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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 8945
School of Cognitive Sciences
  Title:   Neural Correlate of Filtration of Irrelevant Information from Visual Working Memory but not in acquisition of inhibitory avoidance learning task
  Author(s): 
1.  shahin Nasr
2.  ali moeeny
3.  Hossein Esteky
  Status:   Published
  Journal: Plos One
  Vol.:  3
  Year:  2008
  Pages:   1-10
  Supported by:  IPM
  Abstract:
In a dynamic environment stimulus task relevancy could be altered through time and it is not always possible to dissociate relevant and irrelevant objects from the very first moment they come to our sight. In such conditions, subjects need to retain maximum possible information in their WM until it is clear which items should be eliminated from WM to free attentional and memory resources. Here, we examined the neural basis of irrelevant information filtration from WM by recording human ERP during a visual change detection task in which the stimulus irrelevancy was revealed in a later stage of the task forcing the subjects to keep all of the information in WM until test object set was presented. Assessing subjects? behaviour we found that subjects? RT was highly correlated with the number of irrelevant objects and not the relevant one, pointing to the notion that filtration, and not selection, process was used to handle the distracting effect of irrelevant objects. In addition we found that frontal N150 and parietal N200 peak latencies increased systematically as the amount of irrelevancy load increased. Interestingly, the peak latency of parietal N200, and not frontal N150, better correlated with subjects? RT. The difference between frontal N150 and parietal N200 peak latencies varied with the amount of irrelevancy load suggesting that functional connectivity between modules underlying fronto-parietal potentials vary concomitant with the irrelevancy load. Thus, we suggest a filtration mechanism, and not a selection one, to be responsible for eliminating irrelevant objects from WM. This mechanism relies on two different neural modules whose functional connectivity and activity latency depends on the number of irrelevant object.

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