Value saturation: Architecture of subjective necessity

IF 2.1 4区 生物学 Q2 BIOLOGY
Biosystems Pub Date : 2025-12-01 Epub Date: 2025-11-04 DOI:10.1016/j.biosystems.2025.105642
Joseph J. Trukovich
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引用次数: 0

Abstract

Consciousness requires explanation for how biological architectures reliably generate it. This paper introduces Value Saturation, advancing an identity claim: phenomenal consciousness IS explicit recursive self-modeling saturated by homeostatic significance under perspectival entrapment. Building on the Reaction to Reflection framework, the theory distinguishes sentience (implicit recursion under survival stakes, Level 2) from subjective consciousness (explicit recursion where self-models become manipulable, Levels 4–5). Three integrated components prove necessary: interoceptive binding, homeostatic saturation, and perspectival entrapment. Testable predictions include developmental progression from birth sentience to subjective consciousness around ages 3–5, awareness-manipulation asymmetry, and clinical dissociations producing aberrant rather than absent phenomenology. Converging evidence from prediction error processing, homeostatic feelings, metacognitive hierarchies, and biological computing's thermodynamic advantages supports these requirements. The framework specifies falsification criteria and transforms consciousness into an empirically tractable investigation of organizational transitions in biological systems under thermodynamic constraints.
价值饱和:主观必然性的建筑。
意识需要解释生物结构如何可靠地产生它。本文介绍了价值饱和,提出了一种身份主张:现象意识是在视角陷阱下被稳态意义饱和的显式递归自我建模。基于对反思的反应框架,该理论区分了感知(生存风险下的内隐递归,第2级)和主观意识(自我模型变得可操纵的外显递归,第4-5级)。三个组成部分被证明是必要的:内感受结合、稳态饱和和透视夹持。可测试的预测包括3-5岁左右从出生感知到主观意识的发展进程,意识操纵不对称,以及产生异常现象而不是缺失现象的临床分离。来自预测误差处理、自我平衡感觉、元认知层次和生物计算的热力学优势的证据支持这些要求。该框架规定了证伪标准,并将意识转化为热力学约束下生物系统组织过渡的经验可处理调查。
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
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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