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People with schizophrenia show well-replicated deficits on tasks of explicit recognition of emotional prosody. However it remains unclear whether they are still sensitive to the implicit cues of emotional prosody, particularly when they exhibit high levels of social anhedonia. A dual processing model suggesting a dissociation between the neural networks involved in explicit and implicit recognition of emotional prosody has yet to be validated. 21 participants with schizophrenia and 21 controls were recruited. In the explicit recognition task, individuals listened to semantically neutral words pronounced with two different emotions and judged their emotional prosody. In the vocal emotional Stroop task, patients and controls listened to words with a positive or negative emotional valence pronounced with congruent or incongruent emotional prosody and judged their emotional content. Patients were also assessed with the Chapman Anhedonia Questionnaire and the Schizophrenic Communication Disorders scale. Individuals with schizophrenia were impaired in their explicit recognition of emotional prosody related to controls. In contrast, they showed a vocal emotional Stroop effect that was identical to controls for reaction time and greater for accuracy: patients were still sensitive to implicit emotional prosody. In addition the vocal emotional Stroop score increased with social anhedonia but was unrelated to communication disorders. Whereas explicit vocal affect recognition is impaired, implicit processing of emotional prosody seems to be preserved in schizophrenia. Our results provide evidence that at a behavioural level, the implicit and explicit processing of emotional prosody can be dissociated. Remediation of emotional prosody recognition in schizophrenia should target cognitive rather than sensory processes.
Laboratoire de Sciences Cognitives et Psycholinguistique, UMR 8554,CNRS-ENS-EHESS, 29 Rue d'Ulm, 75004 Paris, France; EA 4047, Université de Versailles-Saint Quentin, Centre Hospitalier de Versailles, 80 Bd de la Reine, 78000 Versailles, France.
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A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
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