Detection of and adaptation to stimulus-response conflict are critical and central aspects of human executive function. Yet, how such adaptation occurs is not well understood. Conflict occurs when multiple stimuli or multiple aspects of the same stimulus become associated with different behavioral responses. Behaviorally, conflict results in slower responses and increased errors. However, observers show moment-to-moment adaptation to conditions of conflict. An outcome of this adaptation is that subsequent conflicting stimuli have less of a negative impact on performance. It has been theorized that top-down control in response to conflict engages selective attention mechanisms to reduce additional conflict;this type of dynamic endogenous modulation has promise as a more ecologically valid model to study selective attention than classic experimental designs. However, the existing literature is inconsistent regarding the nature of these conflict driven attention effects. Some results point to multiple mechanisms of conflict driven attention operating at early stages of visual processing. Other results suggest that some attention mechanisms, such as distractor inhibition, for which there is substantial experimental evidence, may not play an important role in attention driven by contextual conflict. These same data do not support conflict related attention effects for early visual processing. This project proposes to use functional MRI and scalp electroencephalography to explore the brain mechanisms responsible for conflict adaptation, asking when, where and how these mechanisms affect visual processing. Specifically, we ask whether and how the brain uses selective visual attention to filter out unwanted information in response to conflict, whether conflict-driven perceptual modulation operates on feature and space based processing and whether such modulation is allocated in anticipation of impending stimulus events.
Deficits in selective attention and conflict processing are hallmarks of many psychiatric and neurological disorders including Alzheimer's disease, unipolar depression and bipolar disorder, and schizophrenia. Advancing our understanding of the relationship between these fundamental cognitive mechanisms is an important component of developing a more complete understanding of these significant health issues.
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