The ability to anticipate upcoming situations avoids us from being overwhelmed by the vast number of stimuli we experience in our perceptual world. Predictions are vast and various, involving cognitive, motor, and sensory computations. Some specific predictions of particular interest for the current work help us distinguish between stimuli generated by ourselves from environmentally caused stimuli. Numerous studies show that differences in the amplitude of N1 and P2, two ERP components in the EEG signal, appear to reflect the distinction between these types of stimuli. Nevertheless, predictions are not always reliable, but sometimes they fail to represent the upcoming situation. An update of the cognitive models from which they arise is fundamental to be capable of not making the same error in the future. This holds possible through the formation of prediction errors; like the word itself implies, such signals bring the error back to the areas of interest to allow a revision of the cognitive model. This pilot study aims to analyze the ERP variations linked to prediction errors after self-generated actions when the expectations of the subject are unfulfilled.
The ability to anticipate upcoming situations avoids us from being overwhelmed by the vast number of stimuli we experience in our perceptual world. Predictions are vast and various, involving cognitive, motor, and sensory computations. Some specific predictions of particular interest for the current work help us distinguish between stimuli generated by ourselves from environmentally caused stimuli. Numerous studies show that differences in the amplitude of N1 and P2, two ERP components in the EEG signal, appear to reflect the distinction between these types of stimuli. Nevertheless, predictions are not always reliable, but sometimes they fail to represent the upcoming situation. An update of the cognitive models from which they arise is fundamental to be capable of not making the same error in the future. This holds possible through the formation of prediction errors; like the word itself implies, such signals bring the error back to the areas of interest to allow a revision of the cognitive model. This pilot study aims to analyze the ERP variations linked to prediction errors after self-generated actions when the expectations of the subject are unfulfilled.
When expectations do not reflect reality: do event-related-potential amplitudes for self-generated sounds reflect auditory prediction errors?
MOLOGNI, VALENTINA
2021/2022
Abstract
The ability to anticipate upcoming situations avoids us from being overwhelmed by the vast number of stimuli we experience in our perceptual world. Predictions are vast and various, involving cognitive, motor, and sensory computations. Some specific predictions of particular interest for the current work help us distinguish between stimuli generated by ourselves from environmentally caused stimuli. Numerous studies show that differences in the amplitude of N1 and P2, two ERP components in the EEG signal, appear to reflect the distinction between these types of stimuli. Nevertheless, predictions are not always reliable, but sometimes they fail to represent the upcoming situation. An update of the cognitive models from which they arise is fundamental to be capable of not making the same error in the future. This holds possible through the formation of prediction errors; like the word itself implies, such signals bring the error back to the areas of interest to allow a revision of the cognitive model. This pilot study aims to analyze the ERP variations linked to prediction errors after self-generated actions when the expectations of the subject are unfulfilled.File | Dimensione | Formato | |
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Valentina Mologni - Final Dissertation.pdf
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https://hdl.handle.net/20.500.12608/37269