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Fields C. The free energy principle induces intracellular compartmentalization. Biochem Biophys Res Commun 2024; 723:150070. [PMID: 38896995 DOI: 10.1016/j.bbrc.2024.150070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/24/2024] [Accepted: 05/07/2024] [Indexed: 06/21/2024]
Abstract
Living systems at all scales are compartmentalized into interacting subsystems. This paper reviews a mechanism that drives compartmentalization in generic systems at any scale. It first discusses three symmetries of generic physical interactions in a quantum-theoretic description. It then shows that if one of these, a permutation symmetry on the inter-system boundary, is spontaneously broken, the symmetry breaking is amplified by the Free Energy Principle (FEP). It thus shows how compartmentalization generically results from permutation symmetry breaking under the FEP. It finally notes that the FEP asymptotically restores the broken symmetry, showing that the FEP can be regarded as a theory of fluctuations away from a permutation-symmetric boundary, and hence from an entangled joint state of the interacting systems.
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Affiliation(s)
- Chris Fields
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA.
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2
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Nadinda PG, van Laarhoven AIM, Van den Bergh O, Vlaeyen JWS, Peters ML, Evers AWM. Expectancies and avoidance: Towards an integrated model of chronic somatic symptoms. Neurosci Biobehav Rev 2024; 164:105808. [PMID: 38986893 DOI: 10.1016/j.neubiorev.2024.105808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/23/2024] [Accepted: 07/07/2024] [Indexed: 07/12/2024]
Affiliation(s)
- Putu Gita Nadinda
- Leiden University, the Netherlands; Maastricht University, the Netherlands.
| | | | | | - Johan W S Vlaeyen
- Maastricht University, the Netherlands; Katholieke Universiteit Leuven, Belgium
| | | | - Andrea W M Evers
- Leiden University, the Netherlands; Medical Delta, Leiden University, Technical University Delft, and Erasmus University Rotterdam, the Netherlands
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3
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Yoshida N, Daikoku T, Nagai Y, Kuniyoshi Y. Emergence of integrated behaviors through direct optimization for homeostasis. Neural Netw 2024; 177:106379. [PMID: 38762941 DOI: 10.1016/j.neunet.2024.106379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 05/21/2024]
Abstract
Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.
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Affiliation(s)
- Naoto Yoshida
- Graduate School of Information Science and Technology, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; International Research Center for Neurointelligence (WPI-IRCN), Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Tatsuya Daikoku
- International Research Center for Neurointelligence (WPI-IRCN), Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Yukie Nagai
- International Research Center for Neurointelligence (WPI-IRCN), Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Institute for AI and Beyond, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yasuo Kuniyoshi
- Graduate School of Information Science and Technology, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
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4
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Immink MA, Corcoran AW. Action imagery as active inference: a commentary on Rieger et al. (2023). PSYCHOLOGICAL RESEARCH 2024; 88:1859-1861. [PMID: 38079006 DOI: 10.1007/s00426-023-01900-1] [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: 07/04/2023] [Accepted: 11/20/2023] [Indexed: 07/17/2024]
Abstract
Rieger et al. (Psychol Res 2023:1-10, 2023) describe action imagery as motor simulation. Inverse models encode predicted action effects and compute muscle commands, which are inhibited to prevent overt action. We welcome this conceptualization of action imagery as inherently generative and predictive. In the spirit of stimulating further theoretical discourse on action imagery, and more broadly, action control, our commentary provides a brief introduction to Active Inference to establish a contrasting perspective from which to consider what, how, and why action imagery contributes to motor learning.
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Affiliation(s)
| | - Andrew W Corcoran
- Monash Centre for Consciousness and Contemplative Studies, Faculty of Arts, Monash University, Melbourne, Australia
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5
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Bosulu J, Pezzulo G, Hétu S. Needing: An Active Inference Process for Physiological Motivation. J Cogn Neurosci 2024; 36:2011-2028. [PMID: 38940737 DOI: 10.1162/jocn_a_02209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Need states are internal states that arise from deprivation of crucial biological stimuli. They direct motivation, independently of external learning. Despite their separate origin, they interact with reward processing systems that respond to external stimuli. This article aims to illuminate the functioning of the needing system through the lens of active inference, a framework for understanding brain and cognition. We propose that need states exert a pervasive influence on the organism, which in active inference terms translates to a "pervasive surprise"-a measure of the distance from the organism's preferred state. Crucially, we define needing as an active inference process that seeks to reduce this pervasive surprise. Through a series of simulations, we demonstrate that our proposal successfully captures key aspects of the phenomenology and neurobiology of needing. We show that as need states increase, the tendency to occupy preferred states strengthens, independently of external reward prediction. Furthermore, need states increase the precision of states (stimuli and actions) leading to preferred states, suggesting their ability to amplify the value of reward cues and rewards themselves. Collectively, our model and simulations provide valuable insights into the directional and underlying influence of need states, revealing how this influence amplifies the wanting or liking associated with relevant stimuli.
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Affiliation(s)
- Juvenal Bosulu
- Université de Montréal
- Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Montréal, Québec, Canada
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies (ISTC-CNR), Rome, Italy
| | - Sébastien Hétu
- Université de Montréal
- Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage (CIRCA), Montréal, Québec, Canada
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6
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Andersen MM, Kiverstein J. Play in Cognitive Development: From Rational Constructivism to Predictive Processing. Top Cogn Sci 2024. [PMID: 39190838 DOI: 10.1111/tops.12752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024]
Abstract
It is widely believed that play and curiosity are key ingredients as children develop models of the world. There is also an emerging consensus that children are Bayesian learners who combine their structured prior beliefs with estimations of the likelihood of new evidence to infer the most probable model of the world. An influential school of thought within developmental psychology, rational constructivism, combines these two ideas to propose that children learn intuitive theories of how the world works in part by engaging in play activities that allow them to gather new information for testing their theories. There are still, however, at least two pieces missing from rational constructivist theories of development. First, rational constructivism has so far devoted little attention to explaining why children's preferred form of learning, play, feels so fun, enjoyable, and rewarding. Rational constructivism may suggest that children are curious and like to play because reducing uncertainty and learning better theories of the causal workings of the world is enjoyable. What remains unclear, however, is why reducing uncertainty in play is interesting, fun, and joyful, while doing so in other forms of learning can be frustrating or boring. Second, rational constructivism may have overlooked how children, during play, will take control of and manipulate their environment, sometimes in an effort to create ideal niches for surprise-extraction, sometimes for developing strategies for making the world fit with their predictions. These missing elements from rational constructivism can be provided by understanding the contribution of play to development in terms of predictive processing, an influential framework in cognitive neuroscience that models many of the brain's cognitive functions as processes of model-based, probabilistic prediction.
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Affiliation(s)
- Marc M Andersen
- Department of Culture, Cognition and Computation, Aarhus University
- Interacting Minds Centre, Aarhus University
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7
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Costa C, Pezzetta R, Masina F, Lago S, Gastaldon S, Frangi C, Genon S, Arcara G, Scarpazza C. Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach. Hum Brain Mapp 2024; 45:e26817. [PMID: 39169641 PMCID: PMC11339134 DOI: 10.1002/hbm.26817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 07/15/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.
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Affiliation(s)
| | | | | | - Sara Lago
- Padova Neuroscience CenterPaduaItaly
- IRCCS Ospedale San CamilloVeniceItaly
| | - Simone Gastaldon
- Padova Neuroscience CenterPaduaItaly
- Dipartimento di Psicologia dello Sviluppo e della SocializzazioneUniversità degli Studi di PadovaPaduaItaly
| | - Camilla Frangi
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
| | - Sarah Genon
- Institute for Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | | | - Cristina Scarpazza
- IRCCS Ospedale San CamilloVeniceItaly
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
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Batzikosta A, Moraitou D, Steiropoulos P, Papantoniou G, Kougioumtzis GA, Katsouri IG, Sofologi M, Tsolaki M. The Relationships of Specific Cognitive Control Abilities with Objective and Subjective Sleep Parameters in Mild Cognitive Impairment: Revealing the Association between Cognitive Planning and Sleep Duration. Brain Sci 2024; 14:813. [PMID: 39199504 PMCID: PMC11352822 DOI: 10.3390/brainsci14080813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/01/2024] Open
Abstract
This study aimed to examine the associations between specific sleep parameters and specific aspects of cognitive functioning in individuals diagnosed with mild cognitive impairment (MCI), compared with healthy controls (HCs) by using cognitive, subjective, and objective sleep measures. A total of 179 participants were enrolled, all aged ≥ 65 years (mean age = 70.23; SD = 4.74) and with a minimum of six years of education (mean = 12.35; SD = 3.22). The sample included 46 HCs (36 females), 75 individuals with amnestic MCI (aMCI) (51 females), and 58 individuals with non-amnestic MCI (naMCI) (39 females). Inhibition, cognitive flexibility as a combined application of inhibitory control and set shifting or task/rule switching, and planning were examined. The following D-KEFS subtests were administered for their evaluation: Verbal Fluency Test, Color-Word Interference Test, and Tower Test. Self-reported sleep questionnaires (Athens Insomnia Scale, Stop-Bang questionnaire, and Pittsburg Sleep Quality Index) were used for subjective sleep assessments. Actigraphy was used for objective sleep measurements. Mixed-measures ANOVA, MANOVA, and one-way ANOVA, as well as the Scheffe post hoc test, were applied to the data. The results showed that the three groups exhibited statistically significant differences in the Tower Test (total achievement score, total number of administered problems, and total rule violations). As regards objective sleep measurements, the total sleep time (TST) was measured using actigraphy, and indicated that there are significant differences, with the HC group having a significantly higher mean TST compared to the naMCI group. The relationships evaluated in the TST Tower Test were found to be statistically significant. The findings are discussed in the context of potential parameters that can support the connection between sleep duration, measured as TST, and cognitive planning, as measured using the Tower Test.
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Affiliation(s)
- Areti Batzikosta
- Laboratory of Psychology, Department of Cognition, Brain and Behavior, School of Psychology, Faculty of Philosophy, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece;
- Laboratory of Neurodegenerative Diseases, Center of Interdisciplinary Research and Innovation (CIRI-AUTH), Balcan Center, Buildings A & B, 57001 Thessaloniki, Greece; (G.P.); (M.T.)
| | - Despina Moraitou
- Laboratory of Psychology, Department of Cognition, Brain and Behavior, School of Psychology, Faculty of Philosophy, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece;
- Laboratory of Neurodegenerative Diseases, Center of Interdisciplinary Research and Innovation (CIRI-AUTH), Balcan Center, Buildings A & B, 57001 Thessaloniki, Greece; (G.P.); (M.T.)
| | - Paschalis Steiropoulos
- Department of Respiratory Medicine, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Georgia Papantoniou
- Laboratory of Neurodegenerative Diseases, Center of Interdisciplinary Research and Innovation (CIRI-AUTH), Balcan Center, Buildings A & B, 57001 Thessaloniki, Greece; (G.P.); (M.T.)
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece;
- Institute of Humanities and Social Sciences, University Research Centre of Ioannina (URCI), 45110 Ioannina, Greece
| | - Georgios A. Kougioumtzis
- Department of Turkish Studies and Modern Asian Studies, Faculty of Economic and Political Sciences, National and Kapodistrian University of Athens, 15772 Athens, Greece;
- Department of Psychology, School of Health Sciences, Neapolis University Pafos, Pafos 8042, Cyprus
| | - Ioanna-Giannoula Katsouri
- Department of Occupational Therapy, Faculty of Health and Caring Sciences, University of West Attica, 12243 Athens, Greece;
| | - Maria Sofologi
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece;
- Institute of Humanities and Social Sciences, University Research Centre of Ioannina (URCI), 45110 Ioannina, Greece
- Department of Psychology, School of Health Sciences, Neapolis University Pafos, Pafos 8042, Cyprus
| | - Magda Tsolaki
- Laboratory of Neurodegenerative Diseases, Center of Interdisciplinary Research and Innovation (CIRI-AUTH), Balcan Center, Buildings A & B, 57001 Thessaloniki, Greece; (G.P.); (M.T.)
- Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD), Petrou Sindika 13 Str., 54643 Thessaloniki, Greece
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9
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Kuhn RL. A landscape of consciousness: Toward a taxonomy of explanations and implications. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 190:28-169. [PMID: 38281544 DOI: 10.1016/j.pbiomolbio.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/12/2023] [Accepted: 12/25/2023] [Indexed: 01/30/2024]
Abstract
Diverse explanations or theories of consciousness are arrayed on a roughly physicalist-to-nonphysicalist landscape of essences and mechanisms. Categories: Materialism Theories (philosophical, neurobiological, electromagnetic field, computational and informational, homeostatic and affective, embodied and enactive, relational, representational, language, phylogenetic evolution); Non-Reductive Physicalism; Quantum Theories; Integrated Information Theory; Panpsychisms; Monisms; Dualisms; Idealisms; Anomalous and Altered States Theories; Challenge Theories. There are many subcategories, especially for Materialism Theories. Each explanation is self-described by its adherents, critique is minimal and only for clarification, and there is no attempt to adjudicate among theories. The implications of consciousness explanations or theories are assessed with respect to four questions: meaning/purpose/value (if any); AI consciousness; virtual immortality; and survival beyond death. A Landscape of Consciousness, I suggest, offers perspective.
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Parvizi-Wayne D, Severs L. When the interoceptive and conceptual clash: The case of oppositional phenomenal self-modelling in Tourette syndrome. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024; 24:660-680. [PMID: 38777988 PMCID: PMC11233343 DOI: 10.3758/s13415-024-01189-6] [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] [Accepted: 04/15/2024] [Indexed: 05/25/2024]
Abstract
Tourette syndrome (TS) has been associated with a rich set of symptoms that are said to be uncomfortable, unwilled, and effortful to manage. Furthermore, tics, the canonical characteristic of TS, are multifaceted, and their onset and maintenance is complex. A formal account that integrates these features of TS symptomatology within a plausible theoretical framework is currently absent from the field. In this paper, we assess the explanatory power of hierarchical generative modelling in accounting for TS symptomatology from the perspective of active inference. We propose a fourfold analysis of sensory, motor, and cognitive phenomena associated with TS. In Section 1, we characterise tics as a form of action aimed at sensory attenuation. In Section 2, we introduce the notion of epistemic ticcing and describe such behaviour as the search for evidence that there is an agent (i.e., self) at the heart of the generative hierarchy. In Section 3, we characterise both epistemic (sensation-free) and nonepistemic (sensational) tics as habitual behaviour. Finally, in Section 4, we propose that ticcing behaviour involves an inevitable conflict between distinguishable aspects of selfhood; namely, between the minimal phenomenal sense of self-which is putatively underwritten by interoceptive inference-and the explicit preferences that constitute the individual's conceptual sense of self. In sum, we aim to provide an empirically informed analysis of TS symptomatology under active inference, revealing a continuity between covert and overt features of the condition.
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Affiliation(s)
- D Parvizi-Wayne
- Department of Psychology, Royal Holloway University of London, London, UK.
| | - L Severs
- Centre for the Philosophy of Science, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
- Ruhr-Universität Bochum, Institute of Philosophy II, Bochum, Germany
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11
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Nau M, Schmid AC, Kaplan SM, Baker CI, Kravitz DJ. Centering cognitive neuroscience on task demands and generalization. Nat Neurosci 2024:10.1038/s41593-024-01711-6. [PMID: 39075326 DOI: 10.1038/s41593-024-01711-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 06/17/2024] [Indexed: 07/31/2024]
Abstract
Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.
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Affiliation(s)
- Matthias Nau
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA.
| | - Alexandra C Schmid
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA
| | - Simon M Kaplan
- Department of Psychological & Brain Sciences, The George Washington University, Washington, DC, USA
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA.
| | - Dwight J Kravitz
- Department of Psychological & Brain Sciences, The George Washington University, Washington, DC, USA.
- Division of Behavioral and Cognitive Sciences, Directorate for Social, Behavioral, and Economic Sciences, US National Science Foundation, Arlington, VA, USA.
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12
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Fields C, Goldstein A, Sandved-Smith L. Making the Thermodynamic Cost of Active Inference Explicit. ENTROPY (BASEL, SWITZERLAND) 2024; 26:622. [PMID: 39202092 PMCID: PMC11353633 DOI: 10.3390/e26080622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 09/03/2024]
Abstract
When describing Active Inference Agents (AIAs), the term "energy" can have two distinct meanings. One is the energy that is utilized by the AIA (e.g., electrical energy or chemical energy). The second meaning is so-called Variational Free Energy (VFE), a statistical quantity which provides an upper bound on surprisal. In this paper, we develop an account of the former quantity-the Thermodynamic Free Energy (TFE)-and its relationship with the latter. We highlight the necessary tradeoffs between these two in a generic, quantum information-theoretic formulation, and the macroscopic consequences of those tradeoffs for the ways that organisms approach their environments. By making this tradeoff explicit, we provide a theoretical basis for the different metabolic strategies that organisms from plants to predators use to survive.
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Affiliation(s)
- Chris Fields
- Independent Researcher, 11160 Caunes Minervois, France
| | - Adam Goldstein
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK;
| | - Lars Sandved-Smith
- Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne 3168, Australia;
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13
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Carl M. An Active Inference Agent for Modeling Human Translation Processes. ENTROPY (BASEL, SWITZERLAND) 2024; 26:616. [PMID: 39202086 PMCID: PMC11353518 DOI: 10.3390/e26080616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 07/12/2024] [Accepted: 07/15/2024] [Indexed: 09/03/2024]
Abstract
This paper develops an outline for a hierarchically embedded architecture of an artificial agent that models human translation processes based on principles of active inference (AIF) and predictive processing (PP). AIF and PP posit that the mind constructs a model of the environment which guides behavior by continually generating and integrating predictions and sensory input. The proposed model of the translation agent consists of three processing strata: a sensorimotor layer, a cognitive layer, and a phenomenal layer. Each layer consists of a network of states and transitions that interact on different time scales. Following the AIF framework, states are conditioned on observations which may originate from the environment and/or the embedded processing layer, while transitions between states are conditioned on actions that implement plans to optimize goal-oriented behavior. The AIF agent aims at simulating the variation in translational behavior under various conditions and to facilitate investigating the underlying mental mechanisms. It provides a novel framework for generating and testing new hypotheses of the translating mind.
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Affiliation(s)
- Michael Carl
- Department of Modern and Classical Language Studies, Kent State University, Kent, OH 44240, USA
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14
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Schmerwitz C, Kopp B. The future of neuropsychology is digital, theory-driven, and Bayesian: a paradigmatic study of cognitive flexibility. Front Psychol 2024; 15:1437192. [PMID: 39070581 PMCID: PMC11276732 DOI: 10.3389/fpsyg.2024.1437192] [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: 05/23/2024] [Accepted: 06/24/2024] [Indexed: 07/30/2024] Open
Abstract
Introduction This study explores the transformative potential of digital, theory-driven, and Bayesian paradigms in neuropsychology by combining digital technologies, a commitment to evaluating theoretical frameworks, and Bayesian statistics. The study also examines theories of executive function and cognitive flexibility in a large sample of neurotypical individuals (N = 489). Methods We developed an internet-based Wisconsin Card-Sorting Task (iWCST) optimized for online assessment of perseveration errors (PE). Predictions of the percentage of PE, PE (%), in non-repetitive versus repetitive situations were derived from the established supervisory attention system (SAS) theory, non-repetitive PE (%) < repetitive PE (%), and the novel goal-directed instrumental control (GIC) theory, non-repetitive PE (%) > repetitive PE (%). Results Bayesian t-tests revealed the presence of a robust error suppression effect (ESE) indicating that PE are less likely in repetitive situations than in non-repetitive situations, contradicting SAS theory with posterior model probability p < 0.001 and confirming GIC theory with posterior model probability p > 0.999. We conclude that repetitive situations support cognitive set switching in the iWCST by facilitating the retrieval of goal-directed, instrumental memory that associates stimulus features, actions, and outcomes, thereby generating the ESE in neurotypical individuals. We also report exploratory data analyses, including a Bayesian network analysis of relationships between iWCST measures. Discussion Overall, this study serves as a paradigmatic model for combining digital technologies, theory-driven research, and Bayesian statistics in neuropsychology. It also provides insight into how this integrative, innovative approach can advance the understanding of executive function and cognitive flexibility and inform future research and clinical applications.
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Affiliation(s)
| | - Bruno Kopp
- Cognitive Neuropsychology, Department of Neurology, Hannover Medical School, Hannover, Germany
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15
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Hamburg S, Jimenez Rodriguez A, Htet A, Di Nuovo A. Active Inference for Learning and Development in Embodied Neuromorphic Agents. ENTROPY (BASEL, SWITZERLAND) 2024; 26:582. [PMID: 39056944 PMCID: PMC11276484 DOI: 10.3390/e26070582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/23/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
Taking inspiration from humans can help catalyse embodied AI solutions for important real-world applications. Current human-inspired tools include neuromorphic systems and the developmental approach to learning. However, this developmental neurorobotics approach is currently lacking important frameworks for human-like computation and learning. We propose that human-like computation is inherently embodied, with its interface to the world being neuromorphic, and its learning processes operating across different timescales. These constraints necessitate a unified framework: active inference, underpinned by the free energy principle (FEP). Herein, we describe theoretical and empirical support for leveraging this framework in embodied neuromorphic agents with autonomous mental development. We additionally outline current implementation approaches (including toolboxes) and challenges, and we provide suggestions for next steps to catalyse this important field.
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Affiliation(s)
- Sarah Hamburg
- Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK; (A.J.R.); (A.H.); (A.D.N.)
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16
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Manrique HM, Friston KJ, Walker MJ. 'Snakes and ladders' in paleoanthropology: From cognitive surprise to skillfulness a million years ago. Phys Life Rev 2024; 49:40-70. [PMID: 38513522 DOI: 10.1016/j.plrev.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 03/23/2024]
Abstract
A paradigmatic account may suffice to explain behavioral evolution in early Homo. We propose a parsimonious account that (1) could explain a particular, frequently-encountered, archeological outcome of behavior in early Homo - namely, the fashioning of a Paleolithic stone 'handaxe' - from a biological theoretic perspective informed by the free energy principle (FEP); and that (2) regards instances of the outcome as postdictive or retrodictive, circumstantial corroboration. Our proposal considers humankind evolving as a self-organizing biological ecosystem at a geological time-scale. We offer a narrative treatment of this self-organization in terms of the FEP. Specifically, we indicate how 'cognitive surprises' could underwrite an evolving propensity in early Homo to express sporadic unorthodox or anomalous behavior. This co-evolutionary propensity has left us a legacy of Paleolithic artifacts that is reminiscent of a 'snakes and ladders' board game of appearances, disappearances, and reappearances of particular archeological traces of Paleolithic behavior. When detected in the Early and Middle Pleistocene record, anthropologists and archeologists often imagine evidence of unusual or novel behavior in terms of early humankind ascending the rungs of a figurative phylogenetic 'ladder' - as if these corresponded to progressive evolution of cognitive abilities that enabled incremental achievements of increasingly innovative technical prowess, culminating in the cognitive ascendancy of Homo sapiens. The conjecture overlooks a plausible likelihood that behavior by an individual who was atypical among her conspecifics could have been disregarded in a community of Hominina (for definition see Appendix 1) that failed to recognize, imagine, or articulate potential advantages of adopting hitherto unorthodox behavior. Such failure, as well as diverse fortuitous demographic accidents, would cause exceptional personal behavior to be ignored and hence unremembered. It could disappear by a pitfall, down a 'snake', as it were, in the figurative evolutionary board game; thereby causing a discontinuity in the evolution of human behavior that presents like an evolutionary puzzle. The puzzle discomforts some paleoanthropologists trained in the natural and life sciences. They often dismiss it, explaining it away with such self-justifying conjectures as that, maybe, separate paleospecies of Homo differentially possessed different cognitive abilities, which, supposedly, could account for the presence or absence in the Pleistocene archeological record of traces of this or that behavioral outcome or skill. We argue that an alternative perspective - that inherits from the FEP and an individual's 'active inference' about its surroundings and of its own responses - affords a prosaic, deflationary, and parsimonious way to account for appearances, disappearances, and reappearances of particular behavioral outcomes and skills of early humankind.
