Axel Constant , Karl John Friston and Andy Clark

Philosophical Transactions of the Royal Society B Volume 379 Issue 1895

How can one conciliate the claim that humans are uncertainty minimizing systems that seek to navigate predictable and familiar environments with the claim that humans can be creative? We call this the Enlightened Room Problem (ERP). The solution, we suggest, lies not (or not only) in the error-minimizing brain but in the environment itself. Creativity emerges from various degrees of interplay between predictive brains and changing environments: ones that repeatedly move the goalposts for our own error-minimizing machinery. By (co)constructing these challenging worlds, we effectively alter and expand the space within which our own prediction engines operate, and that function as ‘exploration bubbles’ that enable information seeking, uncertainty minimizing minds to penetrate deeper and deeper into artistic, scientific and engineering space. In what follows, we offer a proof of principle for this kind of environmentally led cognitive expansion.

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Froese, Tom and Georgii, Karelin and Takashi, Ikegami

Biological processes are end-directed, that is, teleological. Explaining the physical efficacy of end-directedness continues to be a profound challenge for theoretical biology, especially given its unavoidable implications for our own self-understanding. For a comprehensive theory of life, it is pivotal to bridge our human-centric view of end-directedness, which the social sciences and humanities consider intrinsic to our actions, with the natural sciences’ view of actions’ in purely physiological terms, especially in terms of thermodynamic tendencies. A comprehensive theory should therefore provide an end-involving account, which illuminates how both physiology and teleology distinctly contribute to behavior generation. Here we introduce the “Participation Criterion”: End-involvement in a bodily process entails that, in principle, it is distinguishable from one without end-involvement, specifically in terms of physiologically unpredictable changes in unexplainable variability. To exemplify the difficulty of satisfying this criterion, we critically analyze two theories on the thermodynamic basis of end-directedness. We then propose that “Irruption Theory” points to a way forward because it predicts that bodily processes have an end-involvement-dependent increase in their entropy rate. This is consistent with evidence of an association between conscious intention and neural fluctuations, is open to further experimental verification, and provides a novel perspective on the role of thermodynamic entropy production in the organism.

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Angad Yuvraj Singh and Sanjay Jain

We present a simple mathematical model that captures the evolutionary capabilities of a prebiotic compartment or protocell. In the model, the protocell contains an autocatalytic set whose chemical dynamics is coupled to the growth–division dynamics of the compartment. Bistability in the dynamics of the autocatalytic set results in a protocell that can exist with two distinct growth rates. Stochasticity in chemical reactions plays the role of mutations and causes transitions from one growth regime to another. We show that the system exhibits ‘natural selection’, where a ‘mutant’ protocell in which the autocatalytic set is active arises by chance in a population of inactive protocells, and then takes over the population because of its higher growth rate or ‘fitness’. The work integrates three levels of dynamics: intracellular chemical, single protocell, and population (or ecosystem) of protocells.

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Ana Maria Jaramillo, Hywel T. P. Williams, Nicola Perra & Ronaldo Menezes 

EPJ Data Science volume 12, Article number: 47 (2023)

Co-authorship networks, where nodes represent authors and edges represent co-authorship relations, are key to understanding the production and diffusion of knowledge in academia. Social constructs, biases (implicit and explicit), and constraints (e.g. spatial, temporal) affect who works with whom and cause co-authorship networks to organise into tight communities with different levels of segregation. We aim to examine aspects of the co-authorship network structure that lead to segregation and its impact on scientific production. We measure segregation using the Spectral Segregation Index (SSI) and find four ordered categories: completely segregated, highly segregated, moderately segregated and non-segregated communities. We direct our attention to the non-segregated and highly segregated communities, quantifying and comparing their structural topologies and k-core positions. When considering communities of both categories (controlling for size), our results show no differences in density and clustering but substantial variability in the core position. Larger non-segregated communities are more likely to occupy cores near the network nucleus, while the highly segregated ones tend to be closer to the network periphery. Finally, we analyse differences in citations gained by researchers within communities of different segregation categories. Researchers in highly segregated communities get more citations from their community members in middle cores and gain more citations per publication in middle/periphery cores. Those in non-segregated communities get more citations per publication in the nucleus. To our knowledge, this work is the first to characterise community segregation in co-authorship networks and investigate the relationship between community segregation and author citations. Our results help study highly segregated communities of scientific co-authors and can pave the way for intervention strategies to improve the growth and dissemination of scientific knowledge.

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