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1.  Cognitive Control in Majority Search: A Computational Modeling Approach 
Despite the importance of cognitive control in many cognitive tasks involving uncertainty, the computational mechanisms of cognitive control in response to uncertainty remain unclear. In this study, we develop biologically realistic neural network models to investigate the instantiation of cognitive control in a majority function task, where one determines the category to which the majority of items in a group belong. Two models are constructed, both of which include the same set of modules representing task-relevant brain functions and share the same model structure. However, with a critical change of a model parameter setting, the two models implement two different underlying algorithms: one for grouping search (where a subgroup of items are sampled and re-sampled until a congruent sample is found) and the other for self-terminating search (where the items are scanned and counted one-by-one until the majority is decided). The two algorithms hold distinct implications for the involvement of cognitive control. The modeling results show that while both models are able to perform the task, the grouping search model fit the human data better than the self-terminating search model. An examination of the dynamics underlying model performance reveals how cognitive control might be instantiated in the brain for computing the majority function.
PMCID: PMC3037790  PMID: 21369357
cognitive control; uncertainty; majority function; algorithms; computational modeling; neural networks
2.  Testing the behavioral interaction and integration of attentional networks 
Brain and cognition  2009;70(2):209-220.
One current conceptualization of attention subdivides it into functions of alerting, orienting, and executive control. Alerting describes the function of tonically maintaining the alert state and phasically responding to a warning signal. Automatic and voluntary orienting are involved in the selection of information among multiple sensory inputs. Executive control describes a set of more complex operations that includes monitoring and resolving conflicts in order to control thoughts or behaviors. Converging evidence supports this theory of attention by showing that each function appears to be subserved by anatomically distinct networks in the brain and differentially innervated by various neuromodulatory systems. Although much research has been dedicated to understanding the functional separation of these networks in both healthy and disease states, the interaction and integration among these networks still remain unclear. In this study, we aimed to characterize possible behavioral interaction and integration in healthy adult volunteers using a revised attentional network test (ANT-R) with cue-target interval and cue validity manipulations. We found that whereas alerting improves overall response speed, it exerts negative influence on executive control under certain conditions. A valid orienting cue enhances but an invalid cue diminishes the ability of executive control to overcome conflict. The results support the hypothesis of functional integration and interaction of these brain networks.
PMCID: PMC2674119  PMID: 19269079
attention; attentional networks; alerting; orienting; executive control
3.  Searching for the Majority: Algorithms of Voluntary Control 
PLoS ONE  2008;3(10):e3522.
Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most important under a given situation; the algorithms underlying such control, however, are often not clear. We investigated possible algorithms of control for the performance of the majority function, in which participants searched for and identified one of two alternative categories (left or right pointing arrows) as composing the majority in each stimulus set. We manipulated the amount (set size of 1, 3, and 5) and content (ratio of left and right pointing arrows within a set) of the inputs to test competing hypotheses regarding mental operations for information processing. Using a novel measure based on computational load, we found that reaction time was best predicted by a grouping search algorithm as compared to alternative algorithms (i.e., exhaustive or self-terminating search). The grouping search algorithm involves sampling and resampling of the inputs before a decision is reached. These findings highlight the importance of investigating the implications of voluntary control via algorithms of mental operations.
PMCID: PMC2567037  PMID: 18949039

Results 1-3 (3)