About
30
Publications
6,336
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376
Citations
Citations since 2017
Introduction
Manuel is a researcher at Araya Inc., Tokyo, Japan. Previously, he was a JSPS-Royal Society Postdoctoral Research Fellow at RIKEN CBS in Saitama, Japan.
His interests include models of the origins of life and cognition, decision making and sensorimotor interactions. His work relies on theories from dynamical systems, control and information theory, embodied and enactive cognitive science, artificial intelligence, artificial life, cognitive and behavioural neuroscience.
Additional affiliations
March 2019 - October 2019
February 2019 - June 2019
Position
- Fellow
Description
- (Co-)Running a module on Artificial Neural Networks and occasionally giving lectures in Machine Learning.
Education
October 2015 - July 2019
October 2013 - September 2014
October 2009 - September 2012
Publications
Publications (30)
Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical...
The 35 commentaries cover a wide range of topics and take many different stances on the issues explored by the target article. We have organised our response to the commentaries around three central questions: Are Friston blankets just Pearl blankets? What ontological and metaphysical commitments are implied by the use of Friston blankets? What kin...
When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge...
The Kalman filter is an algorithm for the estimation of hidden variables in dynamical systems under linear Gauss-Markov assumptions with widespread applications across different fields. Recently, its Bayesian interpretation has received a growing amount of attention especially in neuroscience, robotics and machine learning. In neuroscience, in part...
The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the l...
Rodents use whisking to probe actively their environment and to locate objects in space, hence providing a paradigmatic biological example of active sensing. Numerous studies show that the control of whisking has anticipatory aspects. For example, rodents target their whisker protraction to the distance at which they expect objects, rather than jus...
Rodents use whisking to probe actively their environment and to locate objects in space, hence providing a paradigmatic biological example of active sensing. Numerous studies show that the control of whisking has anticipatory aspects. For example, rodents target their whisker protraction to the distance at which they expect objects, rather than jus...
[A heavily rewritten version of this paper has been published in BBS in 2021]
Markov blankets have been used to settle disputes central to philosophy of mind and cognition. Their development from a technical concept in Bayesian inference to a central concept within the free-energy principle is analysed. We propose to distinguish between instrumenta...
Active inference introduces a theory describing action-perception loops via the minimisation of variational (and expected) free energy or, under simplifying assumptions, (weighted) prediction error. Recently, active inference has been proposed as part of a new and unifying framework in the cognitive sciences: predictive processing. Predictive proce...
Linear Quadratic Gaussian (LQG) control is a framework first introduced in control theory that provides an optimal solution to linear problems of regulation in the presence of uncertainty. This framework combines Kalman-Bucy filters for the estimation of hidden states with Linear Quadratic Regulators for the control of their dynamics. Nowadays, LQG...
The connections between optimal control and Bayesian inference have long been recognised, with the field of stochastic (optimal) control combining these frameworks for the solution of partially observable control problems. In particular, for the linear case with quadratic functions and Gaussian noise, stochastic control has shown remarkable results...
The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimot...
In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. 'Active inference' is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of h...
In psychology and neuroscience it is common to de- scribe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches...
The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimoto...
In psychology and neuroscience it is common to describe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches to...
![Figure 1. A PID controller [46]. The prediction error e(t) is given by...](publication/331626799/figure/fig1/AS:734381798875137@1552101673432/A-PID-controller-46-The-prediction-error-et-is-given-by-the-difference-between-a_Q320.jpg)
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical...
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical...
![Fig. 1. A PID controller [2]. The prediction error e(t) is given by the...](profile/Manuel-Baltieri/publication/326619825/figure/fig1/AS:791205671620609@1565649540829/A-PID-controller-2-The-prediction-error-et-is-given-by-the-difference-between-a_Q320.jpg)
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition wit...
The assumption that action and perception can be investigated independently is entrenched in theories, models and experimental approaches across the brain and mind sciences. In cognitive science, this has been a central point of contention between computationalist and 4Es (enactive, embodied, extended and embedded) theories of cognition, with the f...
![Fig. 1. A PID controller [2]. The prediction error e(t) is given by the...](publication/345672008/figure/fig1/AS:956284870348801@1605007488389/A-PID-controller-2-The-prediction-error-et-is-given-by-the-difference-between-a_Q320.jpg)
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition wit...
Active inference is emerging as a possible unifying theory of perception and action in cognitive and computational neuroscience. On this theory, perception is a process of inferring the causes of sensory data by minimising the error between actual sensations and those predicted by an inner \emph{generative} (probabilistic) model. Action on the othe...
Research on the so-called "free-energy principle'' (FEP) in cognitive
neuroscience is becoming increasingly high-profile. To date, introductions to
this theory have proved difficult for many readers to follow, but it depends
mainly upon two relatively simple ideas: firstly that normative or teleological
values can be expressed as probability distri...
Projects
Projects (3)
The goal of this project is to challenge a routinely used interpretation of generative models in the cognitive sciences where they are usually depicted as accurate descriptions of world dynamics. We propose instead that simpler characterisations in terms of sensorimotor contingencies and approximate dynamics better describe their possible role in helping to understand cognition.
Dominant theories of motor control borrow the mathematical framework of LQG (Linear Quadratic Gaussian) control from control engineering. This perspective is challenged by active inference, relying on insights derived from the equilibrium point hypothesis and referent control. This project develops a comparison of these ideas, from their philosophical implications to theories of cognition, to their implementation on simple models for the control of joints.
Describe the connections between theories of active inference and established methods of control theory with the goal of, possibly, extending them.
