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Octopuses, artificial intelligence, and advanced alien civilizations: for many reasons, it’s interesting to contemplate ways of thinking other than whatever it is we humans do. How should we think about the space of all possible cognitions? One aspect is simply the physics of the underlying substrate, the physical stuff that is actually doing the thinking. We are used to brains being solid — squishy, perhaps, but consisting of units in an essentially fixed array. What about liquid brains, where the units can move around? Would an ant colony count? We talk with complexity theorist Ricard Solé about complexity, criticality, and cognition.

Listen at: www.preposterousuniverse.com

Mads J Dengsø

Adaptive Behavior Volume 32, Issue 1

Enactive approaches to cognitive science as well as contemporary accounts from neuroscience have argued that we need to reconceptualize the role of temporality and affectivity in minds. Far from being limited to special faculties, such as emotional mental states and timekeeping, these accounts argue that time and affect both constitute fundamental aspects of minds and cognition. If this is true, how should one conceptualize the relation between these two fundamental aspects? This paper offers a way to conceptualize and clarify the relation between temporality and affectivity when understood in this fundamental sense. In particular, the paper contributes to ongoing discussions of structural temporality and affectivity by combining enactive notions of self-maintenance with a thermodynamically informed view of the organization of living systems. In situating temporality and affectivity by way of their role for the maintenance of thermodynamic non-equilibrium, I will argue that temporality and affectivity should be regarded as two sides of the same coin—that is, two distinct ways of highlighting one and the same process. This process corresponds to the continued differentiation of organism and environment as functional poles of a living system. The temporal and affective structure of living systems may thus be seen as the warp and weft by which living systems maintain themselves in terms of thermodynamic non-equilibrium.

Read the full article at: journals.sagepub.com

Bing Yuan, Zhang Jiang, Aobo Lyu, Jiayun Wu, Zhipeng Wang, Mingzhe Yang, Kaiwei Liu, Muyun Mou, Peng Cui

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.

Read the full article at: arxiv.org

Qingchu Wu, Tarik Hadzibeganovic, and Xiao-Pu Han

Mathematical Models and Methods in Applied Sciences Vol. 33, No. 13, pp. 2785-2821 (2023)

Understanding the interplay between human behavioral phenomena and infectious disease dynamics has been one of the central challenges of mathematical epidemiology. However, socio-cognitive processes critical for the initiation of desired behavioral responses during an outbreak have often been neglected or oversimplified in earlier models. Combining the microscopic Markov chain approach with the law of total probability, we herein institute a mathematical model describing the dynamic interplay between stage-based progression of awareness diffusion and endemic disease transmission in multiplex networks. We analytically derived the epidemic thresholds for both discrete-time and continuous-time versions of our model, and we numerically demonstrated the accuracy of our analytic arguments in capturing the time course and the steady state of the coupled disease-awareness dynamics. We found that our model is exact for arbitrary unclustered multiplex networks, outperforming a widely adopted probability-tree-based method, both in the prediction of the time-evolution of a contagion and in the final epidemic size. Our findings show that informing the unaware individuals about the circulating disease will not be sufficient for the prevention of an outbreak unless the distributed information triggers strong awareness of infection risks with adequate protective measures, and that the immunity of highly-aware individuals can elevate the epidemic threshold, but only if the rate of transition from weak to strong awareness is sufficiently high. Our study thus reveals that awareness diffusion and other behavioral parameters can nontrivially interact when producing their effects on epidemiological dynamics of an infectious disease, suggesting that future public health measures should not ignore this complex behavioral interplay and its influence on contagion transmission in multilayered networked systems.

Read the full article at: www.worldscientific.com

The Human Brain & Behavior Laboratory (HBBL) located in FAU’s Center for Complex Systems in the Charles E. Schmidt College of Science (Valery Forbes, Dean) seeks an excellent candidate to join our team to investigate the roots of biological agency (see recent article in PNAS). HBBL explores how sentient agency emerges through self-organizing, coordinative processes that span organisms and environments. The postdoctoral fellow will assist in developing next-generation interactive systems for empirical study, conducting experiments with human infants (involving 3D motion capture, EEG, eye tracking) and analyzing data. The post-holder will have freedom and capacity to develop their own scientific ideas through experimental design and preparing manuscripts and grant applications. This position offers opportunity to participate in HBBL’s active collaborations with world-leading teams in Artificial Intelligence analysis (Intelligent Systems Research Centre, Ulster University, The Institute for the Augmented Human, University of Bath), in mathematical modeling using Active Inference (Welcome Centre for Human Neuroimaging, Univ. College London) and Coordination Dynamics frameworks (Institut de Neurosciences des Systèmes, Aix Marseille University). Qualified candidates will possess advanced technical skills in relevant areas such as coding (Matlab, Python, etc.), machine learning, signal processing, mathematical modeling, EEG analysis, motion capture, and electrical engineering/robotics, as well as creativity, curiosity, and a collaborative spirit. HBBL is directed by Glenwood and Martha Creech Eminent Scholar in Science J.A. Scott Kelso. Prof. Kelso founded FAU’s Center for Complex Systems in 1985 with the goal of bringing scientists from different disciplines together in one place to understand the multiscale structure, function, and dynamics of complex biological systems, including human beings and their activities.
Please forward Letter of Interest to Dr Aliza Sloan (asloan2014@fau.edu) indicating qualifications, CV and names and contact information of 2 Referees as soon as possible. The expected salary will follow the current NIH postdoc salary scale plus benefits. The position will be for 2 years, assuming satisfactory progress in year one, and may be extended further depending on funding. Position start date is May to August 2024.

Nicola Amoroso, Nicola Gambacorta, Fabrizio Mastrolorito, Maria Vittoria Togo, Daniela Trisciuzzi, Alfonso Monaco, Ester Pantaleo, Cosimo Damiano Altomare, Fulvio Ciriaco & Orazio Nicolotti 

Scientific Reports volume 13, Article number: 21335 (2023)

Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.

Read the full article at: www.nature.com