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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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2
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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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3
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Luo T, Fu S. EXPRESS: Contextual uncertainty of visual scene modulates object-based attention. Q J Exp Psychol (Hove) 2022; 76:44-53. [PMID: 35212254 DOI: 10.1177/17470218221086550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Contextual information involves invariant properties that are critical in selective attention. There is no direct evidence showing the effect of contextual information on object-based selective attention. The current study aimed to investigate the role of contextual uncertainty on object-based effect using a flanker task and to clarify the contradictory results obtained in previous studies. Herein, contextual uncertainty specifically referred to the configurations of the stimuli presented randomly as vertical or horizontal displays (high contextual uncertainty) that was reduced by showing consistent configurations within a block, via implicit learning of configuration (low contextual uncertainty). In Experiment 1, the object-based effect was observed under the high uncertainty condition and disappeared under the low uncertainty condition, demonstrating that contextual uncertainty modulated object-based attention. Experiment 2 provided explicit knowledge of the configural orientations, which can be utilised to sufficiently guide subsequent perception with increase in cueing interval, and therefore, affected contextual uncertainty. Relative to a short cueing interval, the long cueing interval enabled the participants to utilise the contextual knowledge for guiding visual attention and reducing uncertainty. Consistent with the finding in Experiment 1, the explicit manipulation of contextual uncertainty affected the object-based effect. The results proved that contextual uncertainty played an important role in prioritisation in object-based attentional selection. The mechanism of the interplay between contextual uncertainty and object-based attention was discussed.
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Affiliation(s)
- Ting Luo
- Department of Psychology, Jiangsu Normal University, Xuzhou, 221116, China12675.,Department of Psychology, Tsinghua University, Beijing 100084, China
| | - Shimin Fu
- Department of Psychology and Center for Brain and Cognitive Sciences, Guangzhou University, Guangzhou 510006, China 47875
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4
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Sennesh E, Theriault J, Brooks D, van de Meent JW, Barrett LF, Quigley KS. Interoception as modeling, allostasis as control. Biol Psychol 2021; 167:108242. [PMID: 34942287 DOI: 10.1016/j.biopsycho.2021.108242] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 01/09/2023]
Abstract
The brain regulates the body by anticipating its needs and attempting to meet them before they arise - a process called allostasis. Allostasis requires a model of the changing sensory conditions within the body, a process called interoception. In this paper, we examine how interoception may provide performance feedback for allostasis. We suggest studying allostasis in terms of control theory, reviewing control theory's applications to related issues in physiology, motor control, and decision making. We synthesize these by relating them to the important properties of allostatic regulation as a control problem. We then sketch a novel formalism for how the brain might perform allostatic control of the viscera by analogy to skeletomotor control, including a mathematical view on how interoception acts as performance feedback for allostasis. Finally, we suggest ways to test implications of our hypotheses.
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Affiliation(s)
- Eli Sennesh
- Northeastern University, Boston, MA , United States.
| | | | - Dana Brooks
- Northeastern University, Boston, MA , United States
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5
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Examining the Continuity between Life and Mind: Is There a Continuity between Autopoietic Intentionality and Representationality? PHILOSOPHIES 2021. [DOI: 10.3390/philosophies6010018] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A weak version of the life-mind continuity thesis entails that every living system also has a basic mind (with a non-representational form of intentionality). The strong version entails that the same concepts that are sufficient to explain basic minds (with non-representational states) are also central to understanding non-basic minds (with representational states). We argue that recent work on the free energy principle supports the following claims with respect to the life-mind continuity thesis: (i) there is a strong continuity between life and mind; (ii) all living systems can be described as if they had representational states; (iii) the ’as-if representationality’ entailed by the free energy principle is central to understanding both basic forms of intentionality and intentionality in non-basic minds. In addition to this, we argue that the free energy principle also renders realism about computation and representation compatible with a strong life-mind continuity thesis (although the free energy principle does not entail computational and representational realism). In particular, we show how representationality proper can be grounded in ’as-if representationality’.
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6
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Hipólito I, Baltieri M, Friston K, Ramstead MJD. Embodied skillful performance: where the action is. SYNTHESE 2021; 199:4457-4481. [PMID: 34866668 PMCID: PMC8602225 DOI: 10.1007/s11229-020-02986-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/02/2020] [Indexed: 05/13/2023]
Abstract
When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory (OMCT), as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed-a motor command. This paper aims to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference-behavioral modeling frameworks that descend, but are distinct, from OMCT-and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference.
