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Inhibiting unwanted thoughts, actions and emotions figures centrally in daily life, and the prefrontal cortex (PFC) is widely viewed as a source of this inhibitory control. We argue that the function of the PFC is best understood in terms of representing and actively maintaining abstract information, such as goals, which produces two types of inhibitory(More)
We address the connection between conceptual knowledge and cognitive control using a neural network model. This model extends a widely held theory of cognitive control [Cohen, J. D., Dunbar, K., & McClelland, J. L. On the control of automatic processes: A parallel distributed processing model of the Stroop effect. Psychological Review, 97, 332-361, 1990] so(More)
Cognitive control refers to the ability to perform task-relevant processing in the face of other distractions or other forms of interference, in the absence of strong environmental support. It depends on the integrity of the prefrontal cortex and associated biological structures (e.g., the basal ganglia). Computational models have played an influential role(More)
A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal "executive network," and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical framework for(More)
We can learn from the wisdom of others to maximize success. However, it is unclear how humans take advice to flexibly adapt behavior. On the basis of data from neuroanatomy, neurophysiology, and neuroimaging, a biologically plausible model is developed to illustrate the neural mechanisms of learning from instructions. The model consists of two complementary(More)
We use a biologically grounded neural network model to investigate the brain mechanisms underlying individual differences specific to the selection and instantiation of representations that exert cognitive control in task switching. Existing computational models of task switching do not focus on individual differences and so cannot explain why task(More)
We present a metacognitive classifier implemented within a hybrid architecture that combines the strengths of two existing, mature cognitive architectures: ACT-R and Leabra. The classification of a set of items into previously seen and novel categories (TRAIN and TEST, respectively) is carried out in ACT-R using metacogni-tive signals supplied by Leabra.(More)
We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish(More)