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Joshua W. Brown
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Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2018) 30 (8): 1061–1065.
Published: 01 August 2018
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Sometime in the past two decades, neuroimaging and behavioral research converged on pFC as an important locus of cognitive control and decision-making, and that seems to be the last thing anyone has agreed on since. Every year sees an increase in the number of roles and functions attributed to distinct subregions within pFC, roles that may explain behavior and neural activity in one context but might fail to generalize across the many behaviors in which each region is implicated. Emblematic of this ongoing proliferation of functions is dorsal ACC (dACC). Novel tasks that activate dACC are followed by novel interpretations of dACC function, and each new interpretation adds to the number of functionally specific processes contained within the region. This state of affairs, a recurrent and persistent behavior followed by an illusory and transient relief, can be likened to behavioral pathology. In Journal of Cognitive Neuroscience , 29:10 we collect contributed articles that seek to move the conversation beyond specific functions of subregions of pFC, focusing instead on general roles that support pFC involvement in a wide variety of behaviors and across a variety of experimental paradigms.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2017) 29 (10): 1656–1673.
Published: 01 October 2017
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Recent work on the role of the ACC in cognition has focused on choice difficulty, action value, risk avoidance, conflict resolution, and the value of exerting control among other factors. A main underlying question is what are the output signals of ACC, and relatedly, what is their effect on downstream cognitive processes? Here we propose a model of how ACC influences cognitive processing in other brain regions that choose actions. The model builds on the earlier Predicted Response Outcome model and suggests that ACC learns to represent specifically the states in which the potential costs or risks of an action are high, on both short and long timescales. It then uses those cost signals as a basis to bias decisions to minimize losses while maximizing gains. The model simulates both proactive and reactive control signals and accounts for a variety of empirical findings regarding value-based decision-making.