Action and behavior: a free-energy formulation

Predictive Processing During Cue-Outcome Associative Learning in Autistic Children

PURPOSE

Predictive coding theories posit that autism is characterized by an over-adjustment to prediction errors, resulting in frequent updates of prior beliefs. Atypical weighting of prediction errors is generally considered to negatively impact the construction of stable models of the world, but may also yield beneficial effects. In a novel associative learning paradigm, we investigated whether unexpected events trigger faster learning updates in favour of subtle but fully predictive cues in autistic children compared to their non-autistic counterparts. We also explored the relationship between children's language proficiency and their predictive performances.

METHODS

Anticipatory fixations and explicit predictions were recorded during three associative learning tasks with deterministic or probabilistic contingencies. One of the probabilistic tasks was designed so that a fully predictive but subtle cue was overshadowed by a less predictive salient one.

RESULTS

Both autistic and non-autistic children based their learning on the salient cue, and, contrary to our predictions, showed no signs of updating in favour of the subtle cue. While both groups demonstrated associative learning, autistic children made less accurate explicit predictions than their non-autistic peers in all tasks. Explicit prediction performances were positively correlated with language proficiency in non-autistic children, but no such correlation was observed in autistic children.

CONCLUSION

These results suggest no over-adjustment to prediction errors in autistic children and highlight the need to control for general performance in cue-outcome associative learning in predictive processing studies. Further research is needed to explore the nature of the relationship between predictive processing and language development in autism.

Understanding music and aging through the lens of Bayesian inference

Bayesian inference has recently gained momentum in explaining music perception and aging. A fundamental mechanism underlying Bayesian inference is the notion of prediction. This framework could explain how predictions pertaining to musical (melodic, rhythmic, harmonic) structures engender action, emotion, and learning, expanding related concepts of music research, such as musical expectancies, groove, pleasure, and tension. Moreover, a Bayesian perspective of music perception may shed new insights on the beneficial effects of music in aging. Aging could be framed as an optimization process of Bayesian inference. As predictive inferences refine over time, the reliance on consolidated priors increases, while the updating of prior models through Bayesian inference attenuates. This may affect the ability of older adults to estimate uncertainties in their environment, limiting their cognitive and behavioral repertoire. With Bayesian inference as an overarching framework, this review synthesizes the literature on predictive inferences in music and aging, and details how music could be a promising tool in preventive and rehabilitative interventions for older adults through the lens of Bayesian inference.

Forgetting ourselves in flow: an active inference account of flow states and how we experience ourselves within them

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.

Synergizing habits and goals with variational Bayes

Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed (flexible but slow). While these two types of behaviors are typically considered to be managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian intention variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision making.

Suppression and omission effects in auditory predictive processing-Two of the same?

Recent theories describe perception as an inferential process based on internal predictive models that are adjusted by prediction violations (prediction error). Two different modulations of the auditory N1 event-related brain potential component are often discussed as an expression of auditory predictive processing. The sound-related N1 component is attenuated for self-generated sounds compared to the N1 elicited by externally generated sounds (N1 suppression). An omission-related component in the N1 time-range is elicited when the self-generated sounds are occasionally omitted (omission N1). Both phenomena were explained by action-related forward modelling, which takes place when the sensory input is predictable: prediction error signals are reduced when predicted sensory input is presented (N1 suppression) and elicited when predicted sensory input is omitted (omission N1). This common theoretical account is appealing but has not yet been directly tested. We manipulated the predictability of a sound in a self-generation paradigm in which, in two conditions, either 80% or 50% of the button presses did generate a sound, inducing a strong or a weak expectation for the occurrence of the sound. Consistent with the forward modelling account, an omission N1 was observed in the 80% but not in the 50% condition. However, N1 suppression was highly similar in both conditions. Thus, our results demonstrate a clear effect of predictability for the omission N1 but not for the N1 suppression. These results imply that the two phenomena rely (at least in part) on different mechanisms and challenge prediction related accounts of N1 suppression.

Active Inference With Empathy Mechanism for Socially Behaved Artificial Agents in Diverse Situations

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.

