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Nature heals: An informational entropy account of self-organization and change in field psychotherapy 自然治愈：对现场心理疗法中的自组织和变化的信息熵解释
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-09-06 DOI: 10.1016/j.plrev.2024.09.005
Pietro Sarasso , Wolfgang Tschacher , Felix Schoeller , Gianni Francesetti , Jan Roubal , Michela Gecele , Katiuscia Sacco , Irene Ronga

This paper reviews biophysical models of psychotherapeutic change based on synergetics and the free energy principle. These models suggest that introducing sensory surprise into the patient-therapist system can lead to self-organization and the formation of new attractor states, disrupting entrenched patterns of thoughts, emotions, and behaviours. We propose that the therapist can facilitate this process by cultivating epistemic trust and modulating embodied attention to allow surprising affective states to enter shared awareness. Transient increases in free energy enable the update of generative models, expanding the range of experiences available within the patient-therapist phenomenal field. We hypothesize that patterns of disorganization at behavioural and physiological levels, indexed by increased entropy, complexity, and lower determinism, are key markers and predictors of psychotherapeutic gains. Future research should investigate how the therapist's openness to novelty shapes therapeutic outcomes.

本文回顾了基于协同学和自由能原理的心理治疗变化生物物理模型。这些模型表明，在患者-治疗师系统中引入感官惊喜可以导致自组织和新吸引子状态的形成，破坏根深蒂固的思想、情感和行为模式。我们建议，治疗师可以通过培养认识上的信任和调节体现注意力来促进这一过程，从而让令人惊讶的情感状态进入共同意识。自由能量的瞬时增加能够更新生成模型，扩大患者-治疗师现象场中可用的体验范围。我们假设，以熵增加、复杂性和决定性降低为指标的行为和生理层面的混乱模式是心理治疗收益的关键标志和预测因素。未来的研究应调查治疗师对新奇事物的开放性如何影响治疗结果。
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引用次数: 0
Why graph theory deserves more focus. Comment on “Connectivity analyses for task-based fMRI” by Huang et al. 图论为何值得更多关注？就 Huang 等人的 "基于任务的 fMRI 的连接性分析 "发表评论
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-09-05 DOI: 10.1016/j.plrev.2024.09.003
Mohammadreza Khodaei , Clayton C. McIntyre , Haley A. Kirse , Paul Laurienti

Huang et al. have conducted a thorough examination of methodologies used for identifying and analyzing functional connectivity using task-based fMRI. Their review adeptly describes current approaches without bias or preference. In this commentary, we explain why we believe that graph theory is the optimal approach for studying neural mechanisms associated with complex behaviors and cognitive processes that are engaged during task-based fMRI.

