Path integrals, particular kinds, and strange things

Thoughts and thinkers: On the complementarity between objects and processes

We argue that "processes versus objects" is not a useful dichotomy. There is, instead, substantial theoretical utility in viewing "objects" and "processes" as complementary ways of describing persistence through time, and hence the possibility of observation and manipulation. This way of thinking highlights the role of memory as an essential resource for observation, and makes it clear that "memory" and "time" are also mutually inter-defined, complementary concepts. We formulate our approach in terms of the Free Energy Principle (FEP) of Friston and colleagues and the fundamental idea from quantum theory that physical interactions can be represented by linear operators. Following Levin (2024) [30], we emphasize that memory is, first and foremost, an interpretative function, from which the idea of memory as a record, at some level of accuracy, of past events is derivative. We conclude that the distinction between objects and processes is always contrived, and always misleading, and that science would be better served by abandoning it entirely.

Life, its origin, and its distribution: a perspective from the Conway-Kochen Theorem and the Free Energy Principle

We argue here that the Origin of Life (OOL) problem is not just a chemistry problem but is also, and primarily, a cognitive science problem. When interpreted through the lens of the Conway-Kochen theorem and the Free Energy Principle, contemporary physics characterizes all complex dynamical systems that persist through time as Bayesian agents. If all persistent systems are to some - perhaps only minimal - extent cognitive, are all persistent systems to some extent alive, or are living systems only a subset of cognitive systems? We argue that no bright line can be drawn, and we re-assess, from this perspective, the Fermi paradox and the Drake equation. We conclude that improving our abilities to recognize and communicate with diverse intelligences in diverse embodiments, whether based on familiar biochemistry or not, will either resolve or obviate the OOL problem.

Bayesian brain theory: Computational neuroscience of belief

Bayesian brain theory, a computational framework grounded in the principles of Predictive Processing (PP), proposes a mechanistic account of how beliefs are formed and updated. This theory assumes that the brain encodes a generative model of its environment, made up of probabilistic beliefs organized in networks, from which it generates predictions about future sensory inputs. The difference between predictions and sensory signals produces prediction errors, which are used to update belief networks. In this article, we introduce the fundamental principles of Bayesian brain theory, and show how the brain dynamics of prediction are associated with the generation and evolution of beliefs.

As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales.

Action of the Euclidean versus projective group on an agent's internal space in curiosity driven exploration

According to the Projective Consciousness Model (PCM), in human spatial awareness, 3-dimensional projective geometry structures information integration and action planning through perspective taking within an internal representation space. The way different perspectives are related to and transform a world model defines a specific perception and imagination scheme. In mathematics, such a collection of transformations corresponds to a 'group', whose 'actions' characterize the geometry of a space. Imbuing world models with a group structure may capture different agents' spatial awareness and affordance schemes. We used group action as a special class of policies for perspective-dependent control. We explored how such a geometric structure impacts agents' behaviors, comparing how the Euclidean versus projective groups act on epistemic value in active inference, drive curiosity, and exploration. We formally demonstrate and simulate how the groups induce distinct behaviors in a simple search task. The projective group's nonlinear magnification of information transformed epistemic value according to the choice of frame, generating behaviors of approach toward objects with uncertain locations due to limited sampling. The Euclidean group had no effect on epistemic value: no action was better than the initial idle state. In structuring a priori an agent's internal representation, we show how geometry can play a key role in information integration and action planning. Our results add further support to the PCM.

An Overview of Neurophenomenological Approaches to Meditation and Their Relevance to Clinical Research

There is a renewed interest in taking phenomenology seriously in consciousness research, contemporary psychiatry, and neurocomputation. The neurophenomenology research program, pioneered by Varela, rigorously examines subjective experience using first-person methodologies, inspired by phenomenology and contemplative practices. This review explores recent advancements in neurophenomenological approaches, particularly their application to meditation practices and potential clinical research translations. First, we examine innovative multidimensional phenomenological assessment tools designed to capture subtle, dynamic shifts in experiential content and structures of consciousness during meditation. These experience sampling approaches enable shedding new light on the mechanisms and dynamic trajectories of meditation practice and retreat. Second, we highlight how empirical studies in neurophenomenology leverage the expertise of experienced meditators to deconstruct aversive and self-related processes, providing detailed first-person reports that guide researchers in identifying novel behavioral and neurodynamic markers associated with pain regulation, self-dissolution, and acceptance of mortality. Finally, we discuss a recent framework, deep computational neurophenomenology, that updates the theoretical ambitions of neurophenomenology to naturalize phenomenology. This framework uses the formalism of deep parametric active inference, where parametric depth refers to a property of generative models that can form beliefs about the parameters of their own modeling process. Collectively, these methodological innovations, centered around rigorous first-person investigation, highlight the potential of epistemologically beneficial mutual constraints among phenomenological, computational, and neurophysiological domains. This could contribute to an integrated understanding of the biological basis of mental illness, its treatment, and its tight connections to the lived experience of the patient.