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Affiliation(s)
- Inês Hipólito
- Berlin School of Mind and Brain and Institut Für Philosophie Humboldt, Universität zu Berlin, Berlin, Germany
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Manuel Baltieri
- Lab for Neural Computation and Adaptation RIKEN Center for Brain Science Wako, Saitama, Japan
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Maxwell J. D. Ramstead
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC Canada
- Culture, Mind, and Brain Program, McGill University, Montreal, QC Canada
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7
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Da Costa L, Parr T, Sajid N, Veselic S, Neacsu V, Friston K. Active inference on discrete state-spaces: A synthesis. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2020; 99:102447. [PMID: 33343039 PMCID: PMC7732703 DOI: 10.1016/j.jmp.2020.102447] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/23/2020] [Accepted: 09/03/2020] [Indexed: 05/05/2023]
Abstract
Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associated process theory has grown to incorporate complex generative models, enabling simulation of a wide range of complex behaviours. Due to successive developments in active inference, it is often difficult to see how its underlying principle relates to process theories and practical implementation. In this paper, we try to bridge this gap by providing a complete mathematical synthesis of active inference on discrete state-space models. This technical summary provides an overview of the theory, derives neuronal dynamics from first principles and relates this dynamics to biological processes. Furthermore, this paper provides a fundamental building block needed to understand active inference for mixed generative models; allowing continuous sensations to inform discrete representations. This paper may be used as follows: to guide research towards outstanding challenges, a practical guide on how to implement active inference to simulate experimental behaviour, or a pointer towards various in-silico neurophysiological responses that may be used to make empirical predictions.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London, SW7 2RH, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
| | - Noor Sajid
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
| | - Sebastijan Veselic
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
| | - Victorita Neacsu
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom
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8
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Henriksen M. Variational Free Energy and Economics Optimizing With Biases and Bounded Rationality. Front Psychol 2020; 11:549187. [PMID: 33240146 PMCID: PMC7677574 DOI: 10.3389/fpsyg.2020.549187] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 09/25/2020] [Indexed: 11/13/2022] Open
Abstract
The purpose of this paper is to offer a new framework for understanding action, optimization, and choice when applied to economic theory more generally. By drawing upon the concept known as the variational free energy principle, the paper will explore how this principle can be used to temper rational choice theory by re-formulating how agents optimize. The approach will result in agent behavior that encompasses a wide range of so-called cognitive biases, as seen in the scientific literature of behavioral economics, but instead of using these biases as further indications of market inefficiencies or market failures, the paper will likewise attempt to show the limits to which these biases can inform or critique standard economic theory. The paper therefore offers up a “middle of the road” approach, in which the neoclassical agent is not quite as “rational” as rational choice theory assumes, but at the same time, not quite as irrational as behavioral economics would often have us believe.
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Affiliation(s)
- Morten Henriksen
- Ministry of Defence, Karup, Denmark.,AAU Business School, The Faculty of Social Sciences, Aalborg University, Aalborg, Denmark
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9
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Tschantz A, Seth AK, Buckley CL. Learning action-oriented models through active inference. PLoS Comput Biol 2020; 16:e1007805. [PMID: 32324758 PMCID: PMC7200021 DOI: 10.1371/journal.pcbi.1007805] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 05/05/2020] [Accepted: 03/19/2020] [Indexed: 11/29/2022] Open
Abstract
Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.
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Affiliation(s)
- Alexander Tschantz
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton, United Kingdom
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Anil K. Seth
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton, United Kingdom
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Canadian Institute for Advanced Research, Azrieli Programme on Brain, Mind, and Consciousness, Toronto, Ontario, Canada
| | - Christopher L. Buckley
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Evolutionary and Adaptive Systems Research Group, University of Sussex, Falmer, United Kingdom
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10
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Loued-Khenissi L, Preuschoff K. Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain. Front Artif Intell 2020; 3:5. [PMID: 33733125 PMCID: PMC7861235 DOI: 10.3389/frai.2020.00005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/03/2020] [Indexed: 12/02/2022] Open
Abstract
Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivation. Beyond reward, decisions are guided by belief, i.e., confidence-weighted expectations. Evidence challenging a belief evokes surprise, which signals a deviation from expectation (stimulus-bound surprise) but also provides an information gain. To support the theory that uncertainty minimization is an essential drive for the brain, we probe the neural trace of uncertainty-related decision variables, namely confidence, surprise, and information gain, in a discrete decision with a deterministic outcome. Confidence and surprise were elicited with a gambling task administered in a functional magnetic resonance imaging experiment, where agents start with a uniform probability distribution, transition to a non-uniform probabilistic state, and end in a fully certain state. After controlling for reward expectation, we find confidence, taken as the negative entropy of a trial, correlates with a response in the hippocampus and temporal lobe. Stimulus-bound surprise, taken as Shannon information, correlates with responses in the insula and striatum. In addition, we also find a neural response to a measure of information gain captured by a confidence error, a quantity we dub accuracy. BOLD responses to accuracy were found in the cerebellum and precuneus, after controlling for reward prediction errors and stimulus-bound surprise at the same time point. Our results suggest that, even absent an overt need for learning, the human brain expends energy on information gain and uncertainty minimization.
