寻找心智认知的进化根源:关于持续有意识的达尔文观点。

IF 2 4区 生物学 Q2 BIOLOGY
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引用次数: 0

摘要

人类的注意力系统经历了重要的进化变化和专业化。事实上,我们杰出的社会和技术复杂性严格依赖于我们的注意能力,这种能力是持续的、有意的和自觉的。注意、意向和意识是心智认知的关键特征,我们不禁要问,这些认知特征的特定组合是自然选择过程的结果,还是心智复杂性的意外副产品。本文回顾了进化人类学的基本概念,以思考积极、中性或消极的选择性力量是否会影响心智认知能力的进化。目前,所有这些选择都有可能得到不同证据的支持。考虑到稳定机制或独特的社会角色和特定内部变异的混合假说也是可能的。用进化的方法来研究注意力和意识所涉及的认知能力,可以揭示我们个体和集体自然行为的潜力、局限和弊端,尤其是在处理人类意识的进化问题时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In search for evolutionary roots of a mindful cognition: A Darwinian view on sustained intentional awareness

The attention system underwent important evolutionary changes and specializations in the human genus. In fact, our outstanding social and technological complexity strictly depends on our attentional ability, which is sustained, intentional, and conscious. Attention, intention, and awareness are key features for what can be defined a mindful cognition, and we may wonder whether a specific combination of these cognitive traits may be the result of a natural selective process, or else an accidental by-product of mental complexity. In this article, basic concepts in evolutionary anthropology are reviewed, to consider whether positive, neutral, or negative selective forces might have influenced the evolution of a mindful cognitive ability. At present, all these alternatives are potentially supported by different kinds of evidence. Hybrid hypotheses, considering stabilizing mechanisms or distinct social roles and intra-specific variation, are also likely. An evolutionary approach to the cognitive abilities involved in attention and awareness can reveal potentialities, limitations, and drawbacks of our individual and collective natural behaviors, especially when dealing with the evolution of the human consciousness.

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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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