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Sensory Entrainment for Improving Spatial Navigation

Sensory Entrainment for Improving Spatial Navigation
The objective of this Project is to proof the effectiveness of rhythmic Theta sensory stimulation to improve SN abilities in realistic scenarios. The aim is to reduce navigation errors and/or speed up path learning time. Our long term vision is...

The objective of this Project is to proof the effectiveness of rhythmic Theta sensory stimulation to improve SN abilities in realistic scenarios. The aim is to reduce navigation errors and/or speed up path learning time. Our long term vision is the application of the findings in interventions to foster learning and the preservation of spatial cognition with an impact in real life. We will evaluate the effects of Theta entrainment in short- and long-term memory for trajectories, compared to other entrainment conditions: we will assess (a) the effectiveness in encoding new trajectories (encoding), and (b) elapsed memory recall for already learnt trajectories (maintenance).
We will follow an experimental protocol using of T-junction mazes in realistic scenarios (e.g., city environments) generated in virtual reality, which have proven particularly relevant to measure accuracy in human SN. EEG neuroimaging will be used to verify that sensory stimulation results in entrainment of brain oscillations, and to evaluate synchrony between sensory areas.
We hypothesize that entrainment of functionally-relevant Theta brain oscillations improves encoding and maintenance of sequences of events necessary for solving SN tasks, resulting in behavioural benefits (lower error rate, shorter learning). We anticipate behavioural benefits for rhythmic, compared to arrhythmic entrainment, and expect the effect to generalize across entrainment modalities. Furthermore, we will test whether audiovisual entrainment further facilitates encoding and maintenance, via increased synchronization between sensory areas.
Finally, we expect that the short-term memory facilitation in encoding and maintenance will result in the consolidation of the learnt trajectories, which will still be measurable days after learning.
 
 BIAL Foundation, ref: 229-2020