and process their own voice is increasingly important. Disruptions in self-voice processing are implicated in several clinical conditions, including psychosis, autism, and personality disorders, highlighting the need for integrative models to explain the self-voice across contexts. However, research faces two major challenges: a methodological one (i.e., replicating the bone-conducted acoustics that shape natural self-voice perception) and a conceptual one (i.e., a persistent bias toward treating the self-voice as purely auditory). To address these gaps, we propose a framework that decomposes the self-voice into five interacting components: auditory, motor control, memory, multisensory integration, and self-concept. We review the functional and neural basis of each component and suggest how they converge within distributed brain networks to support coherent self-voice processing. This integrative framework aims to advance theoretical and translational work by bridging psychology, neuroscience, clinical research, and voice technology in the context of emerging digital voice environments.

The self-voice plays a fundamental role in communication and identity, yet remains a relatively neglected topic in psychological science. As AI-generated and digitally manipulated voices become more common, understanding how individuals perceive and process their own voice is increasingly important. Disruptions in self-voice processing are implicated in several clinical conditions, including psychosis, autism, and personality disorders, highlighting the need for integrative models to explain self-voice across contexts. However, research faces two major challenges: a methodological one – replicating the bone-conducted acoustics that shape natural self-voice perception, and a conceptual one – a persistent bias toward treating the self-voice as purely auditory. To address these gaps, we propose a framework decomposing the self-voice into five interacting components: auditory, motor, memory, multisensory integration, and self-concept. We review the functional and neural basis of each component and suggest how they converge within distributed brain networks to support coherent self-voice processing. This integrative framework aims to advance theoretical and translational work by bridging psychology, neuroscience, clinical research, and voice technology in the context of emerging digital voice environments.

Synthetic voices can now achieve remarkable acoustic accuracy, yet often fail to sound “natural,” especially when designed to reproduce one's own voice. Existing frameworks define naturalness along two dimensions: deviation from acoustic norms and human-likeness. Yet these dimensions overlook the self-voice, which can feel natural or unnatural for reasons beyond the signal itself. Here, self-relevance is proposed as a complementary dimension, capturing the subjective alignment between a voice and the listener's self-representation. Evidence shows that self-relevance modulates perceived naturalness independently of acoustic match. A full understanding of voice naturalness, therefore, requires integrating physical speech properties with the listener's self-representational framework.

Speech production relies on a forward model that predicts the sensory consequences of one’s vocal actions. When auditory feedback matches these predictions, event-related potentials (ERPs) of the electroencephalogram (EEG) are attenuated, supporting two partly overlapping experiences: agency (the sense of causing the action) and ownership (the sense that the voice belongs to oneself). Whether early sensory attenuation effects dissociate these experiences is unknown.We recorded EEG from 50 participants during a voice playback task. Agency was manipulated by contrasting self-chosen (self-generated) and unchosen (other-generated) words; ownership contrasted the participant’s own voice and the experimenter’s voice. Sensory attenuation of the N1 and P2 ERPs was measured, and their cortical generators were source-localized with eLORETA.N1 amplitude was selectively modulated by ownership, whereas the P2 indexed agency, suggesting that ownership precedes and subsequently impacts agency. Source localization revealed partially dissociable networks: ownership engaged a fronto-temporal network, including the insula, whereas agency recruited inferior parietal and somatosensory regions.These findings reveal a temporal and spatial dissociation between voice agency and ownership. They further suggest that the forward model integrates both sensorimotor predictions and memory-based representations to support a coherent sense of self in vocal communication.

The voice is a powerful social signal and a primary channel for communicating emotions when speakers are out of view. When we hear an emotional voice, we quickly form an impression of the person behind it. Neurocognitive models emphasize the multi-step dynamic operations that occur when listeners decode emotional information from vocal sounds. However, these models have primarily focused on stimulus quality, often neglecting the perception of other relevant person characteristics (e.g., gender, age, personal identity) evolving on different timescales. How do the emerging details about the speaker affect how listeners decode emotional information? And how are these different types of information integrated into a comprehensive impression of the speaker? This review examines recent data highlighting multiple stages of vocal expression analysis and the interplay between distinct types of nonverbal information revealed in the speaker’s voice. It serves as a starting point for broader research examining how distinct person characteristics, perceived simultaneously or in close succession, interact and affect the decoding of vocal emotions.

The voice is a powerful social signal and a primary channel for communicating emotions when speakers are out of view. When we hear an emotional voice, we quickly form an impression of the person behind it. Neurocognitive models emphasize the multi-step dynamic operations that occur when listeners decode emotional information from vocal sounds. However, these models have primarily focused on stimulus quality, often neglecting the perception of other relevant person characteristics (e.g., gender, age, personal identity) evolving on different timescales. How do the emerging details about the speaker affect how listeners decode emotional information? And how are these different types of information integrated into a comprehensive impression of the speaker? This review examines recent data highlighting multiple stages of vocal expression analysis and the interplay between distinct types of nonverbal information revealed in the speaker’s voice. It serves as a starting point for broader research examining how distinct person characteristics, perceived simultaneously or in close succession, interact and affect the decoding of vocal emotions.

Time perception is inherently subjective and malleable. We experience a wide range of time scales, from less than a second to decades. In addition, our perception of time can be affected by our attentional and emotional states. Previous psychological and neuroimaging studies have used several paradigms and methods to probe factors that influence time perception. Considering these factors facilitates approaches to improve time management and to enhance sensory experiences. This Collection of time perception studies includes reports that focus on stimulus property, physiological state, cross-modal interaction, attention, learning, age, and environment. These findings help to illuminate the complex mechanisms of time perception.