Embodied and Extended Cognition

Precision not prediction: Body-ownership illusion as a consequence of online precision adaptation under Bayesian inference

The paper presents a computational model showing that body-ownership illusions arise from precision adaptation in Bayesian inference during sensory stimulation.

Human Brain Dynamics Dissociate Early Perceptual and Late Motor-Related Stages of Affordance Processing

The paper examines affordance perception by comparing early perceptual and late motor-related ERP components in different movement contexts, finding that affordance perception shifts from automated to context-dependent over time.

Cerebellar state estimation enables resilient coupling across behavioural domains

This paper proposes a cerebellar model for state estimation that links neuronal dynamics with behavioral coordination, using the free energy principle to explain contextual behavior.

Needing: an active inference process for physiological motivation

This paper proposes a model of needing as an active inference process that reduces pervasive surprise, demonstrating its influence on reward processing and behavior through simulations.

Knowing what you don't know: Estimating the uncertainty of feedforward and feedback inputs with prediction-error circuits

The paper proposes a network model to estimate sensory and prediction uncertainty using prediction-error circuits and investigates how inhibitory interneurons influence input weighting.

Deep kinematic inference affords efficient and scalable control of bodily movements

The paper introduces an Active Inference architecture for efficient control of complex kinematic chains by mapping extrinsic to intrinsic coordinates through inference.

Body size as a metric for the affordable world

This study explores how human body size influences perception of environmental affordances, finding that body size defines a boundary for action possibilities and that this boundary is not exclusive to embodied organisms.

A stochastic world model on gravity for stability inference

The paper investigates how humans infer object stability using a stochastic world model of gravity, finding that gravity's direction is represented as a Gaussian distribution, which explains perceptual illusions and supports efficient stability inference.

Predictive neural representations of naturalistic dynamic input

This paper introduces a dynamic extension to representational similarity analysis to study predictive neural representations in naturalistic dynamic input using MEG data.

Flexible Intentions: An Active Inference Theory

The paper presents a computational theory using Active Inference to model goal-directed actions, showing how the PPC and DVS support dynamic behavior through flexible intentions.

ACTION AFFORDANCE AFFECTS PROXIMAL AND DISTAL GOAL-ORIENTED PLANNING

This study examines how action affordance realism affects eye movement patterns during goal-oriented tool interaction in virtual environments.

Individual prediction tendencies do not generalise across modalities

This study shows that individual prediction tendencies in sensory modalities do not generalise across modalities, with poor internal consistency between auditory and visual modalities.

Predictive Motor Control Based on a Generative Adversarial Network

This paper explores using generative adversarial networks to model predictive motor control, demonstrating their ability to learn motor dynamics and perform forward and inverse model computations.

A Sensory-Motor Theory of the Neocortex based on Active Predictive Coding

This paper proposes a theory of the neocortex based on active predictive coding, demonstrating how it can solve various sensory-motor problems through hierarchical modeling and planning.

Predictive coding is a consequence of energy efficiency in recurrent neural networks

This paper shows that predictive coding in neural networks arises from energy efficiency rather than requiring hard-wired hierarchical connectivity.

Learning to predict based on self- versus externally induced prediction violations: a direct comparison using a Bayesian inference modelling approach

The paper compares self-generated and externally induced predictions using a Bayesian inference model in a spatial attention task, finding larger reaction time costs for violated self-generated predictions.

Experimental validation of the free-energy principle with in vitro neural networks

The paper validates the free-energy principle in vitro using rat cortical neurons, showing that free energy minimization predicts network self-organization and plasticity.

In the Body’s Eye: The Computational Anatomy of Interoceptive Inference

This paper introduces a formal model of cardiac active inference to explain how cardiac signals influence exteroceptive perception and confidence, and demonstrates its implications for fear perception and physiological reactivity.

Differentiating Bayesian model updating and model revision based on their prediction error dynamics

This study empirically distinguishes Bayesian model updating and revision by analyzing prediction error dynamics during learning tasks.

Prediction-error neurons in circuits with multiple neuron types: Formation, refinement and functional implications

The paper develops a computational model to study how prediction-error neurons form and function in networks with multiple inhibitory interneurons, showing their robustness and functional roles.

Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using Active Inference

This paper uses Active Inference to simulate and model different forms of interoceptive control, including homeostatic, allostatic, and goal-directed, and their dysfunctions.

Canonical neural networks perform active inference

This paper shows canonical neural networks perform active inference by minimizing variational free energy through delayed plasticity and threshold adaptation.

Lifelong learning of cognitive strategies for physical problem-solving: the effect of embodied experience

The paper compares cognitive strategies in individuals with and without congenital limb differences in a virtual problem-solving task, finding differences in thinking time and persistence despite equal motor capabilities.

Egocentric Biases are Determined by the Precision of Self-related Predictions.

The paper tests if self-related predictions influence perception of others' emotions based on their precision, finding that precise predictions increase egocentric bias.

Architectural Affordance Impacts Human Sensorimotor Brain Dynamics

This study shows that alpha-band oscillations in specific brain regions correlate with architectural affordances, influencing sensorimotor dynamics during interaction with the environment.

The Effect of Uncertainty on Prediction Error in the Action-Perception Loop

The paper shows that uncertainty affects prediction error minimization and agency judgments in the action-perception loop, with differences observed in autism traits.

Deep Active Inference and Scene Construction

This paper develops a hierarchical active inference model for visual scene construction, showing it generates empirical evidence accumulation phenomena and novel behaviors in uncertain environments.

Sources of predictive information in dynamical neural networks

This paper investigates how predictive information in dynamical neural networks originates, showing it can come from the environment or the agent's internal dynamics, and that it does not reliably indicate behavioral performance.

The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness

This paper presents a neurocomputational model based on Active Inference to address limitations in the global neuronal workspace theory, demonstrating its ability to reproduce experimental findings and extend the GNW taxonomy.

A Bayesian account of generalist and specialist formation under the Active Inference framework

The paper uses Active Inference to simulate agents learning habit formation strategies in different environments, finding that specialization is advantageous in stable environments while generalization occurs in volatile ones.