Keywords
Attention, EEG, Capture, N2pc, Pd
Abstract
The Reactive-Convergent Gradient Field model (R-CGF) is a unique approach to modeling spatial attention in that it links neural mechanisms to event related potentials (ERPs) from scalp EEG. This model was developed with the aim of explaining different, sometimes conflicting, findings in the attention literature. Specifically, this model address conflicting findings showing both simultaneous and serial deployment of attention. Another argument addressed by the model is whether attention to a location invokes a suppression of the spatial surround, or the selective inhibition of distractors. With the R-CGF, we have found that these results are not as incompatible as they appear but rather can both result from a common set of mechanisms in different kinds of experiments.
The model has three main neural sheets, early vision (EV), late vision (LV) and a master attention map (AM), connected spatiotopically. The LV layers are specialized for different features (e.g. shape or color) with modulated connections to the AM depending on task requirements. The AM implements a reactive inhibitory circuit through gating neurons that suppresses attention selectively at the location of distractors that are proximal to the target.
Start Date
17-5-2017 11:16 AM
End Date
17-5-2017 11:38 AM
Modeling the neural circuitry underlying the behavioral and EEG correlates of attentional capture
The Reactive-Convergent Gradient Field model (R-CGF) is a unique approach to modeling spatial attention in that it links neural mechanisms to event related potentials (ERPs) from scalp EEG. This model was developed with the aim of explaining different, sometimes conflicting, findings in the attention literature. Specifically, this model address conflicting findings showing both simultaneous and serial deployment of attention. Another argument addressed by the model is whether attention to a location invokes a suppression of the spatial surround, or the selective inhibition of distractors. With the R-CGF, we have found that these results are not as incompatible as they appear but rather can both result from a common set of mechanisms in different kinds of experiments.
The model has three main neural sheets, early vision (EV), late vision (LV) and a master attention map (AM), connected spatiotopically. The LV layers are specialized for different features (e.g. shape or color) with modulated connections to the AM depending on task requirements. The AM implements a reactive inhibitory circuit through gating neurons that suppresses attention selectively at the location of distractors that are proximal to the target.