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: 27 June 2025 | Viewed by 380
Special Issue Editor
Interests: neurocomputational modelling; machine learning; neural networks; deep learning; bayesian reinforcement learning; visual perception; cognitive number processing; decision making and planning; spatial navigation
Special Issues, Collections and Topics in MDPI journals
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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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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