In Case of Doubt, One Follows One's Self: The Implicit Guidance of the Embodied Self-Avatar

The sense of embodiment in virtual reality (VR) is commonly understood as the subjective experience that one's physical body is substituted by a virtual counterpart, and is typically achieved when the avatar's body, seen from a first-person view, moves like one's physical body. Embodiment can also be experienced in other circumstances (e.g., in third-person view) or with imprecise or distorted visuo-motor coupling. It was moreover observed, in various cases of small or progressive temporal and spatial manipulations of avatars' movements, that participants may spontaneously follow the movement shown by the avatar. The present work investigates whether, in some specific contexts, participants would follow what their avatar does even when large movement discrepancies occur, thereby extending the scope of understanding of the self-avatar follower effect beyond subtle changes of motion or speed manipulations. We conducted an experimental study in which we introduced uncertainty about which movement to perform at specific times and analyzed participants' movements and subjective feedback after their avatar showed them an incorrect movement. Results show that, when in doubt, participants were influenced by their avatar's movements, leading them to perform that particular error twice more often than normal. Importantly, results of the embodiment score indicate that participants experienced a dissociation with their avatar at those times. Overall, these observations not only demonstrate the possibility of provoking situations in which participants follow the guidance of their avatar for large motor distortions, despite their awareness about the avatar movement disruption and on the possible influence it had on their choice, and, importantly, exemplify how the cognitive mechanism of embodiment is deeply rooted in the necessity of having a body.

Understanding Functional Neurological Disorder: Recent Insights and Diagnostic Challenges

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder's multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.

A computationally informed distinction of interoception and exteroception

While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.

The police hunch: the Bayesian brain, active inference, and the free energy principle in action

In the realm of law enforcement, the "police hunch" has long been a mysterious but crucial aspect of decision-making. Drawing on the developing framework of Active Inference from cognitive science, this theoretical article examines the genesis, mechanics, and implications of the police hunch. It argues that hunches - often vital in high-stakes situations - should not be described as mere intuitions, but as intricate products of our mind's generative models. These models, shaped by observations of the social world and assimilated and enacted through active inference, seek to reduce surprise and make hunches an indispensable tool for officers, in exactly the same way that hypotheses are indispensable for scientists. However, the predictive validity of hunches is influenced by a range of factors, including experience and bias, thus warranting critical examination of their reliability. This article not only explores the formation of police hunches but also provides practical insights for officers and researchers on how to harness the power of active inference to fully understand policing decisions and subsequently explore new avenues for future research.

Active inference as a theory of sentient behavior

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.

The importance of highlighting the role of the self in hypnotherapy and hypnosis

The role of the patient in hypnotherapy can be underestimated by both the therapist and the patient. This is likely due to the focus the hypnosis literature has had on the role played by the hypnotist/therapist and less on the phenomenological control (control over subjective experience) applied by the patient. Whilst early approaches to hypnosis and hypnotherapy included concepts such as autosuggestion and self-hypnosis, the role of the self has been largely overlooked. Here we aim to highlight the importance of the self in hypnotherapy and hypnosis by considering the concept of self-hypnosis and how it relates to hetero-hypnosis. We will show that: 1) historically the self was an important component of the concept of hypnosis; 2) extant theories emphasise the role of the self in hypnosis; 3) self-hypnosis is largely indistinguishable from hetero-hypnosis; 4) self-hypnosis is as effective as hetero-hypnosis. We also argue that highlighting the role of the self in hypnotherapy and hypnosis could increase feelings of self-efficacy, especially given that it can be considered a skill that can be advanced and implies self-control and not "mind-control". Highlighting the role of phenomenological control by the patient could also increase the uptake of hypnotherapy as treatment for various disorders.

