1
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Han D, Doya K, Li D, Tani J. Synergizing habits and goals with variational Bayes. Nat Commun 2024; 15:4461. [PMID: 38796491 DOI: 10.1038/s41467-024-48577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 05/06/2024] [Indexed: 05/28/2024] Open
Abstract
Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed (flexible but slow). While these two types of behaviors are typically considered to be managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian intention variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision making.
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Affiliation(s)
- Dongqi Han
- Microsoft Research Asia, Shanghai, 200232, China.
| | - Kenji Doya
- Okinawa Institute of Science and Technology, Okinawa, 904-0495, Japan
| | - Dongsheng Li
- Microsoft Research Asia, Shanghai, 200232, China
| | - Jun Tani
- Okinawa Institute of Science and Technology, Okinawa, 904-0495, Japan
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2
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Zhang Z, Xu F. An Overview of the Free Energy Principle and Related Research. Neural Comput 2024; 36:963-1021. [PMID: 38457757 DOI: 10.1162/neco_a_01642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/20/2023] [Indexed: 03/10/2024]
Abstract
The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making-within an agent-are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.
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Affiliation(s)
- Zhengquan Zhang
- Key Laboratory of Information Science of Electromagnetic Waves, Fudan University, Shanghai, P.R.C.
| | - Feng Xu
- Key Laboratory of Information Science of Electromagnetic Waves, Fudan University, Shanghai, P.R.C.
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3
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Priorelli M, Pezzulo G, Stoianov IP. Deep kinematic inference affords efficient and scalable control of bodily movements. Proc Natl Acad Sci U S A 2023; 120:e2309058120. [PMID: 38085784 PMCID: PMC10743426 DOI: 10.1073/pnas.2309058120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/24/2023] [Indexed: 12/18/2023] Open
Abstract
Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.
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Affiliation(s)
- Matteo Priorelli
- National Research Council, Institute of Cognitive Sciences and Technologies, Padova35137, Italy
| | - Giovanni Pezzulo
- National Research Council, Institute of Cognitive Sciences and Technologies, Rome00185, Italy
| | - Ivilin Peev Stoianov
- National Research Council, Institute of Cognitive Sciences and Technologies, Padova35137, Italy
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4
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Matsumoto T, Ohata W, Tani J. Incremental Learning of Goal-Directed Actions in a Dynamic Environment by a Robot Using Active Inference. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1506. [PMID: 37998198 PMCID: PMC10670890 DOI: 10.3390/e25111506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/27/2023] [Indexed: 11/25/2023]
Abstract
This study investigated how a physical robot can adapt goal-directed actions in dynamically changing environments, in real-time, using an active inference-based approach with incremental learning from human tutoring examples. Using our active inference-based model, while good generalization can be achieved with appropriate parameters, when faced with sudden, large changes in the environment, a human may have to intervene to correct actions of the robot in order to reach the goal, as a caregiver might guide the hands of a child performing an unfamiliar task. In order for the robot to learn from the human tutor, we propose a new scheme to accomplish incremental learning from these proprioceptive-exteroceptive experiences combined with mental rehearsal of past experiences. Our experimental results demonstrate that using only a few tutoring examples, the robot using our model was able to significantly improve its performance on new tasks without catastrophic forgetting of previously learned tasks.
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Affiliation(s)
| | | | - Jun Tani
- Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan; (T.M.); (W.O.)
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5
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Kim CS. Free energy and inference in living systems. Interface Focus 2023; 13:20220041. [PMID: 37065269 PMCID: PMC10102732 DOI: 10.1098/rsfs.2022.0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 01/18/2023] [Indexed: 04/18/2023] Open
Abstract
Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.
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Affiliation(s)
- Chang Sub Kim
- Department of Physics, Chonnam National University, Gwangju 61186, Republic of Korea
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6
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Friston K. Really radical? Behav Brain Sci 2023; 46:e93. [PMID: 37154143 DOI: 10.1017/s0140525x2200276x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
I enjoyed reading this compelling account of Conviction Narrative Theory (CNT). As a theoretical neurobiologist, I recognised - and applauded - the tenets of CNT. My commentary asks whether its claims could be installed into a Bayesian mechanics of decision-making, in a way that would enable theoreticians to model, reproduce and predict decision-making.
