Sébastien Hélie

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There is evidence that rule-based category learning is supported by a broad neural network that includes the prefrontal cortex, the anterior cingulate cortex, the head of the caudate nucleus, and medial temporal lobe structures. Although thousands of studies have examined rule-based category learning, only a few have studied the development of automaticity(More)
This article proposes a unified framework for understanding creative problem solving, namely, the explicit-implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and(More)
Three experiments studied the effects of category structure on the development of categorization automaticity. In Experiment 1, participants were each trained for over 10,000 trials in a simple categorization task with one of three category structures. Results showed that after the first few sessions, there were no significant behavioral differences between(More)
Cools (2006) suggested that prefrontal dopamine levels are related to cognitive stability whereas striatal dopamine levels are related to cognitive plasticity. With such a wide ranging role, almost all cognitive activities should be affected by dopamine levels in the brain. Not surprisingly, factors influencing brain dopamine levels have been shown to(More)
Rule-guided behavior is essential in quickly adapting to one's ever-changing environment. In particular, learned rules can quickly be used in new contexts or applied to new stimuli (which confers an advantage over restricting learning to perceptual - motor associations). Here, we propose a new neurocomputational model of automaticity in rule-guided(More)
Many models offer different explanations of learning processes, some of them predicting equal learning rates between conditions. The simplest method by which to assess this equality is to evaluate the curvature parameter for each condition, followed by a statistical test. However, this approach is highly dependent on the fitting procedure, which may come(More)
Computational Cognitive Neuroscience (CCN) is a new field that lies at the intersection of computational neuroscience, machine learning, and neural network theory (i.e., connectionism). The ideal CCN model should not make any assumptions that are known to contradict the current neuroscience literature and at the same time provide good accounts of behavior(More)
Psychological theories of problem solving have largely focused on explicit processes that gradually bring the solver closer to the solution step-by-step in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient or ineffective when the problem is too complex, ill-understood, or ambiguous. In such a case, a(More)
Parkinson's disease (PD) is caused by the accelerated death of dopamine (DA) producing neurons. Numerous studies documenting cognitive deficits of PD patients have revealed impairments in a variety of tasks related to memory, learning, visuospatial skills, and attention. While there have been several studies documenting cognitive deficits of PD patients,(More)
Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel(More)