Order and change in art: towards an active inference account of aesthetic experience

How to account for the power that art holds over us? Why do artworks touch us deeply, consoling, transforming or invigorating us in the process? In this paper, we argue that an answer to this question might emerge from a fecund framework in cognitive science known as predictive processing (a.k.a. active inference). We unpack how this approach connects sense-making and aesthetic experiences through the idea of an 'epistemic arc', consisting of three parts (curiosity, epistemic action and aha experiences), which we cast as aspects of active inference. We then show how epistemic arcs are built and sustained by artworks to provide us with those satisfying experiences that we tend to call 'aesthetic'. Next, we defuse two key objections to this approach; namely, that it places undue emphasis on the cognitive component of our aesthetic encounters-at the expense of affective aspects-and on closure and uncertainty minimization (order)-at the expense of openness and lingering uncertainty (change). We show that the approach offers crucial resources to account for the open-ended, free and playful behaviour inherent in aesthetic experiences. The upshot is a promising but deflationary approach, both philosophically informed and psychologically sound, that opens new empirical avenues for understanding our aesthetic encounters. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Aesthetics and predictive processing: grounds and prospects of a fruitful encounter

In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in the arts and aesthetics from a variety of perspectives and cognitive scientists studying the mind and brain within the predictive processing (PP) framework. This convergence has so far proven fruitful for both sides: while PP is increasingly adopted as a framework for understanding aesthetic phenomena, the arts and aesthetics, examined under the lens of PP, are starting to be seen as important windows into our mental functioning. The result is a vast and fast-growing research programme that promises to deliver important insights into our aesthetic encounters as well as a wide range of psychological phenomena of general interest. Here, we present this developing research programme, describing its grounds and highlighting its prospects. We start by clarifying how the study of the arts and aesthetics encounters the PP picture of mental functioning (§1). We then go on to outline the prospects of this encounter for the fields involved: philosophy and history of art (§2), psychology of aesthetics and neuroaesthetics (§3) and psychology and neuroscience more generally (§4). The upshot is an ambitious but well-defined framework within which aesthetics and cognitive science can partner up to illuminate crucial aspects of the human mind. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Bistable perception, precision and neuromodulation

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.

Does Active Inference Provide a Comprehensive Theory of Placebo Analgesia?

Placebo interventions generate mismatches between expected pain and sensory signals from which pain states are inferred. Because we lack direct access to bodily states, we can only infer whether nociceptive activity indicates tissue damage or results from noise in sensory channels. Predictive processing models propose to make optimal inferences using prior knowledge given noisy sensory data. However, these models do not provide a satisfactory explanation of how pain relief expectations are translated into physiological manifestations of placebo responses. Furthermore, they do not account for individual differences in the ability to endogenously regulate nociceptive activity in predicting placebo analgesia. The brain not only passively integrates prior pain expectations with nociceptive activity to infer pain states (perceptual inference) but also initiates various types of actions to ensure that sensory data are consistent with prior pain expectations (active inference). We argue that depending on whether the brain interprets conflicting sensory data (prediction errors) as a signal to learn from or noise to be attenuated, the brain initiates opposing types of action to facilitate learning from sensory data or, conversely, to enhance the biasing influence of prior pain expectations on pain perception. Furthermore, we discuss the role of stress, anxiety, and unpredictability of pain in influencing the weighting of prior pain expectations and sensory data and how they relate to the individual ability to regulate nociceptive activity (endogenous pain modulation). Finally, we provide suggestions for future studies to test the implications of the active inference model of placebo analgesia.

Deep kinematic inference affords efficient and scalable control of bodily movements

Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.

Path integrals, particular kinds, and strange things

This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defined by a particular partition, in which internal states are individuated from external states by active and sensory blanket states. The variational principle at hand allows one to interpret internal dynamics-of certain kinds of particles-as inferring external states that are hidden behind blanket states. We consider different kinds of particles, and to what extent they can be imbued with an elementary form of inference or sentience. Specifically, we consider the distinction between dissipative and conservative particles, inert and active particles and, finally, ordinary and strange particles. Strange particles can be described as inferring their own actions, endowing them with apparent autonomy or agency. In short-of the kinds of particles afforded by a particular partition-strange kinds may be apt for describing sentient behaviour.

An Introduction to Predictive Processing Models of Perception and Decision-Making

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.

Fetal brain activity and the free energy principle

OBJECTIVES

To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence.