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Affiliation(s)
- Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, UK. ://www.fil.ion.ucl.ac.uk/~karl/
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7
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Konaka Y, Naoki H. Decoding reward-curiosity conflict in decision-making from irrational behaviors. NATURE COMPUTATIONAL SCIENCE 2023; 3:418-432. [PMID: 38177842 PMCID: PMC10768639 DOI: 10.1038/s43588-023-00439-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 03/29/2023] [Indexed: 01/06/2024]
Abstract
Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model describes irrational behaviors depending on the curiosity level. We also proposed a machine learning method to decode temporal curiosity from behavioral data. By applying it to rat behavioral data, we found that the rat had negative curiosity, reflecting conservative selection sticking to more certain options and that the level of curiosity was upregulated by the expected future information obtained from an uncertain environment. Our decoding approach can be a fundamental tool for identifying the neural basis for reward-curiosity conflicts. Furthermore, it could be effective in diagnosing mental disorders.
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Affiliation(s)
- Yuki Konaka
- Laboratory of Data-Driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Hiroshima, Japan
| | - Honda Naoki
- Laboratory of Data-Driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Hiroshima, Japan.
- Kansei-Brain Informatics Group, Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan.
- Theoretical Biology Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan.
- Laboratory of Theoretical Biology, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.
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Farnsworth KD, Elwood RW. Why it hurts: with freedom comes the biological need for pain. Anim Cogn 2023:10.1007/s10071-023-01773-2. [PMID: 37029847 DOI: 10.1007/s10071-023-01773-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 04/09/2023]
Abstract
We argue that pain is not needed to protect the body from damage unless the organism is able to make free choices in action selection. Then pain (including its affective and evaluative aspects) provides a necessary prioritising motivation to select actions expected to avoid it, whilst leaving the possibility of alternative actions to serve potentially higher priorities. Thus, on adaptive grounds, only organisms having free choice over action selection should experience pain. Free choice implies actions must be selected following appraisal of their effects, requiring a predictive model generating estimates of action outcomes. These features give organisms anticipatory behavioural autonomy (ABA), for which we propose a plausible system using an internal predictive model, integrated into a system able to produce the qualitative and affective aspects of pain. Our hypothesis can be tested using behavioural experiments designed to elicit trade-off responses to novel experiences for which algorithmic (automaton) responses might be inappropriate. We discuss the empirical evidence for our hypothesis among taxonomic groups, showing how testing for ABA guides thinking on which groups might experience pain. It is likely that all vertebrates do and plausible that some invertebrates do (decapods, cephalopods and at least some insects).
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Affiliation(s)
- Keith D Farnsworth
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT95DL, UK.
| | - Robert W Elwood
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT95DL, UK
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Floegel M, Kasper J, Perrier P, Kell CA. How the conception of control influences our understanding of actions. Nat Rev Neurosci 2023; 24:313-329. [PMID: 36997716 DOI: 10.1038/s41583-023-00691-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/28/2023] [Indexed: 04/01/2023]
Abstract
Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.
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Affiliation(s)
- Mareike Floegel
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Johannes Kasper
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Pascal Perrier
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, Grenoble, France
| | - Christian A Kell
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany.
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10
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Soda T, Ahmadi A, Tani J, Honda M, Hanakawa T, Yamashita Y. Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy. Front Psychiatry 2023; 14:1080668. [PMID: 37009124 PMCID: PMC10050443 DOI: 10.3389/fpsyt.2023.1080668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.
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Affiliation(s)
- Takafumi Soda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of NCNP Brain Physiology and Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Jun Tani
- Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Manabu Honda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takashi Hanakawa
- Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
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11
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Friston K. The ultimate trick?: Comment on: "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja et al. Phys Life Rev 2022; 43:10-16. [PMID: 35963034 DOI: 10.1016/j.plrev.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 07/28/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK.