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Affiliation(s)
- Héctor Marín Manrique
- Department of Psychology and Sociology, Universidad de Zaragoza, Ciudad Escolar, s/n, Teruel 44003, Spain
| | - Karl John Friston
- Imaging Neuroscience, Institute of Neurology, and The Wellcome Centre for Human Imaging, University College London, London WC1N 3AR, UK
| | - Michael John Walker
- Physical Anthropology, Departamento de Zoología y Antropología Física, Facultad de Biología, Universidad de Murcia, Campus Universitario de Espinardo Edificio 20, Murcia 30100, Spain.
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17
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Schwöbel S, Marković D, Smolka MN, Kiebel S. Joint modeling of choices and reaction times based on Bayesian contextual behavioral control. PLoS Comput Biol 2024; 20:e1012228. [PMID: 38968304 PMCID: PMC11290629 DOI: 10.1371/journal.pcbi.1012228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 07/31/2024] [Accepted: 06/04/2024] [Indexed: 07/07/2024] Open
Abstract
In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms.
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Affiliation(s)
- Sarah Schwöbel
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Dimitrije Marković
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Stefan Kiebel
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
- Centre for Tactile Internet with Human-in-the-Loop (CeTI), Technische Universität Dresden, Dresden, Germany
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18
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Feldman MJ, Bliss-Moreau E, Lindquist KA. The neurobiology of interoception and affect. Trends Cogn Sci 2024; 28:643-661. [PMID: 38395706 PMCID: PMC11222051 DOI: 10.1016/j.tics.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024]
Abstract
Scholars have argued for centuries that affective states involve interoception, or representations of the state of the body. Yet, we lack a mechanistic understanding of how signals from the body are transduced, transmitted, compressed, and integrated by the brains of humans to produce affective states. We suggest that to understand how the body contributes to affect, we first need to understand information flow through the nervous system's interoceptive pathways. We outline such a model and discuss how unique anatomical and physiological aspects of interoceptive pathways may give rise to the qualities of affective experiences in general and valence and arousal in particular. We conclude by considering implications and future directions for research on interoception, affect, emotions, and human mental experiences.
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Affiliation(s)
- M J Feldman
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - E Bliss-Moreau
- Department of Psychology, University of California Davis, Davis, CA, USA; California National Primate Research Center, University of California Davis, Davis, CA, USA
| | - K A Lindquist
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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19
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Varangot-Reille C, Pezzulo G, Thacker M. The fear-avoidance model as an embodied prediction of threat. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024:10.3758/s13415-024-01199-4. [PMID: 38890209 DOI: 10.3758/s13415-024-01199-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/24/2024] [Indexed: 06/20/2024]
Abstract
The fear-avoidance model is a well-established framework in the understanding of persistent pain. It proposes a dichotomous path: either the context is interpreted as safe; there is no fear reaction and, therefore, the individual engages in active (positive) coping; or the context is interpreted as threatening, leading to a self-reinforcing vicious circle of fear and (negative) avoidance. We propose an embodied interpretation of this phenomenon employing the joint framework of predictive coding and active inference. The key idea is that multisensory integration of exteroceptive, proprioceptive, and interoceptive sensory inputs can lead to dysfunctional experiences of threat in nonthreatening situations. Threat inference can promote fear responses, maladaptive strategies (i.e., avoidance) and self-provides evidence for threat in associated or future contexts, or both. Under this treatment, the prediction of nonrealized threat becomes self-evidencing and context-invariant, and hence self-perpetuating. Safety cues are unable to attenuate the interpretation of the negative context as the dominant inference of the context is threatful and gains more precision and becomes resistant over time. Our model provides an explanation for the emergence of a dysfunctional fear response in the clinical setting despite apparent safety based on modern concepts from theoretical (computational) neuroscience.
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Affiliation(s)
- Clovis Varangot-Reille
- MSc Statistics and Computer Science for Data Science, University Lumière Lyon 2, Bron, France.
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Mick Thacker
- Department of Physiotherapy, Royal College of Surgeons in Ireland, Dublin, Ireland
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20
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Sharafeldin A, Imam N, Choi H. Active sensing with predictive coding and uncertainty minimization. PATTERNS (NEW YORK, N.Y.) 2024; 5:100983. [PMID: 39005491 PMCID: PMC11240181 DOI: 10.1016/j.patter.2024.100983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/11/2024] [Accepted: 04/08/2024] [Indexed: 07/16/2024]
Abstract
We present an end-to-end architecture for embodied exploration inspired by two biological computations: predictive coding and uncertainty minimization. The architecture can be applied to any exploration setting in a task-independent and intrinsically driven manner. We first demonstrate our approach in a maze navigation task and show that it can discover the underlying transition distributions and spatial features of the environment. Second, we apply our model to a more complex active vision task, whereby an agent actively samples its visual environment to gather information. We show that our model builds unsupervised representations through exploration that allow it to efficiently categorize visual scenes. We further show that using these representations for downstream classification leads to superior data efficiency and learning speed compared to other baselines while maintaining lower parameter complexity. Finally, the modular structure of our model facilitates interpretability, allowing us to probe its internal mechanisms and representations during exploration.
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Affiliation(s)
- Abdelrahman Sharafeldin
- ML@GT, Georgia Institute of Technology, Atlanta, GA 30332, USA
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nabil Imam
- ML@GT, Georgia Institute of Technology, Atlanta, GA 30332, USA
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hannah Choi
- ML@GT, Georgia Institute of Technology, Atlanta, GA 30332, USA
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA
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21
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Ficca G, De Rosa O, Giangrande D, Mazzei T, Marzolo S, Albinni B, Coppola A, Lustro A, Conte F. Quantitative-Qualitative Assessment of Dream Reports in Schizophrenia and Their Correlations with Illness Severity. Brain Sci 2024; 14:568. [PMID: 38928568 PMCID: PMC11201908 DOI: 10.3390/brainsci14060568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Positive symptoms of schizophrenia have been proposed to be an intrusion of dreaming in wakefulness; conversely, psychotic patients' abnormal cognitive and behavioral features could overflow into sleep, so that their dreams would differ from those of healthy people. Here we assess this hypothesis by comparing dream features of 46 patients affected by schizophrenic spectrum disorders to those of 28 healthy controls. In patients, we also investigated correlations of dream variables with symptom severity and verbal fluency. Overall, patients reported fewer and shorter dreams, with a general impoverishment of content (including characters, settings, interactions) and higher spatiotemporal bizarreness. The number of emotions, mainly negative ones, was lower in patients' reports and correlated inversely with symptom severity. Verbal fluency correlated positively with dream report length and negatively with perceptive bizarreness. In conclusion, our data show a significant impoverishment of dream reports in psychotic patients versus controls. Future research should investigate to what extent this profile of results depends on impaired verbal fluency or on impaired mechanisms of dream generation in this population. Moreover, in line with theories on the role of dreaming in emotion regulation, our data suggest that this function could be impaired in psychoses and related to symptom severity.
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Affiliation(s)
- Gianluca Ficca
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
| | - Oreste De Rosa
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
- Dermatology Unit, Department of Mental and Physical Health and Preventive Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Davide Giangrande
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
| | - Tommaso Mazzei
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
- Residential Community for Therapy and Rehabilitation “Al di là dei sogni (Beyond Dreams)”, 81037 Sessa Aurunca, Italy;
| | - Salvatore Marzolo
- Residential Community for Therapy and Rehabilitation “Al di là dei sogni (Beyond Dreams)”, 81037 Sessa Aurunca, Italy;
- Mental Health Unit 15, Local Health Authority 1, 81016 Piedimonte Matese, Italy
| | - Benedetta Albinni
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
| | - Alessia Coppola
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
| | - Alessio Lustro
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
| | - Francesca Conte
- Department of Psychology, University of Campania “L. Vanvitelli”, 81100 Caserta, Italy; (G.F.); (O.D.R.); (D.G.); (T.M.); (B.A.); (A.C.); (A.L.)
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22
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Parvizi-Wayne D, Sandved-Smith L, Pitliya RJ, Limanowski J, Tufft MRA, Friston KJ. Forgetting ourselves in flow: an active inference account of flow states and how we experience ourselves within them. Front Psychol 2024; 15:1354719. [PMID: 38887627 PMCID: PMC11182004 DOI: 10.3389/fpsyg.2024.1354719] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/26/2024] [Indexed: 06/20/2024] Open
Abstract
Flow has been described as a state of optimal performance, experienced universally across a broad range of domains: from art to athletics, gaming to writing. However, its phenomenal characteristics can, at first glance, be puzzling. Firstly, individuals in flow supposedly report a loss of self-awareness, even though they perform in a manner which seems to evince their agency and skill. Secondly, flow states are felt to be effortless, despite the prerequisite complexity of the tasks that engender them. In this paper, we unpick these features of flow, as well as others, through the active inference framework, which posits that action and perception are forms of active Bayesian inference directed at sustained self-organisation; i.e., the minimisation of variational free energy. We propose that the phenomenology of flow is rooted in the deployment of high precision weight over (i) the expected sensory consequences of action and (ii) beliefs about how action will sequentially unfold. This computational mechanism thus draws the embodied cognitive system to minimise the ensuing (i.e., expected) free energy through the exploitation of the pragmatic affordances at hand. Furthermore, given the challenging dynamics the flow-inducing situation presents, attention must be wholly focussed on the unfolding task whilst counterfactual planning is restricted, leading to the attested loss of the sense of self-as-object. This involves the inhibition of both the sense of self as a temporally extended object and higher-order, meta-cognitive forms of self-conceptualisation. Nevertheless, we stress that self-awareness is not entirely lost in flow. Rather, it is pre-reflective and bodily. Our approach to bodily-action-centred phenomenology can be applied to similar facets of seemingly agentive experience beyond canonical flow states, providing insights into the mechanisms of so-called selfless experiences, embodied expertise and wellbeing.
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Affiliation(s)
- Darius Parvizi-Wayne
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Lars Sandved-Smith
- Monash Centre for Consciousness and Contemplative Studies, Monash University, Clayton, VIC, Australia
| | - Riddhi J. Pitliya
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- VERSES AI Research Lab, Los Angeles, CA, United States
| | - Jakub Limanowski
- Institute of Psychology, University of Greifswald, Greifswald, Germany
| | - Miles R. A. Tufft
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Karl J. Friston
- VERSES AI Research Lab, Los Angeles, CA, United States
- Queen Square Institute of Neurology, University College London, London, United Kingdom
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23
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Murphy E, Holmes E, Friston K. Natural language syntax complies with the free-energy principle. SYNTHESE 2024; 203:154. [PMID: 38706520 PMCID: PMC11068586 DOI: 10.1007/s11229-024-04566-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 03/15/2024] [Indexed: 05/07/2024]
Abstract
Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design-such as "minimal search" criteria from theoretical syntax-adhere to the FEP. This affords a greater degree of explanatory power to the FEP-with respect to higher language functions-and offers linguistics a grounding in first principles with respect to computability. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.
