Franz Kuchling , Isha Singh , Mridushi Daga , Susan Zec , Alexandra Kunen and Michael Levin

JRS Interface February 2025 Volume 22Issue 223

The field of diverse intelligence explores the capacity of systems without complex brains to dynamically engage with changing environments, seeking fundamental principles of cognition and their evolutionary origins. However, there are many knowledge gaps around a general behavioural directive connecting aneural to neural organisms. This study tests predictions of the computational framework of active inference based on the free energy principle in neuroscience, applied to aneural biological processes. We demonstrate pattern recognition in the green algae Volvox using phototactic experiments with varied light pulse patterns, measuring their phototactic bias as a readout for their preferential ability to detect and adapt to one pattern over another. Results show Volvox adapt more readily to regular patterns than irregular ones and even exhibit memory properties, exhibiting a crucial component of basal intelligence. Pharmacological and electric shock-based interventions and photoadaptation simulations reveal how randomized stimuli interfere with normal photoadaptation through a structured dynamic interplay of colony rotation and calcium-mediated photoreceptor-to-flagellar information transfer, consistent with uncertainty minimization. The detection of functional uncertainty minimization in an aneural organism expands concepts like uncertainty minimization beyond neurons and provides insights and novel intervention tools applicable to other living systems, similar to early learning validations in simpler neural organisms.

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Luca Maria Aiello, Anastassia Vybornova, Sándor Juhász, Michael Szell, and Eszter Bokányi

PNAS 122 (10) e2408937122

Highways are physical barriers that cut opportunities for social connections, but the magnitude of this effect has not been quantified. Such quantitative evidence would enable policy-makers to prioritize interventions that reconnect urban communities—an urgent need in many US cities. We relate urban highways in the 50 largest US cities with massive, geolocated online social network data to quantify the decrease in social connectivity associated with highways. We find that this barrier effect is strong in all 50 cities, and particularly prominent over shorter distances. We also confirm this effect for highways that are historically associated with racial segregation. Our research demonstrates with high granularity the long-lasting impact of decades-old infrastructure on society and provides tools for evidence-based remedies.

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GUANGHUI YAN, JIE TANG, HUAYAN PEI, and WENWEN CHANG

Advances in Complex SystemsVol. 28, No. 01n02, 2550005 (2025)

Considering that rumors propagation is affected by many factors in real life, based on the SIRS infectious disease model in complex networks, an extended ISRI rumor propagation model is proposed by using the probability function to define the influence mechanisms such as trust mechanism, and suspicion mechanism. First, dynamic equations are established for homogeneous and heterogeneous networks, and the rumor and rumor-free equilibrium points in the two networks are analyzed, respectively. Then, the basic reproduction number R0 is obtained by using the next generation matrix and derivative calculation methods. Next, the lyapunov function is constructed to discuss the local stability and global stability of the equilibrium point, and the influence of different parameters on the basic reproduction number R0. In addition, we selected ER network and BA network and found that population flow has a significant impact on the speed and scale of rumor propagation. At the same time, the trust mechanism can improve the propagation speed and scale, while the skepticism mechanism can inhibit the propagation speed, and it is more obvious in the BA network. The interaction between these mechanisms further affects the propagation characteristics of rumors in the network.

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Keith Y Patarroyo, Abhishek Sharma, Ian Seet, Ignas Packmore, Sara I. Walker, Leroy Cronin

Quantifying the evolution and complexity of materials is of importance in many areas of science and engineering, where a central open challenge is developing experimental complexity measurements to distinguish random structures from evolved or engineered materials. Assembly Theory (AT) was developed to measure complexity produced by selection, evolution and technology. Here, we extend the fundamentals of AT to quantify complexity in inorganic molecules and solid-state periodic objects such as crystals, minerals and microprocessors, showing how the framework of AT can be used to distinguish naturally formed materials from evolved and engineered ones by quantifying the amount of assembly using the assembly equation defined by AT. We show how tracking the Assembly of repeated structures within a material allows us formalizing the complexity of materials in a manner accessible to measurement. We confirm the physical relevance of our formal approach, by applying it to phase transformations in crystals using the HCP to FCC transformation as a model system. To explore this approach, we introduce random stacking faults in closed-packed systems simplified to one-dimensional strings and demonstrate how Assembly can track the phase transformation. We then compare the Assembly of closed-packed structures with random or engineered faults, demonstrating its utility in distinguishing engineered materials from randomly structured ones. Our results have implications for the study of pre-genetic minerals at the origin of life, optimization of material design in the trade-off between complexity and function, and new approaches to explore material technosignatures which can be unambiguously identified as products of engineered design.

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Manuel Baltieri, Martin Biehl, Matteo Capucci, Nathaniel Virgo

The internal model principle, originally proposed in the theory of control of linear systems, nowadays represents a more general class of results in control theory and cybernetics. The central claim of these results is that, under suitable assumptions, if a system (a controller) can regulate against a class of external inputs (from the environment), it is because the system contains a model of the system causing these inputs, which can be used to generate signals counteracting them. Similar claims on the role of internal models appear also in cognitive science, especially in modern Bayesian treatments of cognitive agents, often suggesting that a system (a human subject, or some other agent) models its environment to adapt against disturbances and perform goal-directed behaviour. It is however unclear whether the Bayesian internal models discussed in cognitive science bear any formal relation to the internal models invoked in standard treatments of control theory. Here, we first review the internal model principle and present a precise formulation of it using concepts inspired by categorical systems theory. This leads to a formal definition of `model’ generalising its use in the internal model principle. Although this notion of model is not a priori related to the notion of Bayesian reasoning, we show that it can be seen as a special case of possibilistic Bayesian filtering. This result is based on a recent line of work formalising, using Markov categories, a notion of `interpretation’, describing when a system can be interpreted as performing Bayesian filtering on an outside world in a consistent way.

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