METHODS

In this observational study, we used a four-dimensional ultrasound technique obtained to collect images of fetal faces from pregnancies at 27-37 weeks of gestation, between February and December 2021. We developed an artificial intelligence classifier that recognizes fetal facial expressions, which are thought to relate to fetal brain activity. We then applied the classifier to video files of facial images to generate each expression category's probabilities. We calculated the chaotic dimensions from the probability lists, and we created and investigated the free energy principle's mathematical model that was assumed to be linked to the chaotic dimension. We used a Mann-Whitney test, linear regression test, and one-way analysis of variance for statistical analysis.

RESULTS

The chaotic dimension revealed that the fetus had dense and sparse states of brain activity, which fluctuated at a statistically significant level. The chaotic dimension and free energy were larger in the sparse state than in the dense state.

CONCLUSIONS

The fluctuating free energy suggests consciousness seemed to exist in the fetus after 27 weeks.

Markov chains as a proxy for the predictive memory representations underlying mismatch negativity

Events not conforming to a regularity inherent to a sequence of events elicit prediction error signals of the brain such as the Mismatch Negativity (MMN) and impair behavioral task performance. Events conforming to a regularity lead to attenuation of brain activity such as stimulus-specific adaptation (SSA) and behavioral benefits. Such findings are usually explained by theories stating that the information processing system predicts the forthcoming event of the sequence via detected sequential regularities. A mathematical model that is widely used to describe, to analyze and to generate event sequences are Markov chains: They contain a set of possible events and a set of probabilities for transitions between these events (transition matrix) that allow to predict the next event on the basis of the current event and the transition probabilities. The accuracy of such a prediction depends on the distribution of the transition probabilities. We argue that Markov chains also have useful applications when studying cognitive brain functions. The transition matrix can be regarded as a proxy for generative memory representations that the brain uses to predict the next event. We assume that detected regularities in a sequence of events correspond to (a subset of) the entries in the transition matrix. We apply this idea to the Mismatch Negativity (MMN) research and examine three types of MMN paradigms: classical oddball paradigms emphasizing sound probabilities, between-sound regularity paradigms manipulating transition probabilities between adjacent sounds, and action-sound coupling paradigms in which sounds are associated with actions and their intended effects. We show that the Markovian view on MMN yields theoretically relevant insights into the brain processes underlying MMN and stimulates experimental designs to study the brain's processing of event sequences.

Adversarial inference: predictive minds in the attention economy

What is it about our current digital technologies that seemingly makes it difficult for users to attend to what matters to them? According to the dominant narrative in the literature on the "attention economy," a user's lack of attention is due to the large amounts of information available in their everyday environments. I will argue that information-abundance fails to account for some of the central manifestations of distraction, such as sudden urges to check a particular information-source in the absence of perceptual information. I will use active inference, and in particular models of action selection based on the minimization of expected free energy, to develop an alternative answer to the question about what makes it difficult to attend. Besides obvious adversarial forms of inference, in which algorithms build up models of users in order to keep them scrolling, I will show that active inference provides the tools to identify a number of problematic structural features of current digital technologies: they contain limitless sources of novelty, they can be navigated by very simple and effortless motor movements, and they offer their action possibilities everywhere and anytime independent of place or context. Moreover, recent models of motivated control show an intricate interplay between motivation and control that can explain sudden transitions in motivational state and the consequent alteration of the salience of actions. I conclude, therefore, that the challenges users encounter when engaging with digital technologies are less about information overload or inviting content, but more about the continuous availability of easily available possibilities for action.

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

Complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behaviour science, evolutionary developmental biology and the field of machine intelligence all seek to understand the scaling of biological cognition: what enables individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence with large-scale goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioural intelligence by scaling up homeostatic competencies of cells in metabolic space. In this article, we created a minimal in silico system (two-dimensional neural cellular automata) and tested the hypothesis that evolutionary dynamics are sufficient for low-level setpoints of metabolic homeostasis in individual cells to scale up to tissue-level emergent behaviour. Our system showed the evolution of the much more complex setpoints of cell collectives (tissues) that solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French flag problem in developmental biology). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve the target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover, we observed an unexpected behaviour of sudden remodelling long after the system stabilizes. We tested this prediction in a biological system-regenerating planaria-and observed a very similar phenomenon. We propose that this system is a first step towards a quantitative understanding of how evolution scales minimal goal-directed behaviour (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.