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Affiliation(s)
- Elliot Murphy
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030 USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX 77030 USA
| | - Emma Holmes
- Department of Speech Hearing and Phonetic Sciences, University College London, London, WC1N 1PF UK
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR UK
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24
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Antonioni A, Raho EM, Sensi M, Di Lorenzo F, Fadiga L, Koch G. A new perspective on positive symptoms: expression of damage or self-defence mechanism of the brain? Neurol Sci 2024; 45:2347-2351. [PMID: 38353846 PMCID: PMC11021333 DOI: 10.1007/s10072-024-07395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 04/17/2024]
Abstract
Usually, positive neurological symptoms are considered as the consequence of a mere, afinalistic and abnormal increase in function of specific brain areas. However, according to the Theory of Active Inference, which argues that action and perception constitute a loop that updates expectations according to a Bayesian model, the brain is rather an explorer that formulates hypotheses and tests them to assess the correspondence between internal models and reality. Moreover, the cerebral cortex is characterised by a continuous "conflict" between different brain areas, which constantly attempt to expand in order to acquire more of the limited available computational resources, by means of their dopamine-induced neuroplasticity. Thus, it has recently been suggested that dreams, during rapid eye movement sleep (REMS), protect visual brain areas (deprived of their stimuli during rest) from being conquered by other normally stimulated ones. It is therefore conceivable that positive symptoms also have a functional importance for the brain. We evaluate supporting literature data of a 'defensive' role of positive symptoms and the relevance of dopamine-induced neuroplasticity in the context of neurodegenerative and psychiatric diseases. Furthermore, the possible functional significance of idiopathic REMS-related behavioural disorder as well as phantom limb syndrome is examined. We suggest that positive neurological symptoms are not merely a passive expression of a damage, but active efforts, related to dopamine-induced plasticity, to maintain a correct relationship between the external world and its brain representation, thus preventing healthy cortical areas from ousting injured ones.
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Affiliation(s)
- Annibale Antonioni
- Doctoral Program in Translational Neurosciences and Neurotechnologies, Department of Neuroscience and Rehabilitation, University of Ferrara, Via Ludovico Ariosto 35, 44121, Ferrara, Italy.
| | - Emanuela Maria Raho
- Department of Neuroscience and Rehabilitation, University Unit of Neurology, University of Ferrara, 44121, Ferrara, Italy
| | - Mariachiara Sensi
- Unit of Neurology, Interdistrict Health Care Department of Neuroscience, S. Anna Ferrara University Hospital, 44124, Ferrara, Italy
| | - Francesco Di Lorenzo
- Non Invasive Brain Stimulation Unit, Istituto Di Ricovero E Cura a Carattere Scientifico Santa Lucia, 00179, Rome, Italy
| | - Luciano Fadiga
- Center for Translational Neurophysiology, Istituto Italiano Di Tecnologia, 44121, Ferrara, Italy
- Section of Physiology, Department of Neuroscience and Rehabilitation, University of Ferrara, 44121, Ferrara, Italy
| | - Giacomo Koch
- Non Invasive Brain Stimulation Unit, Istituto Di Ricovero E Cura a Carattere Scientifico Santa Lucia, 00179, Rome, Italy
- Center for Translational Neurophysiology, Istituto Italiano Di Tecnologia, 44121, Ferrara, Italy
- Section of Physiology, Department of Neuroscience and Rehabilitation, University of Ferrara, 44121, Ferrara, Italy
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25
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Matsumura T, Esaki K, Yang S, Yoshimura C, Mizuno H. Active Inference With Empathy Mechanism for Socially Behaved Artificial Agents in Diverse Situations. ARTIFICIAL LIFE 2024; 30:277-297. [PMID: 38018026 DOI: 10.1162/artl_a_00416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
This article proposes a method for an artificial agent to behave in a social manner. Although defining proper social behavior is difficult because it differs from situation to situation, the agent following the proposed method adaptively behaves appropriately in each situation by empathizing with the surrounding others. The proposed method is achieved by incorporating empathy into active inference. We evaluated the proposed method regarding control of autonomous mobile robots in diverse situations. From the evaluation results, an agent controlled by the proposed method could behave more adaptively socially than an agent controlled by the standard active inference in the diverse situations. In the case of two agents, the agent controlled with the proposed method behaved in a social way that reduced the other agent's travel distance by 13.7% and increased the margin between the agents by 25.8%, even though it increased the agent's travel distance by 8.2%. Also, the agent controlled with the proposed method behaved more socially when it was surrounded by altruistic others but less socially when it was surrounded by selfish others.
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26
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Aydin M, Lucia S, Casella A, Di Bello B, Di Russo F. Bayesian interpretation of the prefrontal P2 ERP component based on stimulus/response mapping uncertainty. Int J Psychophysiol 2024; 199:112337. [PMID: 38537889 DOI: 10.1016/j.ijpsycho.2024.112337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/22/2024] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
The brain can be seen as a predictive system continuously computing prior information to guess posterior probabilities minimizing sources of uncertainty. To test this Bayesian view of the brain, event-related potentials (ERP) methods have been used focusing on the well-known P3 component, traditionally associated with decision-making processes and sources of uncertainty regarding target probability. Another ERP component linked with decision-making is the prefrontal P2 (pP2) component, which has never been considered within the Bayesian framework. To test which source of uncertainty could be associated with the pP2, uncertainty induced by target probability and stimulus-response (S/R) mapping were modulated in three visuomotor tasks. Results showed that the pP2 had the largest amplitude in the task with the largest uncertainty regarding the S/R mapping and degraded as the S/R mapping became more predictable. The P3 was maximal in the tasks with larger uncertainty regarding the target probability. While we confirmed the P3 association with target probability, we extended our knowledge on the pP2 associating it with S/R mapping uncertainty. This component, which has been previously localized within the anterior insular cortex, may minimize S/R mapping uncertainty allowing response-related evidence accumulation and comparing current events with internal representations to extract action-related probabilities.
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Affiliation(s)
- Merve Aydin
- Dept. of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy.
| | - Stefania Lucia
- Dept. of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy
| | - Andrea Casella
- Dept. of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy
| | - BiancaMaria Di Bello
- Dept. of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy
| | - Francesco Di Russo
- Dept. of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy; Santa Lucia Foundation IRCCS, 00179 Rome, Italy
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Bortolotti A, Conti A, Romagnoli A, Sacco PL. Imagination vs. routines: festive time, weekly time, and the predictive brain. Front Hum Neurosci 2024; 18:1357354. [PMID: 38736532 PMCID: PMC11082368 DOI: 10.3389/fnhum.2024.1357354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 04/05/2024] [Indexed: 05/14/2024] Open
Abstract
This paper examines the relationship between societal structures shaped by traditions, norms, laws, and customs, and creative expressions in arts and media through the lens of the predictive coding framework in cognitive science. The article proposes that both dimensions of culture can be viewed as adaptations designed to enhance and train the brain's predictive abilities in the social domain. Traditions, norms, laws, and customs foster shared predictions and expectations among individuals, thereby reducing uncertainty in social environments. On the other hand, arts and media expose us to simulated experiences that explore alternative social realities, allowing the predictive machinery of the brain to hone its skills through exposure to a wider array of potentially relevant social circumstances and scenarios. We first review key principles of predictive coding and active inference, and then explore the rationale of cultural traditions and artistic culture in this perspective. Finally, we draw parallels between institutionalized normative habits that stabilize social worlds and creative and imaginative acts that temporarily subvert established conventions to inject variability.
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Affiliation(s)
- Alessandro Bortolotti
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Alice Conti
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio” of Chieti-Pescara, Chieti, Italy
| | | | - Pier Luigi Sacco
- Department of Neuroscience, Imaging, and Clinical Sciences, University “G. D'Annunzio” of Chieti-Pescara, Chieti, Italy
- metaLAB (at) Harvard, Cambridge, MA, United States
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Heins C, Millidge B, Da Costa L, Mann RP, Friston KJ, Couzin ID. Collective behavior from surprise minimization. Proc Natl Acad Sci U S A 2024; 121:e2320239121. [PMID: 38630721 PMCID: PMC11046639 DOI: 10.1073/pnas.2320239121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.
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Affiliation(s)
- Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, KonstanzD-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, KonstanzD-78457, Germany
- Department of Biology, University of Konstanz, KonstanzD-78457, Germany
- VERSES Research Lab, Los Angeles, CA90016
| | - Beren Millidge
- Medical Research Council Brain Networks Dynamics Unit, University of Oxford, OxfordOX1 3TH, United Kingdom
| | - Lancelot Da Costa
- VERSES Research Lab, Los Angeles, CA90016
- Department of Mathematics, Imperial College London, LondonSW7 2AZ, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
| | - Richard P. Mann
- Department of Statistics, School of Mathematics, University of Leeds, LeedsLS2 9JT, United Kingdom
| | - Karl J. Friston
- VERSES Research Lab, Los Angeles, CA90016
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
| | - Iain D. Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, KonstanzD-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, KonstanzD-78457, Germany
- Department of Biology, University of Konstanz, KonstanzD-78457, Germany
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29
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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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30
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Gupta RS, Simmons AN, Dugas NN, Stout DM, Harlé KM. Motivational context and neurocomputation of stop expectation moderate early attention responses supporting proactive inhibitory control. Front Hum Neurosci 2024; 18:1357868. [PMID: 38628969 PMCID: PMC11019005 DOI: 10.3389/fnhum.2024.1357868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Alterations in attention to cues signaling the need for inhibitory control play a significant role in a wide range of psychopathology. However, the degree to which motivational and attentional factors shape the neurocomputations of proactive inhibitory control remains poorly understood. The present study investigated how variation in monetary incentive valence and stake modulate the neurocomputational signatures of proactive inhibitory control. Adults (N = 46) completed a Stop-Signal Task (SST) with concurrent EEG recording under four conditions associated with stop performance feedback: low and high punishment (following unsuccessful stops) and low and high reward (following successful stops). A Bayesian learning model was used to infer individual's probabilistic expectations of the need to stop on each trial: P(stop). Linear mixed effects models were used to examine whether interactions between motivational valence, stake, and P(stop) parameters predicted P1 and N1 attention-related event-related potentials (ERPs) time-locked to the go-onset stimulus. We found that P1 amplitudes increased at higher levels of P(stop) in punished but not rewarded conditions, although P1 amplitude differences between punished and rewarded blocks were maximal on trials when the need to inhibit was least expected. N1 amplitudes were positively related to P(stop) in the high punishment condition (low N1 amplitude), but negatively related to P(stop) in the high reward condition (high N1 amplitude). Critically, high P(stop)-related N1 amplitude to the go-stimulus predicted behavioral stop success during the high reward block, providing evidence for the role of motivationally relevant context and inhibitory control expectations in modulating the proactive allocation of attentional resources that affect inhibitory control. These findings provide novel insights into the neurocomputational mechanisms underlying proactive inhibitory control under valence-dependent motivational contexts, setting the stage for developing motivation-based interventions that boost inhibitory control.
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Affiliation(s)
- Resh S. Gupta
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
| | - Alan N. Simmons
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Nathalie N. Dugas
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Daniel M. Stout
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Katia M. Harlé
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
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31
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Engström J, Liu SY, Dinparastdjadid A, Simoiu C. Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework. ACCIDENT; ANALYSIS AND PREVENTION 2024; 198:107460. [PMID: 38295653 DOI: 10.1016/j.aap.2024.107460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/07/2023] [Accepted: 01/03/2024] [Indexed: 02/20/2024]
Abstract
There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.