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Affiliation(s)
- Leyla Loued-Khenissi
- Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kerstin Preuschoff
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
- Geneva Finance Research Institute, University of Geneva, Geneva, Switzerland
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11
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Parr T, Friston KJ. Generalised free energy and active inference. BIOLOGICAL CYBERNETICS 2019; 113:495-513. [PMID: 31562544 PMCID: PMC6848054 DOI: 10.1007/s00422-019-00805-w] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 09/13/2019] [Indexed: 05/30/2023]
Abstract
Active inference is an approach to understanding behaviour that rests upon the idea that the brain uses an internal generative model to predict incoming sensory data. The fit between this model and data may be improved in two ways. The brain could optimise probabilistic beliefs about the variables in the generative model (i.e. perceptual inference). Alternatively, by acting on the world, it could change the sensory data, such that they are more consistent with the model. This implies a common objective function (variational free energy) for action and perception that scores the fit between an internal model and the world. We compare two free energy functionals for active inference in the framework of Markov decision processes. One of these is a functional of beliefs (i.e. probability distributions) about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy), priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to future observations, we equip the generative model with a prior over policies that ensure preferred (i.e. priors over) outcomes are realised. In other words, if we expect to encounter a particular kind of outcome, this lends plausibility to those policies for which this outcome is a consequence. In addition, this formulation ensures that selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional-that effectively treats future observations as hidden states-we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not.
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Affiliation(s)
- Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG UK
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG UK
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12
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Badcock PB, Friston KJ, Ramstead MJD, Ploeger A, Hohwy J. The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2019; 19:1319-1351. [PMID: 31115833 PMCID: PMC6861365 DOI: 10.3758/s13415-019-00721-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.
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Affiliation(s)
- Paul B Badcock
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Maxwell J D Ramstead
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
- Department of Philosophy, McGill University, Montreal, QC, Canada
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Annemie Ploeger
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Jakob Hohwy
- Cognition & Philosophy Lab, Monash University, Clayton, VIC, Australia
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13
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Abstract
A popular distinction in the human and animal learning literature is between deliberate (or willed) and habitual (or automatic) modes of control. Extensive evidence indicates that, after sufficient learning, living organisms develop behavioural habits that permit them saving computational resources. Furthermore, humans and other animals are able to transfer control from deliberate to habitual modes (and vice versa), trading off efficiently flexibility and parsimony - an ability that is currently unparalleled by artificial control systems. Here, we discuss a computational implementation of habit formation, and the transfer of control from deliberate to habitual modes (and vice versa) within Active Inference: a computational framework that merges aspects of cybernetic theory and of Bayesian inference. To model habit formation, we endow an Active Inference agent with a mechanism to "cache" (or memorize) policy probabilities from previous trials, and reuse them to skip - in part or in full - the inferential steps of deliberative processing. We exploit the fact that the relative quality of policies, conditioned upon hidden states, is constant over trials; provided that contingencies and prior preferences do not change. This means the only quantity that can change policy selection is the prior distribution over the initial state - where this prior is based upon the posterior beliefs from previous trials. Thus, an agent that caches the quality (or the probability) of policies can safely reuse cached values to save on cognitive and computational resources - unless contingencies change. Our simulations illustrate the computational benefits, but also the limits, of three caching schemes under Active Inference. They suggest that key aspects of habitual behaviour - such as perseveration - can be explained in terms of caching policy probabilities. Furthermore, they suggest that there may be many kinds (or stages) of habitual behaviour, each associated with a different caching scheme; for example, caching associated or not associated with contextual estimation. These schemes are more or less impervious to contextual and contingency changes.