The integration of action-oriented multisensory information from target and limb within the movement planning and execution

The planning and execution of a grasping or reaching movement toward targets we sense with the other hand requires integrating multiple sources of sensory information about the limb performing the movement and the target of the action. In the last two decades, several sensory and motor control theories have thoroughly described how this multisensory-motor integration process occurs. However, even though these theories were very influential in their respective field, they lack a clear, unified vision of how target-related and movement-related multisensory information integrates within the action planning and execution phases. This brief review aims to summarize the most influential theories in multisensory integration and sensory-motor control by underscoring their critical points and hidden connections, providing new ideas on the multisensory-motor integration process. Throughout the review, I wll propose an alternative view of how the multisensory integration process unfolds along the action planning and execution and I will make several connections with the existent multisensory-motor control theories.

Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology

A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

Really radical?

I enjoyed reading this compelling account of Conviction Narrative Theory (CNT). As a theoretical neurobiologist, I recognised - and applauded - the tenets of CNT. My commentary asks whether its claims could be installed into a Bayesian mechanics of decision-making, in a way that would enable theoreticians to model, reproduce and predict decision-making.

Intertwining the social and the cognitive loops: socially enactive cognition for human-compatible interactive systems

It is increasingly important for technical systems to be able to interact flexibly, robustly and fluently with humans in real-world scenarios. However, while current AI systems excel at narrow task competencies, they lack crucial interaction abilities for the adaptive and co-constructed social interactions that humans engage in. We argue that a possible avenue to tackle the corresponding computational modelling challenges is to embrace interactive theories of social understanding in humans. We propose the notion of socially enactive cognitive systems that do not rely solely on abstract and (quasi-)complete internal models for separate social perception, reasoning and action. By contrast, socially enactive cognitive agents are supposed to enable a close interlinking of the enactive socio-cognitive processing loops within each agent, and the social-communicative loop between them. We discuss theoretical foundations of this view, identify principles and requirements for according computational approaches, and highlight three examples of our own research that showcase the interaction abilities achievable in this way. This article is part of a discussion meeting issue 'Face2face: advancing the science of social interaction'.

Functional Gait Disorders: Clinical presentations, Phenotypes and Implications for treatment

BACKGROUND

Functional Gait Disorders (FGD) are a common presentation of motor-Functional Neurological Disorders (motor-FND) that affect walking ability.

AIM

To provide a narrative review of the current literature on FGD.

METHODS

A narrative overview of published literature was undertaken, based on a systematic search of relevant databases, authoritative texts and citation tracking.

RESULTS

FGD is multidimensional and disabling, with numerous phenotypes described in the literature, including 'knee buckling,' 'astasia-abasia' and 'excessive slowness.' Motor symptoms such as weakness or tremor, and non-motor symptoms, such as pain and fatigue may contribute to the disability and distress in FGD. Phenotypic features and clinical signs are seen in FGD that demonstrate inconsistency and incongruity with structural disease. A limited number of treatment studies have specifically focussed on FGD, however, reporting of outcomes from motor-FND cohorts has demonstrated short and long-term improvements in walking ability through multidisciplinary rehabilitation.

CONCLUSIONS

The relative contribution of motor and non-motor symptoms in FGD remains unknown, but it is likely that non-motor symptoms increase the illness burden and should be considered during assessment and treatment. Recommended treatment for FGD involves multidisciplinary rehabilitation, but optimum treatment elements are yet to be determined.

How the conception of control influences our understanding of actions

Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.