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Affiliation(s)
- Johan Engström
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA.
| | - Shu-Yuan Liu
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA
| | | | - Camelia Simoiu
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA
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32
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Albarracin M, Bouchard-Joly G, Sheikhbahaee Z, Miller M, Pitliya RJ, Poirier P. Feeling our place in the world: an active inference account of self-esteem. Neurosci Conscious 2024; 2024:niae007. [PMID: 38562605 PMCID: PMC10984624 DOI: 10.1093/nc/niae007] [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: 03/01/2023] [Revised: 12/21/2023] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Self-esteem, the evaluation of one's own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer or an inferential capacity to interpret one's standing within a social group. This approach allows us to explore the interaction between an individual's self-perception and the expectations of their social environment.When there is a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation with recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health.
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Affiliation(s)
- Mahault Albarracin
- Département d'Informatique, Université du Québec à Montréal, 405 Rue Sainte-Catherine Est, Montreal H2L 2C4, QC, Canada
| | - Gabriel Bouchard-Joly
- Département d'Informatique, Université du Québec à Montréal, 405 Rue Sainte-Catherine Est, Montreal H2L 2C4, QC, Canada
| | - Zahra Sheikhbahaee
- CHU Sainte-Justine Research Center, University of Montreal, 5757, Av. Decelles bureau 500, Montreal, QC H3S 2C3, Canada
| | - Mark Miller
- Monash Centre for Consciousness and Contemplative Studies, Monash University, Melboume, Australia
- Psychology Department, University of Toronto, Toronto, ON M5S 3G3, Canada
| | - Riddhi J Pitliya
- Department of Experimental Psychology Oxford University, Oxford OX1 2JD, United Kingdom
| | - Pierre Poirier
- Département d'Informatique, Université du Québec à Montréal, 405 Rue Sainte-Catherine Est, Montreal H2L 2C4, QC, Canada
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Pezzulo G, D'Amato L, Mannella F, Priorelli M, Van de Maele T, Stoianov IP, Friston K. Neural representation in active inference: Using generative models to interact with-and understand-the lived world. Ann N Y Acad Sci 2024; 1534:45-68. [PMID: 38528782 DOI: 10.1111/nyas.15118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people-to action-oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities-and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Leo D'Amato
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
- Polytechnic University of Turin, Turin, Italy
| | - Francesco Mannella
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Matteo Priorelli
- Institute of Cognitive Sciences and Technologies, National Research Council, Padua, Italy
| | - Toon Van de Maele
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium
| | - Ivilin Peev Stoianov
- Institute of Cognitive Sciences and Technologies, National Research Council, Padua, Italy
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
- VERSES Research Lab, Los Angeles, California, USA
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Trinidad Barnech G, Tejera G, Valle-Lisboa J, Núñez P, Bachiller P, Bustos P. Enhancing Robotic Perception through Synchronized Simulation and Physical Common-Sense Reasoning. SENSORS (BASEL, SWITZERLAND) 2024; 24:2249. [PMID: 38610460 PMCID: PMC11014409 DOI: 10.3390/s24072249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/24/2024] [Accepted: 01/30/2024] [Indexed: 04/14/2024]
Abstract
We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance.
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Affiliation(s)
| | - Gonzalo Tejera
- Facultad de Ingeniería, Universidad de la República, Montevideo 11200, Uruguay
| | - Juan Valle-Lisboa
- Facultad de Ciencias, Universidad de la República, Montevideo 11200, Uruguay
| | - Pedro Núñez
- Computer and Communication Technology Department, Universidad de Extremadura, 10005 Cáceres, Spain; (P.N.); (P.B.)
| | - Pilar Bachiller
- Computer and Communication Technology Department, Universidad de Extremadura, 10005 Cáceres, Spain; (P.N.); (P.B.)
| | - Pablo Bustos
- Computer and Communication Technology Department, Universidad de Extremadura, 10005 Cáceres, Spain; (P.N.); (P.B.)
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35
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Van de Maele T, Verbelen T, Mazzaglia P, Ferraro S, Dhoedt B. Object-Centric Scene Representations Using Active Inference. Neural Comput 2024; 36:677-704. [PMID: 38457764 DOI: 10.1162/neco_a_01637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/17/2023] [Indexed: 03/10/2024]
Abstract
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this letter, we propose a novel approach for scene understanding, leveraging an object-centric generative model that enables an agent to infer object category and pose in an allocentric reference frame using active inference, a neuro-inspired framework for action and perception. For evaluating the behavior of an active vision agent, we also propose a new benchmark where, given a target viewpoint of a particular object, the agent needs to find the best matching viewpoint given a workspace with randomly positioned objects in 3D. We demonstrate that our active inference agent is able to balance epistemic foraging and goal-driven behavior, and quantitatively outperforms both supervised and reinforcement learning baselines by more than a factor of two in terms of success rate.
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Affiliation(s)
| | - Tim Verbelen
- VERSES AI Research Lab, Los Angeles, CA 90016, U.S.A.
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36
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Engström J, Wei R, McDonald AD, Garcia A, O'Kelly M, Johnson L. Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference. Front Neurorobot 2024; 18:1341750. [PMID: 38576893 PMCID: PMC10991681 DOI: 10.3389/fnbot.2024.1341750] [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: 11/20/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024] Open
Abstract
Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.
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Affiliation(s)
| | - Ran Wei
- Department of Industrial and Systems Engineering, Texas A&M, College Station, TX, United States
| | - Anthony D. McDonald
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Alfredo Garcia
- Department of Industrial and Systems Engineering, Texas A&M, College Station, TX, United States
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37
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White B, Clark A, Miller M. Digital Being: social media and the predictive mind. Neurosci Conscious 2024; 2024:niae008. [PMID: 38504826 PMCID: PMC10949958 DOI: 10.1093/nc/niae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/27/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024] Open
Abstract
Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a 'generative model' of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent's generative model and of arresting the model's ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest-based on the active inference framework-some ways to understand why some forms of digital technology pose these risks, while others do not.
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Affiliation(s)
- Ben White
- School of Media, Arts and Humanities, University of Sussex, Arts A07, Brighton BN1 9RH, United Kingdom
| | - Andy Clark
- School of Media, Arts and Humanities, University of Sussex, Arts A07, Brighton BN1 9RH, United Kingdom
- Department of Philosophy, Macquarie University, Macquarie University Wallumattagal Campus Macquarie Park, Sydney, NSW 2109, Australia
| | - Mark Miller
- Monash Centre for Consciousness and Contemplative Studies, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia
- Psychology Department, University of Toronto, 100 St. George Street, 4th Floor, Sidney Smith Hall, Toronto, ON M5S 3G3, Canada
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38
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Gwyther MPD, Lenggenhager B, Windt JM, Aspell JE, Ciaunica A. Examining the association between depersonalisation traits and the bodily self in waking and dreaming. Sci Rep 2024; 14:6107. [PMID: 38480797 PMCID: PMC10937666 DOI: 10.1038/s41598-024-56119-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 02/29/2024] [Indexed: 03/17/2024] Open
Abstract
Depersonalisation (DP) is characterized by fundamental alterations to the sense of self that include feelings of detachment and estrangement from one's body. We conducted an online study in healthy participants (n = 514) with DP traits to investigate and quantify the subjective experience of body and self during waking and dreaming, as the vast majority of previous studies focussed on waking experience only. Investigating dreams in people experiencing DP symptoms may help us understand whether the dream state is a 'spared space' where people can temporarily 'retrieve' their sense of self and sense of bodily presence. We found that higher DP traits-i.e. higher scores on the Cambridge Depersonalisation Scale (CDS)-were associated with more frequent dream experiences from an outside observer perspective (r = 0.28) and more frequent dream experiences of distinct bodily sensations (r = 0.23). We also found that people with higher CDS scores had more frequent dream experiences of altered bodily perception (r = 0.24), more frequent nightmares (r = 0.33) and higher dream recall (r = 0.17). CDS scores were negatively correlated with body boundary scores (r = - 0.31) in waking states and there was a negative association between CDS scores and the degree of trust in interoceptive signals (r = - 0.52). Our study elucidates the complex phenomenology of DP in relation to bodily selfhood during waking and dreaming and suggests avenues for potential therapeutic interventions in people with chronic depersonalisation (depersonalisation -derealisation disorder).
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Affiliation(s)
- Matt P D Gwyther
- School of Psychology and Sport Science, Anglia Ruskin University, Cambridge, UK.
| | - Bigna Lenggenhager
- Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Jennifer M Windt
- Department of Philosophy, Monash University, Melbourne, Australia
- Monash Centre for Consciousness and Contemplative Studies, Melbourne, Australia
| | - Jane E Aspell
- School of Psychology and Sport Science, Anglia Ruskin University, Cambridge, UK
| | - Anna Ciaunica
- Centre for Philosophy of Science, University of Lisbon, Campo Grande, 1749-016, Lisbon, Portugal
- Institute of Cognitive Neuroscience, University College London, London, WC1N 3AR, UK
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Schiller D, Yu ANC, Alia-Klein N, Becker S, Cromwell HC, Dolcos F, Eslinger PJ, Frewen P, Kemp AH, Pace-Schott EF, Raber J, Silton RL, Stefanova E, Williams JHG, Abe N, Aghajani M, Albrecht F, Alexander R, Anders S, Aragón OR, Arias JA, Arzy S, Aue T, Baez S, Balconi M, Ballarini T, Bannister S, Banta MC, Barrett KC, Belzung C, Bensafi M, Booij L, Bookwala J, Boulanger-Bertolus J, Boutros SW, Bräscher AK, Bruno A, Busatto G, Bylsma LM, Caldwell-Harris C, Chan RCK, Cherbuin N, Chiarella J, Cipresso P, Critchley H, Croote DE, Demaree HA, Denson TF, Depue B, Derntl B, Dickson JM, Dolcos S, Drach-Zahavy A, Dubljević O, Eerola T, Ellingsen DM, Fairfield B, Ferdenzi C, Friedman BH, Fu CHY, Gatt JM, de Gelder B, Gendolla GHE, Gilam G, Goldblatt H, Gooding AEK, Gosseries O, Hamm AO, Hanson JL, Hendler T, Herbert C, Hofmann SG, Ibanez A, Joffily M, Jovanovic T, Kahrilas IJ, Kangas M, Katsumi Y, Kensinger E, Kirby LAJ, Koncz R, Koster EHW, Kozlowska K, Krach S, Kret ME, Krippl M, Kusi-Mensah K, Ladouceur CD, Laureys S, Lawrence A, Li CSR, Liddell BJ, Lidhar NK, Lowry CA, Magee K, Marin MF, Mariotti V, Martin LJ, Marusak HA, Mayer AV, Merner AR, Minnier J, Moll J, Morrison RG, Moore M, Mouly AM, Mueller SC, Mühlberger A, Murphy NA, Muscatello MRA, Musser ED, Newton TL, Noll-Hussong M, Norrholm SD, Northoff G, Nusslock R, Okon-Singer H, Olino TM, Ortner C, Owolabi M, Padulo C, Palermo R, Palumbo R, Palumbo S, Papadelis C, Pegna AJ, Pellegrini S, Peltonen K, Penninx BWJH, Pietrini P, Pinna G, Lobo RP, Polnaszek KL, Polyakova M, Rabinak C, Helene Richter S, Richter T, Riva G, Rizzo A, Robinson JL, Rosa P, Sachdev PS, Sato W, Schroeter ML, Schweizer S, Shiban Y, Siddharthan A, Siedlecka E, Smith RC, Soreq H, Spangler DP, Stern ER, Styliadis C, Sullivan GB, Swain JE, Urben S, Van den Stock J, Vander Kooij MA, van Overveld M, Van Rheenen TE, VanElzakker MB, Ventura-Bort C, Verona E, Volk T, Wang Y, Weingast LT, Weymar M, Williams C, Willis ML, Yamashita P, Zahn R, Zupan B, Lowe L. The Human Affectome. Neurosci Biobehav Rev 2024; 158:105450. [PMID: 37925091 PMCID: PMC11003721 DOI: 10.1016/j.neubiorev.2023.105450] [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: 06/11/2022] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/06/2023]
Abstract
Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.
