The ability to reason and problem‐solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real‐world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self‐generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities.

中文翻译：

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基于奖励的学习的个体差异预测流体推理能力

在新情况下推理和解决问题的能力，由 Raven 的高级渐进矩阵 (RAPM) 衡量，高度预测认知任务的表现和现实世界的结果。在这里，我们提供证据表明 RAPM 性能取决于重新分配注意力以响应自我生成的关于进度的反馈的能力。我们认为这种能力是由基底神经节核支撑的，基底神经节核与奖励处理和认知控制密切相关。这个假设是在 RAPM 任务的神经计算模型中实现的，该模型用于在行为和神经层面推导出新的预测。这些预测随后在一项神经成像和两项行为实验中得到验证。此外，对神经影像数据的有效连接分析证实了基底神经节在调节注意力方面的作用。综上所述，这些结果表明，与奖励处理相关的神经回路中的个体差异是人类流体推理能力的基础。