Cognitive and Neural Representations of Fractals in Vision, Music, and Action.

Q3 Neuroscience Advances in neurobiology Pub Date : 2024-01-01 DOI:10.1007/978-3-031-47606-8_46
Mauricio de Jesus Dias Martins
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Abstract

The concept of fractal was popularized by Mandelbrot as a tool to tame the geometrical structure of objects with infinite hierarchical depth. The key aspect of fractals is the use of simple parsimonious rules and initial conditions, which when applied recursively can generate unbounded complexity. Fractals are structures ubiquitous in nature, being present in coast lines, bacteria colonies, trees, and physiological time series. However, within the field of cognitive science, the core question is not which phenomena can generate fractal structures, but whether human or animal minds can represent recursive processes, and if so in which domains. In this chapter, we will explore the cognitive and neural mechanisms underlying the representation of recursive hierarchical embedding. Language is the domain in which this capacity is best studied. Humans can generate an infinite array of hierarchically structured sentences, and this capacity distinguishes us from other species. However, recent research suggests that humans can represent similar structures in the domains of music, vision, and action and has provided additional cues as to how these capacities are cognitively implemented. Using a comparative approach, we will map the commonalities and differences across domains and offer a roadmap to understand the neurobiological implementation of fractal cognition.

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视觉、音乐和动作中分形的认知和神经表征。
分形的概念是由曼德布罗特提出的,作为一种工具,它可以驯服具有无限层次深度的物体的几何结构。分形的关键在于使用简单的准规则和初始条件,在递归应用时可以产生无限的复杂性。分形是自然界中无处不在的结构,存在于海岸线、细菌群落、树木和生理时间序列中。然而,在认知科学领域,核心问题并不是哪些现象可以产生分形结构,而是人类或动物的思维是否可以表现递归过程,如果可以,又是在哪些领域。在本章中,我们将探讨表征递归分层嵌入的认知和神经机制。语言是研究这种能力的最佳领域。人类可以生成无限多的层次结构句子,这种能力使我们有别于其他物种。然而,最近的研究表明,人类可以在音乐、视觉和行动等领域表现出类似的结构,并为这些能力如何在认知中实现提供了更多线索。我们将采用比较的方法,绘制各领域的共性和差异图,为理解分形认知的神经生物学实现提供一个路线图。
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来源期刊
Advances in neurobiology
Advances in neurobiology Neuroscience-Neurology
CiteScore
2.80
自引率
0.00%
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0
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