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Affiliation(s)
- Daniela Schiller
- Department of Psychiatry, the Nash Family Department of Neuroscience, and the Friedman Brain Institute, at the Icahn School of Medicine at Mount Sinai, New York, NY, United States.
| | - Alessandra N C Yu
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
| | - Nelly Alia-Klein
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Susanne Becker
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany; Integrative Spinal Research Group, Department of Chiropractic Medicine, University Hospital Balgrist, University of Zurich, Balgrist Campus, Lengghalde 5, 8008 Zurich, Switzerland
| | - Howard C Cromwell
- J.P. Scott Center for Neuroscience, Mind and Behavior, Department of Psychology, Bowling Green State University, Bowling Green, OH 43403, United States
| | - Florin Dolcos
- Beckman Institute for Advanced Science & Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Paul J Eslinger
- Departments of Neurology, Neural & Behavioral Science, Radiology, and Public Health Sciences, Penn State Hershey Medical Center and College of Medicine, Hershey, PA, United States
| | - Paul Frewen
- Departments of Psychiatry, Psychology and Neuroscience at the University of Western Ontario, London, Ontario, Canada
| | - Andrew H Kemp
- School of Psychology, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, United Kingdom
| | - Edward F Pace-Schott
- Harvard Medical School and Massachusetts General Hospital, Department of Psychiatry, Boston, MA, United States; Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Jacob Raber
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, United States; Departments of Neurology, Radiation Medicine, Psychiatry, and Division of Neuroscience, ONPRC, Oregon Health & Science University, Portland, OR, United States
| | - Rebecca L Silton
- Department of Psychology, Loyola University Chicago, Chicago, IL, United States
| | - Elka Stefanova
- Faculty of Medicine, University of Belgrade, Serbia; Neurology Clinic, Clinical Center of Serbia, Serbia
| | - Justin H G Williams
- Griffith University, Gold Coast Campus, 1 Parklands Dr, Southport, QLD 4215, Australia
| | - Nobuhito Abe
- Institute for the Future of Human Society, Kyoto University, 46 Shimoadachi-cho, Yoshida Sakyo-ku, Kyoto, Japan
| | - Moji Aghajani
- Institute of Education & Child Studies, Section Forensic Family & Youth Care, Leiden University, the Netherlands; Department of Psychiatry, Amsterdam UMC, Location VUMC, GGZ InGeest Research & Innovation, Amsterdam Neuroscience, the Netherlands
| | - Franziska Albrecht
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany; Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Karolinska University Hospital, Women's Health and Allied Health Professionals Theme, Medical unit Occupational Therapy & Physiotherapy, Stockholm, Sweden
| | - Rebecca Alexander
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia; Australian National University, Canberra, ACT, Australia
| | - Silke Anders
- Department of Neurology, University of Lübeck, Lübeck, Germany; Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Oriana R Aragón
- Yale University, 2 Hillhouse Ave, New Haven, CT, United States; Cincinnati University, Marketing Department, 2906 Woodside Drive, Cincinnati, OH 45221-0145, United States
| | - Juan A Arias
- School of Psychology, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, United Kingdom; Department of Statistics, Mathematical Analysis, and Operational Research, Universidade de Santiago de Compostela, Spain; The Galician Center for Mathematical Research and Technology (CITMAga), 15782 Santiago de Compostela, Spain
| | - Shahar Arzy
- Department of Medical Neurobiology, Hebrew University, Jerusalem, Israel
| | - Tatjana Aue
- Institute of Psychology, University of Bern, Fabrikstr. 8, 3012 Bern, Switzerland
| | | | - Michela Balconi
- International Research Center for Cognitive Applied Neuroscience, Catholic University of Milan, Milan, Italy
| | - Tommaso Ballarini
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Scott Bannister
- Durham University, Palace Green, DH1 RL3 Durham, United Kingdom
| | | | - Karen Caplovitz Barrett
- Department of Human Development & Family Studies, Colorado State University, Fort Collins, CO, United States; Department of Community & Behavioral Health, Colorado School of Public Health, Denver, CO, United States
| | | | - Moustafa Bensafi
- Research Center in Neurosciences of Lyon, CNRS UMR5292, INSERM U1028, Claude Bernard University Lyon 1, Lyon, Centre Hospitalier Le Vinatier, 95 bd Pinel, 69675 Bron Cedex, France
| | - Linda Booij
- Department of Psychology, Concordia University, Montreal, Canada; CHU Sainte-Justine, University of Montreal, Montreal, Canada
| | - Jamila Bookwala
- Department of Psychology, Lafayette College, Easton, PA, United States
| | - Julie Boulanger-Bertolus
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States
| | - Sydney Weber Boutros
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, United States
| | - Anne-Kathrin Bräscher
- Department of Clinical Psychology, Psychotherapy and Experimental Psychopathology, University of Mainz, Wallstr. 3, 55122 Mainz, Germany; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Antonio Bruno
- Department of Biomedical, Dental Sciences and Morpho-Functional Imaging - University of Messina, Italy
| | - Geraldo Busatto
- Laboratory of Psychiatric Neuroimaging (LIM-21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Lauren M Bylsma
- Departments of Psychiatry and Psychology; and the Center for Neural Basis of Cognition, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | | | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Nicolas Cherbuin
- Centre for Research on Ageing, Health, and Wellbeing, Australian National University, Canberra, ACT, Australia
| | - Julian Chiarella
- Department of Psychology, Concordia University, Montreal, Canada; CHU Sainte-Justine, University of Montreal, Montreal, Canada
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab., Istituto Auxologico Italiano (IRCCS), Milan, Italy; Department of Psychology, University of Turin, Turin, Italy
| | - Hugo Critchley
- Psychiatry, Department of Neuroscience, Brighton and Sussex Medical School (BSMS), University of Sussex, Sussex, United Kingdom
| | - Denise E Croote
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai and Friedman Brain Institute, New York, NY 10029, United States; Hospital Universitário Gaffrée e Guinle, Universidade do Rio de Janeiro, Brazil
| | - Heath A Demaree
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Thomas F Denson
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
| | - Brendan Depue
- Departments of Psychological and Brain Sciences and Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY, United States
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Joanne M Dickson
- Edith Cowan University, Psychology Discipline, School of Arts and Humanities, 270 Joondalup Dr, Joondalup, WA 6027, Australia
| | - Sanda Dolcos
- Beckman Institute for Advanced Science & Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Anat Drach-Zahavy
- The Faculty of Health and Welfare Sciences, University of Haifa, Haifa, Israel
| | - Olga Dubljević
- Neurology Clinic, Clinical Center of Serbia, Serbia; Institute for Biological Research "Siniša Stanković", National Institute of Republic of Serbia, Belgrade, Serbia
| | - Tuomas Eerola
- Durham University, Palace Green, DH1 RL3 Durham, United Kingdom
| | - Dan-Mikael Ellingsen
- Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Beth Fairfield
- Department of Humanistic Studies, University of Naples Federico II, Naples, Italy; UniCamillus, International Medical University, Rome, Italy
| | - Camille Ferdenzi
- Research Center in Neurosciences of Lyon, CNRS UMR5292, INSERM U1028, Claude Bernard University Lyon 1, Lyon, Centre Hospitalier Le Vinatier, 95 bd Pinel, 69675 Bron Cedex, France
| | - Bruce H Friedman
- Department of Psychology, Virginia Tech, Blacksburg, VA, United States
| | - Cynthia H Y Fu
- School of Psychology, University of East London, United Kingdom; Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Justine M Gatt
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia; School of Psychology, University of New South Wales, Randwick, Sydney, NSW, Australia
| | - Beatrice de Gelder
- Department of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Guido H E Gendolla
- Geneva Motivation Lab, University of Geneva, FPSE, Section of Psychology, CH-1211 Geneva 4, Switzerland
| | - Gadi Gilam
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; Systems Neuroscience and Pain Laboratory, Stanford University School of Medicine, CA, United States
| | - Hadass Goldblatt
- Department of Nursing, Faculty of Social Welfare & Health Sciences, University of Haifa, Haifa, Israel
| | | | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness & Centre du Cerveau2, University and University Hospital of Liege, Liege, Belgium
| | - Alfons O Hamm
- Department of Biological and Clinical Psychology/Psychotherapy, University of Greifswald, Greifswald, Germany
| | - Jamie L Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15206, United States
| | - Talma Hendler
- Tel Aviv Center for Brain Function, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; School of Psychological Sciences, Tel-Aviv University, Tel Aviv, Israel
| | - Cornelia Herbert
- Department of Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
| | - Stefan G Hofmann
- Department of Clinical Psychology, Philipps University Marburg, Germany
| | - Agustin Ibanez
- Universidad de San Andres, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), United States and Trinity Collegue Dublin (TCD), Ireland
| | - Mateus Joffily
- Groupe d'Analyse et de Théorie Economique (GATE), 93 Chemin des Mouilles, 69130 Écully, France
| | - Tanja Jovanovic
- Department of Psychiatry and Behavaioral Neurosciences, Wayne State University, Detroit, MI, United States
| | - Ian J Kahrilas
- Department of Psychology, Loyola University Chicago, Chicago, IL, United States
| | - Maria Kangas
- Department of Psychology, Macquarie University, Sydney, Australia
| | - Yuta Katsumi
- Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, United States; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Elizabeth Kensinger
- Department of Psychology and Neuroscience, Boston College, Boston, MA, United States
| | - Lauren A J Kirby
- Department of Psychology and Counseling, University of Texas at Tyler, Tyler, TX, United States
| | - Rebecca Koncz
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, Australia; Specialty of Psychiatry, The University of Sydney, Concord, New South Wales, Australia
| | - Ernst H W Koster
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | | | - Sören Krach
- Social Neuroscience Lab, Translational Psychiatry Unit, University of Lübeck, Lübeck, Germany
| | - Mariska E Kret
- Leiden University, Cognitive Psychology, Pieter de la Court, Waassenaarseweg 52, Leiden 2333 AK, the Netherlands
| | - Martin Krippl
- Faculty of Natural Sciences, Department of Psychology, Otto von Guericke University Magdeburg, Universitätsplatz 2, Magdeburg, Germany
| | - Kwabena Kusi-Mensah
- Department of Psychiatry, Komfo Anokye Teaching Hospital, P. O. Box 1934, Kumasi, Ghana; Department of Psychiatry, University of Cambridge, Darwin College, Silver Street, CB3 9EU Cambridge, United Kingdom; Behavioural Sciences Department, School of Medicine and Dentistry, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Cecile D Ladouceur
- Departments of Psychiatry and Psychology and the Center for Neural Basis of Cognition (CNBC), University of Pittsburgh, Pittsburgh, PA, United States
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness & Centre du Cerveau2, University and University Hospital of Liege, Liege, Belgium
| | - Alistair Lawrence
- Scotland's Rural College, King's Buildings, Edinburgh, Scotland; The Roslin Institute, University of Edinburgh, Easter Bush, Scotland
| | - Chiang-Shan R Li
- Connecticut Mental Health Centre, Yale University, New Haven, CT, United States
| | - Belinda J Liddell
- School of Psychology, University of New South Wales, Randwick, Sydney, NSW, Australia
| | - Navdeep K Lidhar
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Christopher A Lowry
- Department of Integrative Physiology and Center for Neuroscience, University of Colorado Boulder, Boulder, CO, United States
| | - Kelsey Magee
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Marie-France Marin
- Department of Psychology, Université du Québec à Montréal, Montreal, Canada; Research Center, Institut universitaire en santé mentale de Montréal, Montreal, Canada
| | - Veronica Mariotti
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Loren J Martin
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON, Canada
| | - Hilary A Marusak
- Department of Psychiatry and Behavaioral Neurosciences, Wayne State University, Detroit, MI, United States; Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, MI, United States
| | - Annalina V Mayer
- Social Neuroscience Lab, Translational Psychiatry Unit, University of Lübeck, Lübeck, Germany
| | - Amanda R Merner
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Jessica Minnier
