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Rational metareasoning and the plasticity of cognitive control

机译:理性的元推理和认知控制的可塑性

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摘要

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.
机译:人脑具有令人印象深刻的能力,可以适应如何将信息处理为高水平目标。虽然已知这些认知控制技能具有延展性,并且可以通过培训来提高,但其潜在的可塑性机制尚未得到很好的理解。在这里,我们开发和评估一个模型,该模型可以了解人们如何学习何时施加认知控制,使用哪种受控过程以及需要付出多少努力。我们从通用理论推导该模型,根据该理论,认知控制的功能是选择和配置神经通路,以便最佳利用有限的时间和有限的计算资源。我们的“学习的控制价值”模型的中心思想是人们使用强化学习根据刺激特征预测不同类型和强度的候选控制信号的价值。该模型正确预测了在视觉注意力实验和Stroop和Flanker范例中的四个干扰控制实验中观察到的自适应控制需求行为基础下的学习和转移效应。而且,我们的模型比关联学习模型和Win-Stay输班制模型更好地解释了这些发现。我们的发现阐明了学习和经验如何影响人们适应性地控制其思想和行为的能力和倾向。我们通过预测在哪些情况下这些学习机制可能导致自我控制失败来得出结论。

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