Temporal dynamics of reward processing revealed by magnetoencephalography

Temporal dynamics of reward processing revealed by magnetoencephalography.

Monetary gains and losses in gambling situations are associated with a distinct electroencephalographic signature: in the event-related potentials (ERPs), a mediofrontal feedback-related negativity (FRN) is seen for losses, whereas oscillatory activity shows a burst of in the θ-range for losses and in the β-range for gains. We used whole-head magnetoencephalography to pinpoint the magnetic coun...

Human visual processing as revealed by magnetoencephalography.

Reward Associations Reduce Behavioral Interference by Changing the Temporal Dynamics of Conflict Processing

Associating stimuli with the prospect of reward typically facilitates responses to those stimuli due to an enhancement of attentional and cognitive-control processes. Such reward-induced facilitation might be especially helpful when cognitive-control mechanisms are challenged, as when one must overcome interference from irrelevant inputs. Here, we investigated the neural dynamics of reward effe...

Synchronous dynamic brain networks revealed by magnetoencephalography.

We visualized synchronous dynamic brain networks by using prewhitened (stationary) magnetoencephalography signals. Data were acquired from 248 axial gradiometers while 10 subjects fixated on a spot of light for 45 s. After fitting an autoregressive integrative moving average model and taking the residuals, all pairwise, zero-lag, partial cross-correlations (PCC(ij)(0)) between the i and j senso...

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial ...