The neural dynamics associated with computational complexity.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-09-23 eCollection Date: 2024-09-01 DOI:10.1371/journal.pcbi.1012447

Juan Pablo Franco, Peter Bossaerts, Carsten Murawski

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Abstract

Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which participants solved several instances of the 0-1 knapsack problem, a combinatorial optimization problem, while undergoing ultra-high field (7T) functional magnetic resonance imaging (fMRI). Instances varied in computational hardness. We characterize a network of brain regions whose activation was correlated with computational complexity, including the anterior insula, dorsal anterior cingulate cortex and the intra-parietal sulcus/angular gyrus. Activation and connectivity changed dynamically as a function of complexity, in line with theoretical computational requirements. Overall, our results suggest that computational complexity theory provides a suitable framework to study the effects of computational hardness on the neural processes associated with solving complex cognitive tasks.

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与计算复杂性相关的神经动力学。

许多日常任务都要求人们解决计算复杂的问题。然而，人们对计算难度对解决此类问题相关神经过程的影响知之甚少。在此，我们借鉴计算复杂性理论来解决这一问题。我们进行了一项实验，让参与者在接受超高场（7T）功能磁共振成像（fMRI）检查的同时，解决 0-1 knapsack 问题（一种组合优化问题）的多个实例。这些实例的计算难度各不相同。我们描述了激活与计算复杂性相关的脑区网络，包括前脑岛、背侧前扣带回皮层和顶内沟/角回。激活和连接性随着复杂性的变化而动态变化，这与理论上的计算要求是一致的。总之，我们的研究结果表明，计算复杂性理论为研究计算难度对解决复杂认知任务相关神经过程的影响提供了一个合适的框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.