Huang 等人对使用基于任务的 fMRI 识别和分析功能连接的方法进行了深入研究。他们的综述巧妙地描述了当前的方法，没有偏见或偏好。在这篇评论中，我们将解释为什么我们认为图论是研究与复杂行为和认知过程相关的神经机制的最佳方法，这些复杂行为和认知过程是在基于任务的 fMRI 过程中参与的。
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引用次数: 0
Addressing researcher degrees of freedom with best practice guidelines: Comment on ‘Connectivity analysis for task-based fMRI’ by S. Huang, F. De Brigard, R. Cabeza, and S. W. Davis 用最佳实践指南解决研究人员的自由度问题：对 S. Huang、F. De Brigard、R. Cabeza 和 S. W. Davis 的 "基于任务的 fMRI 连接性分析 "的评论
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-09-04 DOI: 10.1016/j.plrev.2024.09.004
Lucina Q. Uddin
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引用次数: 0
Perspectives on modeling pedestrian dynamics by thermostatted kinetic theory 用恒温动力学理论建立行人动力学模型的视角：对 Carlo Bianca 所著 "复杂活性物质生命系统恒温动力学理论模型的十年 "的评论。
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-09-03 DOI: 10.1016/j.plrev.2024.08.012
Nouamane Bakhdil , Abdelghani El Mousaoui , Abdelilah Hakim
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引用次数: 0
Why the distinction between language and languages matters Comment on “Language follows a distinct mode of extra-genomic evolution” by Balthasar Bickel, Anne-Lise Giraud, Klaus Zuberbühler, Carel P. van Schaik 为什么语言和语言之间的区别很重要 对 Balthasar Bickel、Anne-Lise Giraud、Klaus Zuberbühler、Carel P. van Schaik 所著《语言遵循独特的基因组外进化模式》发表的评论
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-09-03 DOI: 10.1016/j.plrev.2024.08.010
R.I.M. Dunbar
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引用次数: 0
White matter, and why twin resemblance can still matter 白质，以及为什么双胞胎的相似性仍然重要
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-08-29 DOI: 10.1016/j.plrev.2024.08.009
Marco Battaglia
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引用次数: 0
Opportunities and challenges in connectivity analysis for task-based fMRI comment on “connectivity analyses for task-based fMRI” by Huang S, De Brigard, F., Cabeza, R, and Davis, S.W. 基于任务的 fMRI 连接性分析的机遇与挑战》的评论，作者：Huang S、De Brigard、F.、Cabeza、R 和 Davis、S.W.
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-08-29 DOI: 10.1016/j.plrev.2024.08.011
Xin Di , Li Zhang , Zhiguo Zhang , Bharat B. Biswal
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引用次数: 0
Alpha suppression in the context of cross-frequency interactions between fast and intermediate timescales 快速时标和中级时标之间跨频相互作用背景下的α抑制
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-08-26 DOI: 10.1016/j.plrev.2024.08.008
Narayanan Srinivasan, Ramya Mudumba
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引用次数: 0
Exploring the genetics of white matter comment on “heritability of white matter in twins: A diffusion neuroimaging review” by Giovanni Videtta, Chiara Colli, Letizia Squarcina, Corrado Fagnani, Emanuela Medda, Paolo Brambilla, and Giuseppe Delvecchio 探索白质的遗传学》对 "双生子白质的遗传性：Giovanni Videtta、Chiara Colli、Letizia Squarcina、Corrado Fagnani、Emanuela Medda、Paolo Brambilla 和 Giuseppe Delvecchio 的 "扩散神经影像学综述 "的评论
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-08-20 DOI: 10.1016/j.plrev.2024.08.007
Christopher M. Filley
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引用次数: 0
Life and self-organization on the way to artificial intelligence for collective dynamics 集体动力人工智能道路上的生命与自组织
IF 13.7 1区 生物学 Q1 BIOLOGY
Physics of Life Reviews
Pub Date : 2024-08-20 DOI: 10.1016/j.plrev.2024.08.006
Nicola Bellomo , Marina Dolfin , Jie Liao

This work is dedicated to the study, modeling, and simulation, of the collective dynamics of interacting living entities. The first perspective is to develop a mathematical theory of swarm intelligence for the above mentioned systems. The second perspective is to design the conceptual tools for a theory of artificial intelligence. The aim is to model a dynamics where interacting entities learn from other entities as well as from the environment and external actions. Then, out of this collective learning process, each entity develops a strategy to pursue specific goals through a decision making process that leads to the dynamic. The approach is based on developments of the kinetic theory of active particles. This paper does not naively state that the problem of artificial intelligence for collective dynamics has been exhaustively considered, but some hints are proposed to contribute to such a challenging perspective in view of further developments.

这项工作致力于对相互作用的生命实体的集体动力学进行研究、建模和模拟。第一个视角是为上述系统开发蜂群智能数学理论。第二个角度是设计人工智能理论的概念工具。目的是建立一个动态模型，在这个模型中，相互作用的实体从其他实体以及环境和外部行动中学习。然后，从这一集体学习过程中，每个实体通过决策过程制定出追求特定目标的战略，从而形成动态。这种方法基于活动粒子动力学理论的发展。本文并没有天真地认为集体动力学人工智能问题已经得到了详尽的考虑，但提出了一些提示，以便在进一步的发展中为这一具有挑战性的观点做出贡献。
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引用次数: 0
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