The paper by Mark Miller et al. conceptualizes resilience through active inference, a modeling approach for complex adaptive systems. It defines resilience in three ways: inertia (resisting change), elasticity (recovering from disturbances), and plasticity (expanding adaptive states), situating these concepts

The paper discusses using predictive coding (PC) for training neural networks on arbitrary graph topologies, addressing limitations of backpropagation (BP) in cyclic structures. It demonstrates how PC can perform flexible tasks by stimulating specific neurons, ultimately examining how graph topology

Mahault Albarracin and Riddhi J Pitliya provide insights on “The Markov blanket trick” by Raja and colleagues. They acknowledge the critique of the Free Energy Principle (FEP) as interesting yet flawed, highlighting its philosophical nature while appreciating the authors’ technical

Resources – Scientific Research Papers AN ACTIVE INFERENCE APPROACH TO SEMIOTICS Antoine Milette-Gagnon, Samuel PL Veissière, Karl J Friston, Maxwell JD Ramstead November 14, 2022 https://books.google.com/books?hl=en&lr=&id=hPCKEAAAQBAJ&oi=fnd&pg=PT90&dq=info:4nbBr-hRLywJ:scholar.google.com&ots=swlVMAqd4g&sig=pbcTzDigc55YVsjIYwkCCgSUc4I#v=onepage&q&f=false Abstract: Recent decades have borne witness to the emergence of new frameworks in psychology

Resources – Scientific Research Papers Natural Language Syntax Complies with the Free-Energy Principle Elliot Murphy, Emma Holmes, Karl Friston October 26, 2022 https://arxiv.org/abs/2210.15098 Abstract: Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in

The study presents strategies to enhance computational efficiency in image-based reinforcement learning for robotics, addressing limitations of large models on available hardware. By reducing model complexity, downscaling input resolution, and utilizing weight quantization, the authors achieve up to 300 times

The paper explores how digital social platforms manipulate attention and social interactions through the concept of digital affordances. It highlights how these platforms are designed to maximize user engagement by creating rewarding experiences, particularly in the context of the COVID-19

This paper explores active inference in modeling intelligent agents by minimizing free energy. It critiques the entangled state space from deep learning methods, proposing a model that disentangles object shape, pose, and category using the ShapeNet dataset. Results indicate that

This work presents a method for selecting policies in active inference by mapping policies to a vector embedding space. It utilizes k-means clustering for efficient sampling of expected free energy, enabling focused searches around promising policies. The approach is applied

The paper critiques traditional social neuroscience for its detached third-person perspective and introduces second-person neuroscience, emphasizing real-time interactions. Utilizing the active inference framework, it explores neural mechanisms during social engagement, highlighting the importance of mutual predictions and bodily adaptation. This

This paper investigates the relationship between individual and collective inference in multi-agent Bayesian systems using spin glass models. It reveals that collective dynamics of active inference agents resemble sampling from a spin glass’s stationary distribution, though this equivalence is delicate

The paper discusses the development of capsule networks as generative models for visual scene recognition, emphasizing their architecture that uses dynamic routing through capsules to extract features and pose information. It presents a new routing algorithm based on iterative inference

Resources – Scientific Research Papers A worked example of the Bayesian mechanics of classical objects Dalton AR Sakthivadivel September 19, 2022 https://link.springer.com/chapter/10.1007/978-3-031-28719-0_21 Abstract: Bayesian mechanics is a new approach to studying the mathematics and physics of interacting stochastic processes. We

The paper explores the intersection between Husserlian phenomenology and active inference, proposing a mathematical model of perception that incorporates prior knowledge and expectations. It reviews active inference and key aspects of Husserl’s phenomenology, particularly time consciousness, ultimately illustrating how these

The commentary by Conor Heins critiques the target article on the Free Energy Principle (FEP) by Aguilera et al., focusing on linear diffusion processes. Heins offers insights into the marginal flows of conditional modes, analyzing how these “particular states” influence

The authors respond to Aguilera et al.’s critique of the free energy principle, addressing ambiguities in system dynamics and misinterpretations of surprisal and variational free energy. They argue that despite these critiques, the original findings remain valid and resilient under

The editorial examines the evolution of the affordance concept within the active inference framework, differentiating between Affordance 1.0, 2.0, and the author’s proposed Affordance 3.0. It argues that while active inference deviates from ecological psychology’s foundations, applying this concept in

The paper explores the relationship between Husserlian phenomenology and active inference, proposing a mathematical framework for understanding perception through prior knowledge and expectations. It aims to enhance computational phenomenology by aligning Husserl’s concepts of consciousness, particularly time consciousness, with active

The paper explores the free energy principle (FEP) and addresses the map-territory fallacy in scientific modeling. It asserts that critiques of the FEP based on this fallacy are themselves fallacious. The authors emphasize the FEP’s distinctiveness in modeling interactions within

Maxwell JD Ramstead critiques Raja et al.’s 2021 paper on the Markov blanket trick, acknowledging the validity of their argument but identifying a flawed premise that weakens their conclusions. He suggests that subsequent research on the free energy principle provides

