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Predicting Sequential Learning from Oscillatory Activity in Human MEG-Data


Sequence learning has been centrally involved in the acquisition of many different forms of behavior including language, visual object recognition and motor learning. The neurobiological and cognitive mechanisms associated with the knowledge of sequences, however, remain only partially understood. Findings from experimental psychology and computational models indicate that statistical learning rules may be a powerful mechanism to sequence and categorize information in our environment. One important question that remains to be addressed, however, is how sequential contingencies are encoded, consolidated and retrieved in memory. In addition, it remains unclear if statistical learning is a domain general or modality specific. One candidate mechanism that may allow addressing both of these issues, are neuronal oscillations. Previous electrophysiological studies have established a link between oscillatory activity at theta (5-7H) and gamma (30-100Hz) frequencies and the representation of behavioral events in memory circuits, suggesting that theta and gamma oscillations may support the processing of sequenced information in memory circuits. However, an open question is to what extent this mechanism is related with the learning of sequential information and whether it occurs in different sensory modalities. The present proposal aims to investigate the impact of theta and gamma oscillations during the encoding, consolidation and retrieval of sequential information in memory circuits by recording magneto-encephalographical (MEG) activity in healthy adult human participants performing a statistical learning task in the auditory and visual domain. The outcome of this project is expected to have important implications for our understanding of how fundamental behavioral skills such as language are acquired.

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Type d’activité
Research Organisations
Contact administratif
Ana Fernández (Ms.)
Contribution de l’UE
€ 166 336,20