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Affiliation(s)
- D Maisto
- Institute for High Performance Computing and Networking, National Research Council, Via P. Castellino, 111, Naples 80131, Italy
| | - K Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - G Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, Rome 00185, Italy
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14
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Piekarski M. Normativity of Predictions: A New Research Perspective. Front Psychol 2019; 10:1710. [PMID: 31396137 PMCID: PMC6664069 DOI: 10.3389/fpsyg.2019.01710] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 07/09/2019] [Indexed: 11/25/2022] Open
Affiliation(s)
- Michał Piekarski
- Institute of Philosophy, Cardinal Stefan Wyszyński University in Warsaw, Warsaw, Poland
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15
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Constant A, Ramstead MJD, Veissière SPL, Friston K. Regimes of Expectations: An Active Inference Model of Social Conformity and Human Decision Making. Front Psychol 2019; 10:679. [PMID: 30988668 PMCID: PMC6452780 DOI: 10.3389/fpsyg.2019.00679] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 03/11/2019] [Indexed: 01/06/2023] Open
Abstract
How do humans come to acquire shared expectations about how they ought to behave in distinct normalized social settings? This paper offers a normative framework to answer this question. We introduce the computational construct of 'deontic value' - based on active inference and Markov decision processes - to formalize conceptions of social conformity and human decision-making. Deontic value is an attribute of choices, behaviors, or action sequences that inherit directly from deontic cues in our econiche (e.g., red traffic lights); namely, cues that denote an obligatory social rule. Crucially, the prosocial aspect of deontic value rests upon a particular form of circular causality: deontic cues exist in the environment in virtue of the environment being modified by repeated actions, while action itself is contingent upon the deontic value of environmental cues. We argue that this construction of deontic cues enables the epistemic (i.e., information-seeking) and pragmatic (i.e., goal- seeking) values of any behavior to be 'cached' or 'outsourced' to the environment, where the environment effectively 'learns' about the behavior of its denizens. We describe the process whereby this particular aspect of value enables learning of habitual behavior over neurodevelopmental and transgenerational timescales.
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Affiliation(s)
- Axel Constant
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- Wellcome Trust Centre for Human Neuroimaging, University College London, London, United Kingdom
- Culture, Mind, and Brain Program, McGill University, Montreal, QC, Canada
| | - Maxwell J. D. Ramstead
- Wellcome Trust Centre for Human Neuroimaging, University College London, London, United Kingdom
- Culture, Mind, and Brain Program, McGill University, Montreal, QC, Canada
- Department of Philosophy, McGill University, Montreal, QC, Canada
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, QC, Canada
| | - Samuel P. L. Veissière
- Culture, Mind, and Brain Program, McGill University, Montreal, QC, Canada
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, QC, Canada
- Department of Anthropology, McGill University, Montreal, QC, Canada
| | - Karl Friston
- Wellcome Trust Centre for Human Neuroimaging, University College London, London, United Kingdom
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Abstract
How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, United Kingdom.
| | - Richard Rosch
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, United Kingdom.
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, United Kingdom.
| | - Cathy Price
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, United Kingdom.
| | - Howard Bowman
- Centre for Cognitive Neuroscience and Cognitive Systems and the School of Computing, University of Kent at Canterbury, Canterbury, Kent, CT2 7NF, United Kingdom; School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom.
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17
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Lowe R, Almér A, Billing E, Sandamirskaya Y, Balkenius C. Affective-associative two-process theory: a neurocomputational account of partial reinforcement extinction effects. BIOLOGICAL CYBERNETICS 2017; 111:365-388. [PMID: 28913644 PMCID: PMC5688222 DOI: 10.1007/s00422-017-0730-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 08/16/2017] [Indexed: 06/07/2023]
Abstract
The partial reinforcement extinction effect (PREE) is an experimentally established phenomenon: behavioural response to a given stimulus is more persistent when previously inconsistently rewarded than when consistently rewarded. This phenomenon is, however, controversial in animal/human learning theory. Contradictory findings exist regarding when the PREE occurs. One body of research has found a within-subjects PREE, while another has found a within-subjects reversed PREE (RPREE). These opposing findings constitute what is considered the most important problem of PREE for theoreticians to explain. Here, we provide a neurocomputational account of the PREE, which helps to reconcile these seemingly contradictory findings of within-subjects experimental conditions. The performance of our model demonstrates how omission expectancy, learned according to low probability reward, comes to control response choice following discontinuation of reward presentation (extinction). We find that a PREE will occur when multiple responses become controlled by omission expectation in extinction, but not when only one omission-mediated response is available. Our model exploits the affective states of reward acquisition and reward omission expectancy in order to differentially classify stimuli and differentially mediate response choice. We demonstrate that stimulus-response (retrospective) and stimulus-expectation-response (prospective) routes are required to provide a necessary and sufficient explanation of the PREE versus RPREE data and that Omission representation is key for explaining the nonlinear nature of extinction data.
