Abstract
In a cluttered visual environment, in addition to focusing on task-relevant objects, it is also important for attention to be occasionally captured to task-irrelevant but salient stimuli. Our previous EEG study has revealed that attention dynamically coordinates neural representations of multiple objects and flexibly modulates the sampling profile to manage different task demands (Jia et al., 2017). Meanwhile, it remains unclear how the brain deals with task-irrelevant but salient objects and further registers them into the task framework. To address the issue, we recorded magnetoencephalography (MEG) signals from 20 human subjects while they were presented with a multi-object visual display and performed a central fixation task. Notably, one of the items was made salient by being distinct from the others (e.g., red among green, etc.) but was completely task-irrelevant (i.e., demanding central fixation task). Next, we employed a temporal response function (TRF) approach (Liu et al., in press) to dissociate the time-resolved neuronal response that specifically tracks the salient (S-TRF) and non-salient (NS-TRF) items from the same MEG signals. We then performed source analysis on the S-TRF and NS-TRF responses to examine the fine spatiotemporal neuronal profiles. Our data demonstrates that salient object, compared to the non-salient one, first initiated response in right temporoparietal area (TPJ) at about 120 ms, followed by activation in left intraparietal sulcus (IPS) and bilateral frontal area, and finally reached early visual area (EVA). Interestingly, after the information was conveyed back to EVA, salient object showed higher alpha-band response compared to the non-salient one, suggesting a registration process during which the salient but task-irrelevant item was further inhibited. Our results support the crucial role of dorsal attentional network in saliency representation and provide a temporally precise and whole-brain description of how the salient information is processed, transferred, and integrated into the current task context.
Meeting abstract presented at VSS 2018