Bayesian Mechanics of Synaptic Learning Under the Free-Energy Principle

The brain is a biological system comprising nerve cells and orchestrates its embodied agent's perception, behavior, and learning in dynamic environments. The free-energy principle (FEP) advocated by Karl Friston explicates the local, recurrent, and self-supervised cognitive dynamics of the brain's higher-order functions. In this study, we continue to refine the FEP through a physics-guided formulation; specifically, we apply our theory to synaptic learning by considering it an inference problem under the FEP and derive the governing equations, called Bayesian mechanics. Our study uncovers how the brain infers weight changes and postsynaptic activity, conditioned on the presynaptic input, by deploying generative models of the likelihood and prior belief. Consequently, we exemplify the synaptic efficacy in the brain with a simple model; in particular, we illustrate that the brain organizes an optimal trajectory in neural phase space during synaptic learning in continuous time, which variationally minimizes synaptic surprisal.

Why the Fermi paradox may not be well explained by Wong and Bartlett's theory of civilization collapse. A Comment on: 'Asymptotic burnout and homeostatic awakening: a possible solution to the Fermi paradox?' (2022) by Wong and Bartlett

Wong and Bartlett explain the Fermi paradox by arguing that neither human nor extra-terrestrial civilizations can escape the time window singularity which, they claim, results from the way in which social characteristics of civilizations follow super-linear growth curves of cities. We question if data at the city level necessarily can lead to conclusions at the civilization level. More specifically, we suggest ways in which learnings from research, foresight, diversity and effective future government might act outside of their model to regulate super-linear growth curves of civilizations, and thus substantively increase the likelihood of civilizations progressing towards higher levels of the Kardashev scale. Moreover, we believe their claimed history of the collapse of terrestrial societies used to evidence their model is difficult to justify. Overall, we cast reasonable doubt on the ability of their proposed model to satisfactorily explain the Fermi paradox.

The free energy principle induces intracellular compartmentalization

Living systems at all scales are compartmentalized into interacting subsystems. This paper reviews a mechanism that drives compartmentalization in generic systems at any scale. It first discusses three symmetries of generic physical interactions in a quantum-theoretic description. It then shows that if one of these, a permutation symmetry on the inter-system boundary, is spontaneously broken, the symmetry breaking is amplified by the Free Energy Principle (FEP). It thus shows how compartmentalization generically results from permutation symmetry breaking under the FEP. It finally notes that the FEP asymptotically restores the broken symmetry, showing that the FEP can be regarded as a theory of fluctuations away from a permutation-symmetric boundary, and hence from an entangled joint state of the interacting systems.

Artificial consciousness: a perspective from the free energy principle

Does the assumption of a weak form of computational functionalism, according to which the right form of neural computation is sufficient for consciousness, entail that a digital computational simulation of such neural computations is conscious? Or must this computational simulation be implemented in the right way, in order to replicate consciousness?

From the perspective of Karl Friston’s free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial systems, but are not instantiated by computers with a classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness.

Making the Thermodynamic Cost of Active Inference Explicit

When describing Active Inference Agents (AIAs), the term "energy" can have two distinct meanings. One is the energy that is utilized by the AIA (e.g., electrical energy or chemical energy). The second meaning is so-called Variational Free Energy (VFE), a statistical quantity which provides an upper bound on surprisal. In this paper, we develop an account of the former quantity-the Thermodynamic Free Energy (TFE)-and its relationship with the latter. We highlight the necessary tradeoffs between these two in a generic, quantum information-theoretic formulation, and the macroscopic consequences of those tradeoffs for the ways that organisms approach their environments. By making this tradeoff explicit, we provide a theoretical basis for the different metabolic strategies that organisms from plants to predators use to survive.

