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Active Inference in Cognitive Neuroscience

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4223

Special Issue Editor


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Guest Editor
Institute of Cognitive Science and Technologies (ISTC), National Research Council (CNR) of Italy, Via Martiri della Libertà 2, 35137 Padova, Italy
Interests: neurocomputational modelling; machine learning; neural networks; deep learning; bayesian reinforcement learning; visual perception; cognitive number processing; decision making and planning; spatial navigation
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Special Issue Information

Dear Colleagues,

Active inference is a normative computational theory of cognitive processing developed by Karl Friston, inspired by historical insights from Helmholtz and more recent “Bayesian brain” predictive coding perspectives. It entails a generative model that integrates sensory and internal representations of the world, enabling the brain to predict and interpret observations, encode goals (e.g., desires, intentions), predict the consequences of actions, plan, make choices, and exert control.

Perception, control, and learning are unified under the objective of minimizing surprise: perception refines internal representations, control selects actions that fulfill internal predictions, and learning improves the generative model itself.

The theory exists in several forms, including continuous-time active inference, addressing lower-level processes such as perception and motor control; discrete-time active inference, which supports higher-level cognitive phenomena like decision making and planning; and hybrid active inference, enabling full-fledged cognition and, more broadly, intelligent behavior. Building on the increasing attention Entropy has devoted to active inference in recent years—and the growing research interest from phenomenological disciplines—the goal of this Special Issue is to advance active inference as a theory of cognition by gathering novel empirical and computational evidence from a cognitive neuroscience perspective.

We invite original submissions, commentaries, review articles, and highlights of key innovations, focusing on the following:

  • Novel empirical evidence for active inference (behavioral, neural, clinical);
  • Interpretations of published data through an active inference lens;
  • Computational advances with an emphasis of biological plausibility;
  • Philosophical essays exploring theoretical implications;
  • Applications of active inference, such as its use as a research tool or for human–machine interaction.

Dr. Ivilin Stoianov
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • active inference
  • predictive coding
  • free energy principle
  • prediction errors
  • neurocomputational modeling
  • perception
  • motor control
  • cognitive control
  • decision making
  • cognitive neuroscience
  • neuropsychology
  • psychiatry

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Published Papers (5 papers)

