I predict, therefore I do not hallucinate: a longitudinal study testing the neurophysiological underpinnings of auditory verbal hallucinations

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Fundação para a Ciência e a Tecnologia

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Attention to voices is increased in non-clinical auditory verbal hallucinations irrespective of salience

Publication . Castiajo, P; Pinheiro, Ana P.

2021-11Journal article

Restricted access

Emotional authenticity modulates affective and social trait inferences from voices

Publication . Pinheiro, Ana P.; Anikin, Andrey; Conde, Tatiana magro; Sarzedas, João; Chen, Sinead; Scott, Sophie K; Lima, César F.

The human voice is a primary tool for verbal and nonverbal communication. Studies on laughter emphasize a distinction between spontaneous laughter, which reflects a genuinely felt emotion, and volitional laughter, associated with more intentional communicative acts. Listeners can reliably differentiate the two. It remains unclear, however, if they can detect authenticity in other vocalizations, and whether authenticity determines the affective and social impressions that we form about others. Here, 137 participants listened to laughs and cries that could be spontaneous or volitional and rated them on authenticity, valence, arousal, trustworthiness and dominance. Bayesian mixed models indicated that listeners detect authenticity similarly well in laughter and crying. Speakers were also perceived to be more trustworthy, and in a higher arousal state, when their laughs and cries were spontaneous. Moreover, spontaneous laughs were evaluated as more positive than volitional ones, and we found that the same acoustic features predicted perceived authenticity and trustworthiness in laughter: high pitch, spectral variability and less voicing. For crying, associations between acoustic features and ratings were less reliable. These findings indicate that emotional authenticity shapes affective and social trait inferences from voices, and that the ability to detect authenticity in vocalizations is not limited to laughter. This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part I)’.

2021-12-20Journal article

Open access

Expectancy changes the self-monitoring of voice identity

Publication . Johnson, Joseph; Belyk, Michel; Schwartze, Michael; Pinheiro, Ana P.; Kotz, Sonja

Self-voice attribution can become difficult when voice characteristics are ambiguous, but functional magnetic resonance imaging (fMRI) investigations of such ambiguity are sparse. We utilized voice-morphing (self-other) to manipulate (un-)certainty in self-voice attribution in a button-press paradigm. This allowed investigating how levels of self-voice certainty alter brain activation in brain regions monitoring voice identity and unexpected changes in voice playback quality. FMRI results confirmed a self-voice suppression effect in the right anterior superior temporal gyrus (aSTG) when self-voice attribution was unambiguous. Although the right inferior frontal gyrus (IFG) was more active during a self-generated compared to a passively heard voice, the putative role of this region in detecting unexpected self-voice changes during the action was demonstrated only when hearing the voice of another speaker and not when attribution was uncertain. Further research on the link between right aSTG and IFG is required and may establish a threshold monitoring voice identity in action. The current results have implications for a better understanding of the altered experience of self-voice feedback in auditory verbal hallucinations.

2021-02-26Journal article

Open access

The perceived salience of vocal emotions is dampened in non-clinical auditory verbal hallucinations

Publication . Amorim, Maria; Roberto, Magda Sofia; Kotz, Sonja; Pinheiro, Ana P.

Introduction: Auditory verbal hallucinations (AVH) are a cardinal symptom of schizophrenia but are also reported in the general population without need for psychiatric care. Previous evidence suggests that AVH may reflect an imbalance of prior expectation and sensory information, and that altered salience processing is characteristic of both psychotic and non-clinical voice hearers. However, it remains to be shown how such an imbalance affects the categorisation of vocal emotions in perceptual ambiguity. Methods: Neutral and emotional nonverbal vocalisations were morphed along two continua differing in valence (anger; pleasure), each including 11 morphing steps at intervals of 10%. College students (N = 234) differing in AVH proneness (measured with the Launay-Slade Hallucination Scale) evaluated the emotional quality of the vocalisations. Results: Increased AVH proneness was associated with more frequent categorisation of ambiguous vocalisations as ‘neutral’, irrespective of valence. Similarly, the perceptual boundary for emotional classification was shifted by AVH proneness: participants needed more emotional information to categorise a voice as emotional. Conclusions: These findings suggest that emotional salience in vocalisations is dampened as a function of increased AVH proneness. This could be related to changes in the acoustic representations of emotions or reflect top-down expectations of less salient information in the social environment.

2021-07-14Journal article

Open access

From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

Publication . Barros, Carla; Roach, Brian; Ford, Judith M.; Pinheiro, Ana P.; Silva, Carlos

Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

2022-02-17Journal article

Open access