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Affiliation(s)
- Robert Lowe
- Department of Applied IT, University of Gothenburg, Gothenburg, Sweden.
| | - Alexander Almér
- Department of Applied IT, University of Gothenburg, Gothenburg, Sweden
| | - Erik Billing
- Institutionen för informationsteknologi, Högskolan i Skövde, Skövde, Sweden
| | - Yulia Sandamirskaya
- Institute of Neuroinformatics, Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
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18
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Friston KJ, Lin M, Frith CD, Pezzulo G, Hobson JA, Ondobaka S. Active Inference, Curiosity and Insight. Neural Comput 2017; 29:2633-2683. [PMID: 28777724 DOI: 10.1162/neco_a_00999] [Citation(s) in RCA: 152] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a handful of observations, which are solicited through curious behavior. We use simulations of abstract rule learning and approximate Bayesian inference to show that minimizing (expected) variational free energy leads to active sampling of novel contingencies. This epistemic behavior closes explanatory gaps in generative models of the world, thereby reducing uncertainty and satisfying curiosity. We then move from epistemic learning to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries (i.e., invariances or rules) in their generative models. The ensuing Bayesian model reduction evinces mechanisms associated with sleep and has all the hallmarks of "aha" moments. This formulation moves toward a computational account of consciousness in the pre-Cartesian sense of sharable knowledge (i.e., con: "together"; scire: "to know").
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3BG, U.K.
| | - Marco Lin
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3BG, U.K.
| | - Christopher D Frith
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3BG, and Institute of Philosophy, School of Advanced Studies, University of London EC1E 7HU, U.K.
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, 7-00185 Rome, Italy
| | - J Allan Hobson
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3BG, U.K., and Division of Sleep Medicine, Harvard Medical School, Boston, MA 02215, U.S.A.
| | - Sasha Ondobaka
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London WC1N 3BG, U.K.
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19
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Friston KJ, Rosch R, Parr T, Price C, Bowman H. Deep temporal models and active inference. Neurosci Biobehav Rev 2017; 77:388-402. [PMID: 28416414 PMCID: PMC5461873 DOI: 10.1016/j.neubiorev.2017.04.009] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 04/11/2017] [Indexed: 11/02/2022]
Abstract
How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.
| | - Richard Rosch
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.
| | - Cathy Price
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.
| | - Howard Bowman
- Centre for Cognitive Neuroscience and Cognitive Systems and the School of Computing, University of Kent at Canterbury, Canterbury, Kent, CT2 7NF, UK; School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
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de Figueiredo RP, Bernardino A, Santos-Victor J, Araújo H. On the advantages of foveal mechanisms for active stereo systems in visual search tasks. Auton Robots 2017. [DOI: 10.1007/s10514-017-9617-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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21
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Stephan KE, Manjaly ZM, Mathys CD, Weber LAE, Paliwal S, Gard T, Tittgemeyer M, Fleming SM, Haker H, Seth AK, Petzschner FH. Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression. Front Hum Neurosci 2016; 10:550. [PMID: 27895566 PMCID: PMC5108808 DOI: 10.3389/fnhum.2016.00550] [Citation(s) in RCA: 213] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 10/14/2016] [Indexed: 01/13/2023] Open
Abstract
This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.
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Affiliation(s)
- Klaas E Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College LondonLondon, UK; Max Planck Institute for Metabolism ResearchCologne, Germany
| | - Zina M Manjaly
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Department of Neurology, Schulthess ClinicZurich, Switzerland
| | - Christoph D Mathys
- Wellcome Trust Centre for Neuroimaging, University College London London, UK
| | - Lilian A E Weber
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Saee Paliwal
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Tim Gard
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Center for Complementary and Integrative Medicine, University Hospital ZurichZurich, Switzerland
| | | | - Stephen M Fleming
- Wellcome Trust Centre for Neuroimaging, University College London London, UK
| | - Helene Haker
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
| | - Anil K Seth
- Sackler Centre for Consciousness Science, School of Engineering and Informatics, University of Sussex Brighton, UK
| | - Frederike H Petzschner
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland
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22
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Friston K, FitzGerald T, Rigoli F, Schwartenbeck P, O Doherty J, Pezzulo G. Active inference and learning. Neurosci Biobehav Rev 2016; 68:862-879. [PMID: 27375276 PMCID: PMC5167251 DOI: 10.1016/j.neubiorev.2016.06.022] [Citation(s) in RCA: 249] [Impact Index Per Article: 31.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Revised: 06/15/2016] [Accepted: 06/17/2016] [Indexed: 11/07/2022]
Abstract
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity.