The article discusses the nature of beliefs in cognitive science, describing them as propositional attitudes with representational content and assumed veracity. Beliefs shape cognition by influencing perceptions, behaviors, and decision-making, often remaining unconscious. They provide agents with a coherent understanding

The commentary analyzes the role of the free-energy principle (FEP) in defining cognitive system boundaries within third-wave extended-mind research. It emphasizes that Markov blankets connect internal and external states, highlighting the plasticity of these boundaries and the relevance of niche

The authors demonstrate that systems with morphological degrees of freedom and constrained free energy evolve toward neuromorphic structures that facilitate hierarchical computations. This concept is applicable in various biological contexts, linking quantum reference frames and topological quantum field theories to

The paper by Millidge and Buckley explores the relationship between classical reinforcement learning, Bayesian filtering, and Active Inference, proposing a novel agent architecture using successor representations. This new model shows benefits in planning horizons and computational efficiency, allowing generalization to

This paper by Dalton AR Sakthivadivel explores high-dimensional random dynamical systems with sparse couplings to external systems, focusing on controlled exchanges. Utilizing the adiabatic theorem, it demonstrates that such coupling structures are common in complex systems, thereby validating K Friston’s

The editorial discusses belief in cognitive science, particularly through the lens of computational neuroscience, defining beliefs as representations of reality. It introduces active inference, a theory detailing how organisms update beliefs and minimize uncertainty. The article also explores belief dynamics

This commentary addresses a technical analysis of the Free Energy Principle by Aguilera et al., challenging the claim that sparse coupling ensures Markov blankets in certain stochastic differential equations. It clarifies conditions under which Markov blankets arise, emphasizing the implications

The author critiques a recent paper on the free energy principle (FEP) by Aguilera et al., arguing their points on its limitations are valid but not detrimental to the FEP. A proposed path-based formulation of the FEP is suggested to

The article discusses the Free Energy Principle (FEP) as a unified model for understanding adaptive behaviors in complex systems, extending beyond the human brain to include various biological phenomena. It aims to explore the FEP’s applicability to different organisms and

This paper presents a probabilistic generative model for morphisms in free monoidal categories, linking applied category theory and machine learning. It demonstrates how acyclic directed wiring diagrams can specify morphisms and employs amortized variational inference for parameter learning and latent

The paper formulates Bayesian mechanics via axiomatic principles, arguing that constrained dynamical systems infer against these constraints, implying environmental influences. It links classical dynamical systems theory to Bayesian inference, using concepts like Shannon entropy and gauge degrees of freedom. This

The article discusses a novel approach to object recognition and representation learning, emphasizing the importance of interactive learning, akin to human cognition. It introduces a Cortical Column Network (CCN) that predicts object transformations through active inference, enhancing classification accuracy and

The paper discusses how stress induces physiological changes that lead to a loss of predictive confidence, resulting in depression. It uses the active inference framework to explain how organisms manage energy resources. The authors identify neuroendocrine and immunological systems as

Active inference presents a cohesive theory of perception, learning, and decision-making. The authors address concerns about its compatibility with folk psychology’s beliefs and desires, demonstrating that active inference includes elements reflecting desires. They explore how this framework can enhance folk-psychological

The paper explores the dynamics of information spread and confirmation bias within epistemic communities using an active inference model. It reveals how agents create echo chambers by selectively engaging with content that reinforces their beliefs, resulting in entrenched views and

This paper introduces a generative modelling approach to neurophenomenology, merging computational neuroscience with phenomenological philosophy. It reviews objections to naturalized phenomenology, outlines generative modelling frameworks, and details the authors’ method for creating a computational model that interprets lived experiences, distinguishing

The paper introduces Action Perception Divergence (APD), a framework for categorizing objective functions in intelligent agents. It distinguishes between narrow, task-specific rewards, and broad objectives maximizing information through interactions with the environment. The authors assert that effective exploration and adaptability

The paper explores why Bayesian inference in the brain contributes to poor performance on explicit probabilistic reasoning tasks. It distinguishes between inferring probability distributions and representing probabilities as hidden states, asserting that effective reasoning relies on a generative model of

This research explores canonical neural networks, demonstrating that they perform active inference and learning via rate coding models. It reveals that plasticity modulation with delay allows for minimising future risk, aligning with principles of Bayesian belief updating. The findings highlight

The post introduces pymdp, a Python library designed for simulating active inference in complex systems using partially-observable Markov Decision Processes (POMDPs). The authors aim to enhance accessibility and user-friendliness of active inference for researchers from diverse backgrounds, promoting innovation and

The paper discusses advancements in unsupervised reinforcement learning (RL), focusing on enhancing data efficiency in high-dimensional environments. The authors propose a method where an agent collects experience through self-supervised exploration to pre-train a world model. This model, combined with a

The paper discusses the construction of spatial maps for navigation, proposing that a unified mechanism underlies the chunking of sensory data in both time and space. By developing a hierarchical generative model of perception and action, the authors relate their