'Snakes and ladders' in paleoanthropology: From cognitive surprise to skillfulness a million years ago

A paradigmatic account may suffice to explain behavioral evolution in early Homo. We propose a parsimonious account that (1) could explain a particular, frequently-encountered, archeological outcome of behavior in early Homo - namely, the fashioning of a Paleolithic stone 'handaxe' - from a biological theoretic perspective informed by the free energy principle (FEP); and that (2) regards instances of the outcome as postdictive or retrodictive, circumstantial corroboration. Our proposal considers humankind evolving as a self-organizing biological ecosystem at a geological time-scale. We offer a narrative treatment of this self-organization in terms of the FEP. Specifically, we indicate how 'cognitive surprises' could underwrite an evolving propensity in early Homo to express sporadic unorthodox or anomalous behavior. This co-evolutionary propensity has left us a legacy of Paleolithic artifacts that is reminiscent of a 'snakes and ladders' board game of appearances, disappearances, and reappearances of particular archeological traces of Paleolithic behavior. When detected in the Early and Middle Pleistocene record, anthropologists and archeologists often imagine evidence of unusual or novel behavior in terms of early humankind ascending the rungs of a figurative phylogenetic 'ladder' - as if these corresponded to progressive evolution of cognitive abilities that enabled incremental achievements of increasingly innovative technical prowess, culminating in the cognitive ascendancy of Homo sapiens. The conjecture overlooks a plausible likelihood that behavior by an individual who was atypical among her conspecifics could have been disregarded in a community of Hominina (for definition see Appendix 1) that failed to recognize, imagine, or articulate potential advantages of adopting hitherto unorthodox behavior. Such failure, as well as diverse fortuitous demographic accidents, would cause exceptional personal behavior to be ignored and hence unremembered. It could disappear by a pitfall, down a 'snake', as it were, in the figurative evolutionary board game; thereby causing a discontinuity in the evolution of human behavior that presents like an evolutionary puzzle. The puzzle discomforts some paleoanthropologists trained in the natural and life sciences. They often dismiss it, explaining it away with such self-justifying conjectures as that, maybe, separate paleospecies of Homo differentially possessed different cognitive abilities, which, supposedly, could account for the presence or absence in the Pleistocene archeological record of traces of this or that behavioral outcome or skill. We argue that an alternative perspective - that inherits from the FEP and an individual's 'active inference' about its surroundings and of its own responses - affords a prosaic, deflationary, and parsimonious way to account for appearances, disappearances, and reappearances of particular behavioral outcomes and skills of early humankind.

Principled Limitations on Self-Representation for Generic Physical Systems

The ideas of self-observation and self-representation, and the concomitant idea of self-control, pervade both the cognitive and life sciences, arising in domains as diverse as immunology and robotics. Here, we ask in a very general way whether, and to what extent, these ideas make sense. Using a generic model of physical interactions, we prove a theorem and several corollaries that severely restrict applicable notions of self-observation, self-representation, and self-control. We show, in particular, that adding observational, representational, or control capabilities to a meta-level component of a system cannot, even in principle, lead to a complete meta-level representation of the system as a whole. We conclude that self-representation can at best be heuristic, and that self models cannot, in general, be empirically tested by the systems that implement them.

Generating meaning: active inference and the scope and limits of passive AI

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

Federated inference and belief sharing

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.

Hybrid Life: Integrating biological, artificial, and cognitive systems

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural, and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and toward "life as it could be," with theoretical, synthetic, and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas, and contributions from a wide range of subjects. Hybrid Life brings our attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. Hybrid Life aims to cover studies that can lead to an understanding, from first principles, of what systems are and how biological and artificial systems can interact and integrate to form new kinds of hybrid (living) systems, individuals, and societies. To do so, it focuses on three complementary perspectives: theories of systems and agents, hybrid augmentation, and hybrid interaction. Theories of systems and agents are used to define systems, how they differ (e.g., biological or artificial, autonomous, or nonautonomous), and how multiple systems relate in order to form new hybrid systems. Hybrid augmentation focuses on implementations of systems so tightly connected that they act as a single, integrated one. Hybrid interaction is centered around interactions within a heterogeneous group of distinct living and nonliving systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022. This article is categorized under: Neuroscience > Cognition Philosophy > Artificial Intelligence Computer Science and Robotics > Robotics.