Lam Sze-Ying, Zénon Alexandre
INCIA, University of Bordeaux, 33076 Bordeaux, France.
Entropy (Basel). 2021 Feb 15;23(2):228. doi: 10.3390/e23020228.
Previous investigations concluded that the human brain's information processing rate remains fundamentally constant, irrespective of task demands. However, their conclusion rested in analyses of simple discrete-choice tasks. The present contribution recasts the question of human information rate within the context of visuomotor tasks, which provides a more ecologically relevant arena, albeit a more complex one. We argue that, while predictable aspects of inputs can be encoded virtually free of charge, real-time information transfer should be identified with the processing of surprises. We formalise this intuition by deriving from first principles a decomposition of the total information shared by inputs and outputs into a feedforward, predictive component and a feedback, error-correcting component. We find that the information measured by the feedback component, a proxy for the brain's information processing rate, scales with the difficulty of the task at hand, in agreement with cost-benefit models of cognitive effort.
先前的研究得出结论,无论任务要求如何,人类大脑的信息处理速率在根本上保持恒定。然而,他们的结论基于对简单离散选择任务的分析。本文在视觉运动任务的背景下重新审视了人类信息速率的问题,视觉运动任务提供了一个更符合生态学的场景,尽管它更为复杂。我们认为,虽然输入的可预测方面几乎可以免费编码,但实时信息传递应与意外情况的处理相关联。我们通过从第一原理推导出将输入和输出共享的总信息分解为前馈预测成分和反馈纠错成分,将这一直觉形式化。我们发现,由反馈成分测量的信息(大脑信息处理速率的一个指标)随着手头任务的难度而变化,这与认知努力的成本效益模型一致。