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12
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Goal-Directed Planning and Goal Understanding by Extended Active Inference: Evaluation through Simulated and Physical Robot Experiments. ENTROPY 2022; 24:e24040469. [PMID: 35455132 PMCID: PMC9026632 DOI: 10.3390/e24040469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 02/04/2023]
Abstract
We show that goal-directed action planning and generation in a teleological framework can be formulated by extending the active inference framework. The proposed model, which is built on a variational recurrent neural network model, is characterized by three essential features. These are that (1) goals can be specified for both static sensory states, e.g., for goal images to be reached and dynamic processes, e.g., for moving around an object, (2) the model cannot only generate goal-directed action plans, but can also understand goals through sensory observation, and (3) the model generates future action plans for given goals based on the best estimate of the current state, inferred from past sensory observations. The proposed model is evaluated by conducting experiments on a simulated mobile agent as well as on a real humanoid robot performing object manipulation.
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Parr T, Limanowski J, Rawji V, Friston K. The computational neurology of movement under active inference. Brain 2021; 144:1799-1818. [PMID: 33704439 PMCID: PMC8320263 DOI: 10.1093/brain/awab085] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 11/08/2020] [Accepted: 12/20/2020] [Indexed: 12/31/2022] Open
Abstract
We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Jakub Limanowski
- Faculty of Psychology and Center for Tactile Internet with Human-in-the-Loop, Technische Universität Dresden, Dresden, Germany
| | - Vishal Rawji
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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14
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Ciria A, Schillaci G, Pezzulo G, Hafner VV, Lara B. Predictive Processing in Cognitive Robotics: A Review. Neural Comput 2021; 33:1402-1432. [PMID: 34496394 DOI: 10.1162/neco_a_01383] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/31/2020] [Indexed: 11/04/2022]
Abstract
Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.
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Affiliation(s)
- Alejandra Ciria
- Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, CP 04510, Mexico
| | - Guido Schillaci
- BioRobotics Institute, Scuola Superiore Sant'Anna, 34 56025 Pontedera, Italy
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, 44 00185 Rome, Italy
| | - Verena V Hafner
- Adaptive Systems Group, Department of Computer Science, Humboldt-Universität zu Berlin, D-12489, Germany
| | - Bruno Lara
- Laboratorio de Robótica Cognitiva, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca CP 62209, Mexico
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Da Costa L, Parr T, Sengupta B, Friston K. Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing. ENTROPY (BASEL, SWITZERLAND) 2021; 23:454. [PMID: 33921298 PMCID: PMC8069154 DOI: 10.3390/e23040454] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/06/2021] [Indexed: 02/07/2023]
Abstract
Active inference is a normative framework for explaining behaviour under the free energy principle-a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy-a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error-plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Biswa Sengupta
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
- Core Machine Learning Group, Zebra AI, London WC2H 8TJ, UK
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
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Van de Maele T, Verbelen T, Çatal O, De Boom C, Dhoedt B. Active Vision for Robot Manipulators Using the Free Energy Principle. Front Neurorobot 2021; 15:642780. [PMID: 33746730 PMCID: PMC7973267 DOI: 10.3389/fnbot.2021.642780] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 02/03/2021] [Indexed: 11/14/2022] Open
Abstract
Occlusions, restricted field of view and limited resolution all constrain a robot's ability to sense its environment from a single observation. In these cases, the robot first needs to actively query multiple observations and accumulate information before it can complete a task. In this paper, we cast this problem of active vision as active inference, which states that an intelligent agent maintains a generative model of its environment and acts in order to minimize its surprise, or expected free energy according to this model. We apply this to an object-reaching task for a 7-DOF robotic manipulator with an in-hand camera to scan the workspace. A novel generative model using deep neural networks is proposed that is able to fuse multiple views into an abstract representation and is trained from data by minimizing variational free energy. We validate our approach experimentally for a reaching task in simulation in which a robotic agent starts without any knowledge about its workspace. Each step, the next view pose is chosen by evaluating the expected free energy. We find that by minimizing the expected free energy, exploratory behavior emerges when the target object to reach is not in view, and the end effector is moved to the correct reach position once the target is located. Similar to an owl scavenging for prey, the robot naturally prefers higher ground for exploring, approaching its target once located.
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Affiliation(s)
- Toon Van de Maele
- IDLab, Department of Information Technology, Ghent University—imec, Ghent, Belgium
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