- School of Public Health, Oregon Health & Science University, Portland, OR, United States
| | - Jorge Moll
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Robert G Morrison
- Department of Psychology, Loyola University Chicago, Chicago, IL, United States
| | - Matthew Moore
- Beckman Institute for Advanced Science & Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, United States; War Related Illness and Injury Study Center (WRIISC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States
| | - Anne-Marie Mouly
- Lyon Neuroscience Research Center, CNRS-UMR 5292, INSERM U1028, Universite Lyon, Lyon, France
| | - Sven C Mueller
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | - Andreas Mühlberger
- Department of Psychology (Clinical Psychology and Psychotherapy), University of Regensburg, Regensburg, Germany
| | - Nora A Murphy
- Department of Psychology, Loyola Marymount University, Los Angeles, CA, United States
| | | | - Erica D Musser
- Center for Children and Families, Department of Psychology, Florida International University, Miami, FL, United States
| | - Tamara L Newton
- Department of Psychological and Brain Sciences, University of Louisville, Louisville, KY, United States
| | - Michael Noll-Hussong
- Psychosomatic Medicine and Psychotherapy, TU Muenchen, Langerstrasse 3, D-81675 Muenchen, Germany
| | - Seth Davin Norrholm
- Department of Psychiatry and Behavaioral Neurosciences, Wayne State University, Detroit, MI, United States
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa Institute of Mental Health Research, Royal Ottawa Mental Health Centre, Canada
| | - Robin Nusslock
- Department of Psychology and Institute for Policy Research, Northwestern University, 2029 Sheridan Road, Evanston, IL, United States
| | - Hadas Okon-Singer
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Thomas M Olino
- Department of Psychology, Temple University, 1701N. 13th St, Philadelphia, PA, United States
| | - Catherine Ortner
- Thompson Rivers University, Department of Psychology, 805 TRU Way, Kamloops, BC, Canada
| | - Mayowa Owolabi
- Department of Medicine and Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan; University College Hospital, Ibadan, Oyo State, Nigeria; Blossom Specialist Medical Center Ibadan, Oyo State, Nigeria
| | - Caterina Padulo
- Department of Psychological, Health and Territorial Sciences, University of Chieti, Chieti, Italy
| | - Romina Palermo
- School of Psychological Science, University of Western Australia, Perth, WA, Australia
| | - Rocco Palumbo
- Department of Psychological, Health and Territorial Sciences, University of Chieti, Chieti, Italy
| | - Sara Palumbo
- Department of Surgical, Medical and Molecular Pathology and of Critical Care, University of Pisa, Pisa, Italy
| | - Christos Papadelis
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Alan J Pegna
- School of Psychology, University of Queensland, Saint Lucia, Queensland, Australia
| | - Silvia Pellegrini
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Kirsi Peltonen
- Research Centre for Child Psychiatry, University of Turku, Turku, Finland; INVEST Research Flagship, University of Turku, Turku, Finland
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Location VUMC, GGZ InGeest Research & Innovation, Amsterdam Neuroscience, the Netherlands
| | | | - Graziano Pinna
- The Psychiatric Institute, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Rosario Pintos Lobo
- Center for Children and Families, Department of Psychology, Florida International University, Miami, FL, United States
| | - Kelly L Polnaszek
- Department of Psychology, Loyola University Chicago, Chicago, IL, United States
| | - Maryna Polyakova
- Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christine Rabinak
- Department of Pharmacy Practice, Wayne State University, Detroit, MI, United States
| | - S Helene Richter
- Department of Behavioural Biology, University of Münster, Badestraße 13, Münster, Germany
| | - Thalia Richter
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab., Istituto Auxologico Italiano (IRCCS), Milan, Italy; Humane Technology Lab., Università Cattolica del Sacro Cuore, Milan, Italy
| | - Amelia Rizzo
- Department of Biomedical, Dental Sciences and Morpho-Functional Imaging - University of Messina, Italy
| | | | - Pedro Rosa
- Laboratory of Psychiatric Neuroimaging (LIM-21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, Australia; Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, Australia
| | - Wataru Sato
- Psychological Process Research Team, Guardian Robot Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
| | - Matthias L Schroeter
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Susanne Schweizer
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom; School of Psychology, University of New South Wales, Sydney, Australia
| | - Youssef Shiban
- Department of Psychology (Clinical Psychology and Psychotherapy), University of Regensburg, Regensburg, Germany; Department of Psychology (Clinical Psychology and Psychotherapy Research), PFH - Private University of Applied Sciences, Gottingen, Germany
| | - Advaith Siddharthan
- Knowledge Media Institute, The Open University, Milton Keynes MK7 6AA, United Kingdom
| | - Ewa Siedlecka
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
| | - Robert C Smith
- Departments of Medicine and Psychiatry, Michigan State University, East Lansing, MI, United States
| | - Hermona Soreq
- Department of Biological Chemistry, Edmond and Lily Safra Center of Brain Science and The Institute of Life Sciences, Hebrew University, Jerusalem, Israel
| | - Derek P Spangler
- Department of Biobehavioral Health, The Pennsylvania State University, State College, PA, United States
| | - Emily R Stern
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States; New York University School of Medicine, New York, NY, United States
| | - Charis Styliadis
- Neuroscience of Cognition and Affection group, Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - James E Swain
- Departments of Psychiatry & Behavioral Health, Psychology, Obstetrics, Gynecology & Reproductive Medicine, and Program in Public Health, Renaissance School of Medicine at Stony Brook University, New York, United States
| | - Sébastien Urben
- Division of Child and Adolescent Psychiatry, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Jan Van den Stock
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Michael A Vander Kooij
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, Universitatsmedizin der Johannes Guttenberg University Medical Center, Mainz, Germany
| | | | - Tamsyn E Van Rheenen
- University of Melbourne, Melbourne Neuropsychiatry Centre, Department of Psychiatry, 161 Barry Street, Carlton, VIC, Australia
| | - Michael B VanElzakker
- Division of Neurotherapeutics, Massachusetts General Hospital, Boston, MA, United States
| | - Carlos Ventura-Bort
- Department of Biological Psychology and Affective Science, Faculty of Human Sciences, University of Potsdam, Potsdam, Germany
| | - Edelyn Verona
- Department of Psychology, University of South Florida, Tampa, FL, United States
| | - Tyler Volk
- Professor Emeritus of Biology and Environmental Studies, New York University, New York, NY, United States
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Leah T Weingast
- Department of Social Work and Human Services and the Department of Psychological Sciences, Center for Young Adult Addiction and Recovery, Kennesaw State University, Kennesaw, GA, United States
| | - Mathias Weymar
- Department of Biological Psychology and Affective Science, Faculty of Human Sciences, University of Potsdam, Potsdam, Germany; Faculty of Health Sciences Brandenburg, University of Potsdam, Germany
| | - Claire Williams
- School of Psychology, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, United Kingdom; Elysium Neurological Services, Elysium Healthcare, The Avalon Centre, United Kingdom
| | - Megan L Willis
- School of Behavioural and Health Sciences, Australian Catholic University, Sydney, NSW, Australia
| | - Paula Yamashita
- Department of Integrative Physiology and Center for Neuroscience, University of Colorado Boulder, Boulder, CO, United States
| | - Roland Zahn
- Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Barbra Zupan
- Central Queensland University, School of Health, Medical and Applied Sciences, Bruce Highway, Rockhampton, QLD, Australia
| | - Leroy Lowe
- Neuroqualia (NGO), Truro, Nova Scotia, Canada.
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Dercksen TT, Widmann A, Noesselt T, Wetzel N. Somatosensory omissions reveal action-related predictive processing. Hum Brain Mapp 2024; 45:e26550. [PMID: 38050773 PMCID: PMC10915725 DOI: 10.1002/hbm.26550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/07/2023] [Accepted: 11/16/2023] [Indexed: 12/06/2023] Open
Abstract
The intricate relation between action and somatosensory perception has been studied extensively in the past decades. Generally, a forward model is thought to predict the somatosensory consequences of an action. These models propose that when an action is reliably coupled to a tactile stimulus, unexpected absence of the stimulus should elicit prediction error. Although such omission responses have been demonstrated in the auditory modality, it remains unknown whether this mechanism generalizes across modalities. This study therefore aimed to record action-induced somatosensory omission responses using EEG in humans. Self-paced button presses were coupled to somatosensory stimuli in 88% of trials, allowing a prediction, or in 50% of trials, not allowing a prediction. In the 88% condition, stimulus omission resulted in a neural response consisting of multiple components, as revealed by temporal principal component analysis. The oN1 response suggests similar sensory sources as stimulus-evoked activity, but an origin outside primary cortex. Subsequent oN2 and oP3 responses, as previously observed in the auditory domain, likely reflect modality-unspecific higher order processes. Together, findings straightforwardly demonstrate somatosensory predictions during action and provide evidence for a partially amodal mechanism of prediction error generation.
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Affiliation(s)
- Tjerk T. Dercksen
- Research Group Neurocognitive DevelopmentLeibniz Institute for NeurobiologyMagdeburgGermany
- Center for Behavioral Brain SciencesMagdeburgGermany
| | - Andreas Widmann
- Research Group Neurocognitive DevelopmentLeibniz Institute for NeurobiologyMagdeburgGermany
- Wilhelm Wundt Institute for PsychologyLeipzig UniversityLeipzigGermany
| | - Tömme Noesselt
- Center for Behavioral Brain SciencesMagdeburgGermany
- Department of Biological PsychologyOtto‐von‐Guericke‐University MagdeburgMagdeburgGermany
| | - Nicole Wetzel
- Research Group Neurocognitive DevelopmentLeibniz Institute for NeurobiologyMagdeburgGermany
- Center for Behavioral Brain SciencesMagdeburgGermany
- University of Applied Sciences Magdeburg‐StendalStendalGermany
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41
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Liu X, Guo J, Jiang Z, Liu X, Chen H, Zhang Y, Wang J, Liu C, Gao Q, Chen H. Compressed cerebellar functional connectome hierarchy in spinocerebellar ataxia type 3. Hum Brain Mapp 2024; 45:e26624. [PMID: 38376240 PMCID: PMC10878347 DOI: 10.1002/hbm.26624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024] Open
Abstract
Spinocerebellar ataxia type 3 (SCA3) is an inherited movement disorder characterized by a progressive decline in motor coordination. Despite the extensive functional connectivity (FC) alterations reported in previous SCA3 studies in the cerebellum and cerebellar-cerebral pathways, the influence of these FC disturbances on the hierarchical organization of cerebellar functional regions remains unclear. Here, we compared 35 SCA3 patients with 48 age- and sex-matched healthy controls using a combination of voxel-based morphometry and resting-state functional magnetic resonance imaging to investigate whether cerebellar hierarchical organization is altered in SCA3. Utilizing connectome gradients, we identified the gradient axis of cerebellar hierarchical organization, spanning sensorimotor to transmodal (task-unfocused) regions. Compared to healthy controls, SCA3 patients showed a compressed hierarchical organization in the cerebellum at both voxel-level (p < .05, TFCE corrected) and network-level (p < .05, FDR corrected). This pattern was observed in both intra-cerebellar and cerebellar-cerebral gradients. We observed that decreased intra-cerebellar gradient scores in bilateral Crus I/II both negatively correlated with SARA scores (left/right Crus I/II: r = -.48/-.50, p = .04/.04, FDR corrected), while increased cerebellar-cerebral gradients scores in the vermis showed a positive correlation with disease duration (r = .48, p = .04, FDR corrected). Control analyses of cerebellar gray matter atrophy revealed that gradient alterations were associated with cerebellar volume loss. Further FC analysis showed increased functional connectivity in both unimodal and transmodal areas, potentially supporting the disrupted cerebellar functional hierarchy uncovered by the gradients. Our findings provide novel evidence regarding alterations in the cerebellar functional hierarchy in SCA3.
