Distributed Science – The Scientific Process as Multi-Scale Active Inference (2023) | Balzan et al | osf.io

Abstract

The scientific process plays out in a multi-scale system comprising subsystems, each with their own properties and dynamics. For the practice of science to generate useful world models — and lead to the development of enabling technologies — practicing scientists, their theories, methods, dissemination, and infrastructure (e.g., funding and laboratories) must all fit together in an orchestrated manner. Scientific practice has broad societal implications that go beyond mere scientific progress: we base our decisions on theoretical (i.e., models and forecasts) and technological (e.g., vaccines and smartphones) scientific advances. This paper applies the free energy principle to provide a multi-scale description of science understood as evidence-seeking processes in a nested hierarchy of living (biological and behavioural) and epistemic (linguistic) structures. This allows us to naturalise the scientific process — as distributed self-evidencing — in terms of dynamics that can be read as inference or Bayesian belief updating; i.e., processes that maximize the evidence for a generative model of the sensed and measured world. The ensuing meta-theoretical approach dispels the notion of science as truth-pointing and foregrounds inference to the best explanation — as evinced by the beliefs of scientists and their encultured niche. Crucially, it furnishes a way of simulating the practice of science, which may have a foundational role in the next generation of augmented intelligence systems. Epistemologically, it also addresses some key questions; e.g., is science a special? And in what ways is scientific pursuit an existential imperative for all beings? These questions may be foundational in how we use and design intelligent systems.

New project for a scientific psychology: General scheme | Mark Solms (2020) | Neuropsychoanalysis

This is a revision of Freud’s “Project for a Scientiﬁc Psychology: General Scheme.” It updates the original, sentence for sentence where possible, in light of contemporary neuroscientiﬁc knowledge. The principle revisions are as follows. (1) Freud’s conception of “quantity” (the precursor of “drive energy”) is replaced by the concept of “free energy.” This is the energy within a system that is not currently performing useful work. (2) Shannon’s conception of “information” is introduced, where information is equivalent to unpredictability, and is formally equivalent to “entropy” in physics. (3) In biology, the fundamental purpose of “homeostasis” is to resist entropy – i.e., to increase predictability. Homeostasis turns out to be the underlying mechanism of what Freud called the “principle of neuronal inertia.” (4) Freud’s conception of “contact barriers” (the physical vehicles of memory) is linked with the modern concepts of consolidation/reconsolidation, whereby more deeply consolidated predictions are less plastic (more resistant to change) than freshly consolidated ones. (5) Freud’s notion of sensory “excitation” is replaced with the concept of “prediction error,” where only that portion of sensory input which is not explained by outgoing predictions is propagated inwards for cognitive processing. (6) Freud’s conception of “bound” (inhibited) cathexis, the main vehicle of his “secondary process” and voluntary action is equated with the buﬀering function of “working memory”; and “freely mobile” cathexis (the vehicle of Freud’s “primary process”) is equated with the automatized response modes of the nondeclarative memory systems. (7) Freud’s notion of ω (the system “consciousness”) is replaced by the concept of “precision” modulation, also known as “arousal” and “postsynaptic gain.”

First principles in the life sciences: the free-energy principle, organicism, and mechanism | Matteo Colombo and Cory Wright

Abstract

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences.

Keywords Adaptation · Free energy · Life · Mechanism · Organicism

Epistemological and Ethical Implications of the Free Energy Principle | Eray Özkural

The free energy principle states that self-organization occurs through minimization of free energy, which is a measure of potential thermodynamic work. By minimizing free energy, the organism happens to also minimize surprise over its boundary, promoting chances of survival. We discuss the ethical implications of the cognitive goal in detail from an empirical point of view, highlighting the principle of least action as a physical basis of Occam’s razor, the universality of the free energy principle, and its explanation of natural selection. We explain that the free energy principle extends to groups of organisms and helps us understand group-scale adaptations and selection in biology. The free energy principle applies to all scales of organization in the organism from single cells to the entire nervous system. When this principle is taken to its logical extremes of modeling groups, populations and ecosystems, we uncover a new, evolutionarily sensible path at explaining puzzling aspects of human motivation and judgement, including ethical decisions. To minimize free energy, populations have to act to maximize gathering of information, while building effective models at mitigating changes to its dynamic structure. The free energy principle thus provides a naturalistic explanation of some of our deepest ethical intuitions, and valuable principles of social behavior. We interpret the cognitive goal that corresponds to the principle as seeking a dynamic, fruitful, yet peaceful activity that sustains the organism. This state of mind is interestingly similar to the Buddhist intuition of mental equanimity; the organism’s final goal is to be at peace and harmony with the environment. Another immediately relevant aspect is that assemblies must form to promote symbiotic, synergistic, positive feedback loops, which coincides with the findings of ecologists. Therefore, ethics naturally emerges in self-organizing systems. Assemblies of organisms must ultimately unite in macro-minds to achieve the greatest reduction in free energy, as well as building technological extensions of themselves to improve their capacity to do such, therefore the principle also predicts a post-singularity world-mind composed mostly of artificial intelligence.