Improving audio-visual temporal perception through training enhances beta-band activity

Stephanie Theves, Jason S. Chan, Marcus J. Naumer, Jochen Kaiser
2019 NeuroImage  
Multisensory integration strongly depends on the temporal proximity between two inputs. In the audio-visual domain, stimulus pairs with delays up to a few hundred milliseconds can be perceived as simultaneous and integrated into a unified percept. Previous research has shown that the size of this temporal window of integration can be narrowed by feedback-guided training on an audio-visual simultaneity judgment task. Yet, it has remained uncertain how the neural network that processes
more » ... l asynchronies is affected by the training. In the present study, participants were trained on a 2-interval forced choice audio-visual simultaneity judgment task. We recorded their neural activity with magnetoencephalography in response to three different stimulus onset asynchronies (0 ms, each participant's individual binding window, 300 ms) before, and one day following training. The Individual Window stimulus onset asynchrony condition was derived by assessing each participant's point of subjective simultaneity. Training improved performance in both asynchronous stimulus onset conditions (300 ms, Individual Window). Furthermore, beta-band amplitude (12-30 Hz) increased from pre-compared to post-training sessions. This increase moved across central, parietal, and temporal sensors during the time window of 80-410 ms post-stimulus onset. Considering the putative role of beta oscillations in carrying feedback from higher to lower cortical areas, these findings suggest that enhanced top-down modulation of sensory processing is responsible for the improved temporal acuity after training. As beta oscillations can be assumed to also preferentially support neural communication over longer conduction delays, the widespread topography of our effect could indicate that training modulates not only processing within primary sensory cortex, but rather the communication within a large-scale network.
doi:10.1016/j.neuroimage.2019.116312 pmid:31669301 fatcat:shmslvmawbghfmizwwowh7536a