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Affiliation(s)
- Xinyuan Liu
- Department of Radiology, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's HospitalUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Zhouyu Jiang
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xingli Liu
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Hui Chen
- Department of Radiology, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Yuhan Zhang
- Department of Radiology, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Jian Wang
- Department of Radiology, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Chen Liu
- Department of Radiology, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Qing Gao
- MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Mathematical SciencesUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Huafu Chen
- Department of Radiology, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's HospitalUniversity of Electronic Science and Technology of ChinaChengduChina
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42
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Hodson R, Mehta M, Smith R. The empirical status of predictive coding and active inference. Neurosci Biobehav Rev 2024; 157:105473. [PMID: 38030100 DOI: 10.1016/j.neubiorev.2023.105473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.
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Affiliation(s)
| | | | - Ryan Smith
- Laureate Institute for Brain Research, USA.
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43
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Pezzulo G, Parr T, Friston K. Active inference as a theory of sentient behavior. Biol Psychol 2024; 186:108741. [PMID: 38182015 DOI: 10.1016/j.biopsycho.2023.108741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/05/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024]
Abstract
This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Thomas Parr
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA
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44
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Pezzulo G, Parr T, Cisek P, Clark A, Friston K. Generating meaning: active inference and the scope and limits of passive AI. Trends Cogn Sci 2024; 28:97-112. [PMID: 37973519 DOI: 10.1016/j.tics.2023.10.002] [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: 06/08/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 11/19/2023]
Abstract
Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Thomas Parr
- Nuffield Department of Clinical Neurosciences, University of Oxford
| | - Paul Cisek
- Department of Neuroscience, University of Montréal, Montréal, Québec, Canada
| | - Andy Clark
- Department of Philosophy, University of Sussex, Brighton, UK; Department of Informatics, University of Sussex, Brighton, UK; Department of Philosophy, Macquarie University, Sydney, New South Wales, Australia
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK; VERSES AI Research Lab, Los Angeles, CA, USA
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45
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Krupnik V, Danilova N. To be or not to be: The active inference of suicide. Neurosci Biobehav Rev 2024; 157:105531. [PMID: 38176631 DOI: 10.1016/j.neubiorev.2023.105531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/27/2023] [Accepted: 12/29/2023] [Indexed: 01/06/2024]
Abstract
Suicide presents an apparent paradox as a behavior whose motivation is not obvious since its outcome is non-existence and cannot be experienced. To address this paradox, we propose to frame suicide in the integrated theory of stress and active inference. We present an active inference-based cognitive model of suicide as a type of stress response hanging in cognitive balance between predicting self-preservation and self-destruction. In it, self-efficacy emerges as a meta-cognitive regulator that can bias the model toward either survival or suicide. The model suggests conditions under which cognitive homeostasis can override physiological homeostasis in motivating self-destruction. We also present a model proto-suicidal behavior, programmed cell death (apoptosis), in active inference terms to illustrate how an active inference model of self-destruction can be embodied in molecular mechanisms and to offer a hypothesis on another puzzle of suicide: why only humans among brain-endowed animals are known to practice it.
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Affiliation(s)
- Valery Krupnik
- Department of Mental Health, Naval Hospital Camp Pendleton, Camp Pendleton, CA, USA.
| | - Nadia Danilova
- Department of Cell Biology, UCLA (retired), Los Angeles, CA, USA
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46
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Krupnik V. I like therefore I can, and I can therefore I like: the role of self-efficacy and affect in active inference of allostasis. Front Neural Circuits 2024; 18:1283372. [PMID: 38322807 PMCID: PMC10839114 DOI: 10.3389/fncir.2024.1283372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
Active inference (AIF) is a theory of the behavior of information-processing open dynamic systems. It describes them as generative models (GM) generating inferences on the causes of sensory input they receive from their environment. Based on these inferences, GMs generate predictions about sensory input. The discrepancy between a prediction and the actual input results in prediction error. GMs then execute action policies predicted to minimize the prediction error. The free-energy principle provides a rationale for AIF by stipulating that information-processing open systems must constantly minimize their free energy (through suppressing the cumulative prediction error) to avoid decay. The theory of homeostasis and allostasis has a similar logic. Homeostatic set points are expectations of living organisms. Discrepancies between set points and actual states generate stress. For optimal functioning, organisms avoid stress by preserving homeostasis. Theories of AIF and homeostasis have recently converged, with AIF providing a formal account for homeo- and allostasis. In this paper, we present bacterial chemotaxis as molecular AIF, where mutual constraints by extero- and interoception play an essential role in controlling bacterial behavior supporting homeostasis. Extending this insight to the brain, we propose a conceptual model of the brain homeostatic GM, in which we suggest partition of the brain GM into cognitive and physiological homeostatic GMs. We outline their mutual regulation as well as their integration based on the free-energy principle. From this analysis, affect and self-efficacy emerge as the main regulators of the cognitive homeostatic GM. We suggest fatigue and depression as target neurocognitive phenomena for studying the neural mechanisms of such regulation.
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Affiliation(s)
- Valery Krupnik
- Department of Mental Health, Naval Hospital Camp Pendleton, Camp Pendleton, Oceanside, CA, United States
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47
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Novicky F, Parr T, Friston K, Mirza MB, Sajid N. Bistable perception, precision and neuromodulation. Cereb Cortex 2024; 34:bhad401. [PMID: 37950879 PMCID: PMC10793076 DOI: 10.1093/cercor/bhad401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/13/2023] Open
Abstract
Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e. inferences) of the same stimulus ensue from specific eye movements that shift the focus to a different visual feature. Formally, these inferences are a consequence of precision control that determines how confident beliefs are and change the frequency with which one can perceive-and alternate between-two distinct percepts. We hypothesized that there are multiple, but distinct, ways in which precision modulation can interact to give rise to a similar frequency of bistable perception. We validated this using numerical simulations of the Necker cube paradigm and demonstrate the multiple routes that underwrite the frequency of perceptual alternation. Our results provide an (enactive) computational account of the intricate precision balance underwriting bistable perception. Importantly, these precision parameters can be considered the computational homologs of particular neurotransmitters-i.e. acetylcholine, noradrenaline, dopamine-that have been previously implicated in controlling bistable perception, providing a computational link between the neurochemistry and perception.
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Affiliation(s)
- Filip Novicky
- Department of Neurophysics, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, Netherlands
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 406229 ER, Maastricht, Netherlands
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square London WC1N 3AR, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square London WC1N 3AR, United Kingdom
| | - Muammer Berk Mirza
- Department of Psychology, University of Cambridge, Downing Pl, Cambridge CB2 3EB, United Kingdom
| | - Noor Sajid
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square London WC1N 3AR, United Kingdom
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48
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Friston KJ, Parr T, Heins C, Constant A, Friedman D, Isomura T, Fields C, Verbelen T, Ramstead M, Clippinger J, Frith CD. Federated inference and belief sharing. Neurosci Biobehav Rev 2024; 156:105500. [PMID: 38056542 PMCID: PMC11139662 DOI: 10.1016/j.neubiorev.2023.105500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/08/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA.
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Conor Heins
- VERSES AI Research Lab, Los Angeles, CA 90016, USA; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78457 Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, 78457 Konstanz, Germany; Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Axel Constant
- VERSES AI Research Lab, Los Angeles, CA 90016, USA; School of Engineering and Informatics, The University of Sussex, Brighton, UK
| | - Daniel Friedman
- Department of Entomology and Nematology, University of California, Davis, Davis, CA, USA; Active Inference Institute, Davis, CA 95616, USA
| | - Takuya Isomura
- Brain Intelligence Theory Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Chris Fields
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA
| | - Tim Verbelen
- VERSES AI Research Lab, Los Angeles, CA 90016, USA
| | - Maxwell Ramstead
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA
| | | | - Christopher D Frith
- Institute of Philosophy, School of Advanced Studies, University of London, UK
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49
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Schubert J, Suess N, Weisz N. Individual prediction tendencies do not generalize across modalities. Psychophysiology 2024; 61:e14435. [PMID: 37691098 PMCID: PMC10909557 DOI: 10.1111/psyp.14435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/04/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Predictive processing theories, which model the brain as a "prediction machine", explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value for clinical psychiatry, prediction tendencies are often implicitly conceptualized as individual traits or as tendencies that generalize across situations. As this has not yet explicitly been shown, in the current study, we quantify to what extent the individual tendency to anticipate sensory features of high probability generalizes across modalities. Using magnetoencephalography (MEG), we recorded brain activity while participants were presented with a sequence of four different (either visual or auditory) stimuli, which changed according to predefined transitional probabilities of two entropy levels: ordered vs. random. Our results show that, on a group-level, under conditions of low entropy, stimulus features of high probability are preactivated in the auditory but not in the visual modality. Crucially, the magnitude of the individual tendency to predict sensory events seems not to correlate between the two modalities. Furthermore, reliability statistics indicate poor internal consistency, suggesting that the measures from the different modalities are unlikely to reflect a single, common cognitive process. In sum, our findings suggest that quantification and interpretation of individual prediction tendencies cannot be generalized across modalities.
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Affiliation(s)
- Juliane Schubert
- Centre for Cognitive Neuroscience and Department of PsychologyUniversity of SalzburgSalzburgAustria
| | - Nina Suess
- Centre for Cognitive Neuroscience and Department of PsychologyUniversity of SalzburgSalzburgAustria
| | - Nathan Weisz
- Centre for Cognitive Neuroscience and Department of PsychologyUniversity of SalzburgSalzburgAustria
- Neuroscience InstituteChristian Doppler University Hospital, Paracelsus Medical UniversitySalzburgAustria
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50
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Lin CHS, Do TT, Unsworth L, Garrido MI. Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer. PLoS Comput Biol 2024; 20:e1011769. [PMID: 38190413 PMCID: PMC10798629 DOI: 10.1371/journal.pcbi.1011769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 01/19/2024] [Accepted: 12/18/2023] [Indexed: 01/10/2024] Open
Abstract
Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively demanding Bayesian computations for behaviours. Across two studies, we trained participants to estimate hidden locations of a target drawn from priors with different levels of uncertainty. In each trial, scattered dots provided noisy likelihood information about the target location. Participants showed that they learned the priors and combined prior and likelihood information to infer target locations in a Bayes fashion. We then introduced a transfer condition presenting a trained prior and a likelihood that has never been put together during training. How well participants integrate this novel likelihood with their learned prior is an indicator of whether participants perform Bayesian computations. In one study, participants experienced the newly introduced likelihood, which was paired with a different prior, during training. Participants changed likelihood weighting following expected directions although the degrees of change were significantly lower than Bayes-optimal predictions. In another group, the novel likelihoods were never used during training. We found people integrated a new likelihood within (interpolation) better than the one outside (extrapolation) the range of their previous learning experience and they were quantitatively Bayes-suboptimal in both. We replicated the findings of both studies in a validation dataset. Our results showed that Bayesian behaviours may not always be achieved by a full Bayesian computation. Future studies can apply our approach to different tasks to enhance the understanding of decision-making mechanisms.
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Affiliation(s)
- Chin-Hsuan Sophie Lin
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Trang Thuy Do
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Lee Unsworth
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Marta I. Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Australia
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