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Research

21 pages, 2034 KB  
Article
Brain Oscillations and Autonomic Synthonization via Comodulation in Collaborative Negotiation
by Katia Rovelli, Carlotta Acconito, Laura Angioletti and Michela Balconi
Entropy 2025, 27(8), 873; https://doi.org/10.3390/e27080873 - 18 Aug 2025
Viewed by 344
Abstract
This study investigates the relationship between neural and physiological synthonization via comodulation (Synth) in dyadic exchanges centered on negotiation processes. In total, 13 dyads participated in a negotiation task with three phases: Initiation (IP), Negotiation Core (NCP), and Resolution (RP). Electroencephalographic (EEG) frequency [...] Read more.
This study investigates the relationship between neural and physiological synthonization via comodulation (Synth) in dyadic exchanges centered on negotiation processes. In total, 13 dyads participated in a negotiation task with three phases: Initiation (IP), Negotiation Core (NCP), and Resolution (RP). Electroencephalographic (EEG) frequency bands (i.e., delta, theta, alpha) and autonomic responses (heart rate variability, HRV) were recorded. Synth was analyzed using Euclidean distance (EuDist) for EEG and autonomic indices. Significant Synth in delta, theta, and alpha bands in temporo-central and parieto-occipital regions was observed, indicating social cognitive alignment. HRV Synth was higher during the NCP than IP, suggesting better coordination. Based on this result, a cluster analysis was performed on HRV EuDist to identify distinct groups based on HRV, and eventually personality patterns, that revealed one cluster with higher Synth and reward sensitivity, and another with lower Synth and reward sensitivity. These findings show how neural and autonomic Synth enhances social cognition and emotional regulation. Full article
(This article belongs to the Special Issue Active Inference in Cognitive Neuroscience)
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23 pages, 8311 KB  
Article
Active Inference with Dynamic Planning and Information Gain in Continuous Space by Inferring Low-Dimensional Latent States
by Takazumi Matsumoto, Kentaro Fujii, Shingo Murata and Jun Tani
Entropy 2025, 27(8), 846; https://doi.org/10.3390/e27080846 - 9 Aug 2025
Viewed by 497
Abstract
Active inference offers a unified framework in which agents can exhibit both goal-directed and epistemic behaviors. However, implementing policy search in high-dimensional continuous action spaces presents challenges in terms of scalability and stability. Our previously proposed model, T-GLean, addressed this issue by enabling [...] Read more.
Active inference offers a unified framework in which agents can exhibit both goal-directed and epistemic behaviors. However, implementing policy search in high-dimensional continuous action spaces presents challenges in terms of scalability and stability. Our previously proposed model, T-GLean, addressed this issue by enabling efficient goal-directed planning through low-dimensional latent space search, further reduced by conditioning on prior habituated behavior. However, the lack of an epistemic term in minimizing expected free energy limited the agent’s ability to engage in information-seeking behavior that can be critical for attaining preferred outcomes. In this study, we present EFE-GLean, an extended version of T-GLean that overcomes this limitation by integrating epistemic value into the planning process. EFE-GLean generates goal-directed policies by inferring low-dimensional future posterior trajectories while maximizing expected information gain. Simulation experiments using an extended T-maze task—implemented in both discrete and continuous domains—demonstrate that the agent can successfully achieve its goals by exploiting hidden environmental information. Furthermore, we show that the agent is capable of adapting to abrupt environmental changes by dynamically revising plans through simultaneous minimization of past variational free energy and future expected free energy. Finally, analytical evaluations detail the underlying mechanisms and computational properties of the model. Full article
(This article belongs to the Special Issue Active Inference in Cognitive Neuroscience)
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42 pages, 3822 KB  
Article
The Criticality of Consciousness: Excitatory–Inhibitory Balance and Dual Memory Systems in Active Inference
by Don M. Tucker, Phan Luu and Karl J. Friston
Entropy 2025, 27(8), 829; https://doi.org/10.3390/e27080829 - 4 Aug 2025
Viewed by 1403
Abstract
The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for [...] Read more.
The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of EI criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article
(This article belongs to the Special Issue Active Inference in Cognitive Neuroscience)
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33 pages, 3843 KB  
Article
Deep Hybrid Models: Infer and Plan in a Dynamic World
by Matteo Priorelli and Ivilin Peev Stoianov
Entropy 2025, 27(6), 570; https://doi.org/10.3390/e27060570 - 27 May 2025
Cited by 1 | Viewed by 565
Abstract
To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal casts planning [...] Read more.
To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal casts planning and control as an inference process. Active inference assumes that action and perception are two complementary aspects of life whereby the role of the former is to fulfill the predictions inferred by the latter. Here, we present an active inference approach that exploits discrete and continuous processing, based on three features: the representation of potential body configurations in relation to the objects of interest; the use of hierarchical relationships that enable the agent to easily interpret and flexibly expand its body schema for tool use; the definition of potential trajectories related to the agent’s intentions, used to infer and plan with dynamic elements at different temporal scales. We evaluate this deep hybrid model on a habitual task: reaching a moving object after having picked a moving tool. We show that the model can tackle the presented task under different conditions. This study extends past work on planning as inference and advances an alternative direction to optimal control. Full article
(This article belongs to the Special Issue Active Inference in Cognitive Neuroscience)
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18 pages, 286 KB  
Article
The Physics and Metaphysics of Social Powers: Bridging Cognitive Processing and Social Dynamics, a New Perspective on Power Through Active Inference
by Mahault Albarracin, Sonia de Jager and David Hyland
Entropy 2025, 27(5), 522; https://doi.org/10.3390/e27050522 - 14 May 2025
Viewed by 838
Abstract
Power operates across multiple scales, from physical action to complex social dynamics, and is constrained by fundamental principles. In the social realm, power is shaped by interactions and cognitive capacity: socially-facilitated empowerment enhances an agent’s information-processing ability, either by delegating tasks or leveraging [...] Read more.
Power operates across multiple scales, from physical action to complex social dynamics, and is constrained by fundamental principles. In the social realm, power is shaped by interactions and cognitive capacity: socially-facilitated empowerment enhances an agent’s information-processing ability, either by delegating tasks or leveraging collective resources. This computational advantage expands access to policies and buffers against vulnerabilities, amplifying an individual’s or group’s influence. In AIF, social power emerges from the capacity to attract attention and process information effectively. Our semantic habitat—narratives, ideologies, representations, etc.—functions through attentional scripts that coordinate social behavior. Shared scripts shape power dynamics by structuring collective attention. Speculative scripts serve as cognitive tools for low-risk learning, allowing agents to explore counterfactuals and refine predictive models. However, dominant scripts can reinforce misinformation, echo chambers, and power imbalances by directing collective attention toward self-reinforcing policies. We argue that power through scripts stems not only from associations with influential agents but also from the ability to efficiently process information, creating a feedback loop of increasing influence. This reframes power beyond traditional material and cultural dimensions, towards an informational and computational paradigm—what we term possibilistic power, i.e., the capacity to explore and shape future trajectories. Understanding these mechanisms has critical implications for political organization and technological foresight. Full article
(This article belongs to the Special Issue Active Inference in Cognitive Neuroscience)
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