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Affiliation(s)
- Karl Friston
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom.
| | - Thomas FitzGerald
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom; Max-PlanckUCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom.
| | - Francesco Rigoli
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom.
| | - Philipp Schwartenbeck
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom; Max-PlanckUCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom; Centre for Neurocognitive Research, University of Salzburg, Salzburg, Austria; Neuroscience Institute, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria.
| | - John O Doherty
- Caltech Brain Imaging Center, California Institute of Technology, Pasadena, USA.
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
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Abstract
Patients with ecstatic epileptic seizures report an altered consciousness, which they describe as a sense of heightened perception of themselves – they “feel very present” – and an increased vividness of sensory perceptions. Recently, the anterior insula has been proposed as the region where these seizures originate, based on the results of ictal nuclear imaging in three patients, the first induction of ecstatic auras by electrical stimulation, and the functional characteristics of the anterior insula in neuroimaging literature. Specifically, the anterior insula is thought to play a key role in integrating information from within the body, the external world, as well as the emotional states. In addition, the anterior insula is thought to convert this integrated information into successive global emotional moments, thus enabling both the construct of a sentient self as well as a mechanism for predictive coding. As part of the salience network, this region is also involved in switching from mind wandering toward attentional and executive processing. In this review, we will summarize previous patient reports and recap how insular functioning may be involved in the phenomenon of ecstatic seizures. Furthermore, we will relate these hypotheses to the results from research on meditation and effects of drug abuse.
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Krichmar JL, Röhrbein F. Value and reward based learning in neurorobots. Front Neurorobot 2013; 7:13. [PMID: 24062683 PMCID: PMC3772325 DOI: 10.3389/fnbot.2013.00013] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 08/25/2013] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jeffrey L Krichmar
- Department of Cognitive Sciences, Department of Computer Science, University of California Irvine, CA, USA
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Picard F. State of belief, subjective certainty and bliss as a product of cortical dysfunction. Cortex 2013; 49:2494-500. [PMID: 23415878 DOI: 10.1016/j.cortex.2013.01.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2012] [Revised: 12/18/2012] [Accepted: 01/01/2013] [Indexed: 10/27/2022]
Abstract
INTRODUCTION Ecstatic seizures are focal epileptic seizures which are fascinating from a phenotypical point of view as they include intense positive affect, feelings of heightened self-awareness and enhanced well-being. They have been previously suggested to arise in the anterior insular cortex, although strong arguments are still lacking. METHODS We describe the cases of two new patients with ecstatic seizures. Their evaluation included a careful history, encouraging the patient to provide significant details about their ictal symptoms in order to better understand the origin of the sense of bliss and support the hypothesis of an insular involvement according to the current stage of knowledge. Ictal electroencephalographic and blood flow studies complemented these data in one patient. RESULTS The comprehensive description of the ictal ecstatic symptoms by the two patients has brought out an unfamiliar sense of absence of doubt which was at the basis of a feeling of meaningfulness and certainty. The ictal single-photon emission computed tomography (SPECT) showed an increased blood flow maximal at the junction of the right dorsal mid-insula and the central operculum. CONCLUSIONS The unveiling of an ictal sense of certainty during ecstatic seizures might imply, in the light of current knowledge, a defect in the system processing prediction errors within the framework of generalized predictive coding mechanisms of the brain. Accumulative evidence has recently highlighted a crucial role of the anterior insular cortex in this system, particularly in the detection of mismatch/conflict between prediction state and outcome. Abnormal activity related to epileptic seizure in a structure prevents its normal activity: in the anterior insula, it could prevent the detection of prediction errors, and thereby prevent the feeling of ambiguity (and the associated negative emotional component), leading to a blissful state which could be close to the deeper states of meditation.
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Affiliation(s)
- Fabienne Picard
- Department of Neurology, University Hospital and Medical School of Geneva, Switzerland.
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