Topic: active inference

Develop computational and robotic models of learning, development and social cognition in Jun Tani’s Cognitive Neurorobotics Research Unit. Topics include recurrent networks, predictive coding, active inference, generalisation during development, executive control and working memory. Duties include model and task design, simulation or robotics experiments, analysis and first-author papers. The full-time appointment initially lasts two years; the expected annual salary is JPY 4.6–5.9 million. Recruitment remains open until all positions are filled, with an early start preferred. Follow the emai

Kathryn Nave discusses how living systems acquire meaning and intelligence through organization, constraints, and active inference. The conversation connects the free-energy principle and organizational closure to questions about brains, cognition, agency, and the boundaries of intelligent life.

According to a classical control-theoretic perspective on self-movement, our brains pilot our bodies to achieve goals, sometimes using open-loop predictive models and sometimes with closed-loop feedback. From this perspective, synchronization of self-motion with a moving target is a simple control problem, where error between body and target is minimized by some combination of responsive and predictive control. However, self-movement can also result from reconciliation of multisensory prediction errors. When subjected to a visual/proprioceptive conflict, our bodies respond spontaneously and un

The free-energy principle and active inference have received a significant attention in the fields of neuroscience and machine learning. However, it remains to be established whether active inference is an apt explanation for any given neural network that actively exchanges with its environment. To address this issue, we show that a class of canonical neural networks of rate coding models implicitly performs variational Bayesian inference under a well-known form of partially observed Markov decision process model (Isomura, Shimazaki, Friston, Commun Biol, 2022). Based on the proposed theory, w

At the foundation of human conscious experience lie basic embodied experiences of selfhood – experiences of simply ‘being alive’. In this talk, I will make the case that this central feature of human existence is underpinned by predictive regulation of the interior of the body, using the framework of predictive processing, or active inference. I start by showing how conscious experiences of the world around us can be understood in terms of perceptual predictions, drawing on examples from psychophysics and virtual reality. Then, turning the lens inwards, we will see how the experience of being

fMRI results have shown that the supplementary motor area (SMA) and the basal ganglia, most often discussed in their roles in generating action, are engaged by beat-based timing even in the absence of movement. Some have argued that the motor system is “recruited” by beat-based timing tasks due to the presence of motor-like timescales, but a deeper understanding of the roles of these motor structures is lacking. Reviewing a body of motor neurophysiology literature and drawing on the “active inference” framework, I argue that we can see the motor and timing functions of these brain areas as exa

Traditionally, vision has been largely treated as a bottom-up system that tries to make sense of the incoming input. However, contemporary accounts of active inference and predictive coding see the visual system as a goal-oriented system in which internal knowledge and learning fundamentally influence perception through top-down interactions. To study how internal visuo-motor predictions guide object visual processing, we combined high-density electrophysiology and a sequential learning task (N=41) in which subjects statistically learn the association between a sequence of objects (faces, scen