From fear of falling to choking under pressure: A predictive processing perspective of disrupted motor control under anxiety

Under the Predictive Processing Framework, perception is guided by internal models that map the probabilistic relationship between sensory states and their causes. Predictive processing has contributed to a new understanding of both emotional states and motor control but is yet to be fully applied to their interaction during the breakdown of motor movements under heightened anxiety or threat. We bring together literature on anxiety and motor control to propose that predictive processing provides a unifying principle for understanding motor breakdowns as a disruption to the neuromodulatory control mechanisms that regulate the interactions of top-down predictions and bottom-up sensory signals. We illustrate this account using examples from disrupted balance and gait in populations who are anxious/fearful of falling, as well as 'choking' in elite sport. This approach can explain both rigid and inflexible movement strategies, as well as highly variable and imprecise action and conscious movement processing, and may also unite the apparently opposing self-focus and distraction approaches to choking. We generate predictions to guide future work and propose practical recommendations.

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures

Within predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning).

Turn-Taking Mechanisms in Imitative Interaction: Robotic Social Interaction Based on the Free Energy Principle

This study explains how the leader-follower relationship and turn-taking could develop in a dyadic imitative interaction by conducting robotic simulation experiments based on the free energy principle. Our prior study showed that introducing a parameter during the model training phase can determine leader and follower roles for subsequent imitative interactions. The parameter is defined as w, the so-called meta-prior, and is a weighting factor used to regulate the complexity term versus the accuracy term when minimizing the free energy. This can be read as sensory attenuation, in which the robot's prior beliefs about action are less sensitive to sensory evidence. The current extended study examines the possibility that the leader-follower relationship shifts depending on changes in w during the interaction phase. We identified a phase space structure with three distinct types of behavioral coordination using comprehensive simulation experiments with sweeps of w of both robots during the interaction. Ignoring behavior in which the robots follow their own intention was observed in the region in which both ws were set to large values. One robot leading, followed by the other robot was observed when one w was set larger and the other was set smaller. Spontaneous, random turn-taking between the leader and the follower was observed when both ws were set at smaller or intermediate values. Finally, we examined a case of slowly oscillating w in anti-phase between the two agents during the interaction. The simulation experiment resulted in turn-taking in which the leader-follower relationship switched during determined sequences, accompanied by periodic shifts of ws. An analysis using transfer entropy found that the direction of information flow between the two agents also shifted along with turn-taking. Herein, we discuss qualitative differences between random/spontaneous turn-taking and agreed-upon sequential turn-taking by reviewing both synthetic and empirical studies.

Position representations of moving objects align with real-time position in the early visual response

When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.

Skilled motor control of an inverted pendulum implies low entropy of states but high entropy of actions

The mastery of skills, such as balancing an inverted pendulum, implies a very accurate control of movements to achieve the task goals. Traditional accounts of skilled action control that focus on either routinization or perceptual control make opposite predictions about the ways we achieve mastery. The notion of routinization emphasizes the decrease of the variance of our actions, whereas the notion of perceptual control emphasizes the decrease of the variance of the states we visit, but not of the actions we execute. Here, we studied how participants managed control tasks of varying levels of difficulty, which consisted of controlling inverted pendulums of different lengths. We used information-theoretic measures to compare the predictions of alternative accounts that focus on routinization and perceptual control, respectively. Our results indicate that the successful performance of the control task strongly correlates with the decrease of state variability and the increase of action variability. As postulated by perceptual control theory, the mastery of skilled pendulum control consists in achieving stable control of goals by flexible means.

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

Flexible intentions: An Active Inference theory

We present a normative computational theory of how the brain may support visually-guided goal-directed actions in dynamically changing environments. It extends the Active Inference theory of cortical processing according to which the brain maintains beliefs over the environmental state, and motor control signals try to fulfill the corresponding sensory predictions. We propose that the neural circuitry in the Posterior Parietal Cortex (PPC) compute flexible intentions-or motor plans from a belief over targets-to dynamically generate goal-directed actions, and we develop a computational formalization of this process. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC might putatively host its core intention mechanism. More broadly, the study provides a normative computational basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.

Adaptive discrimination between harmful and harmless antigens in the immune system by predictive coding

The immune system discriminates between harmful and harmless antigens based on past experiences; however, the underlying mechanism is largely unknown. From the viewpoint of machine learning, the learning system predicts the observation and updates the prediction based on prediction error, a process known as "predictive coding." Here, we modeled the population dynamics of T cells by adopting the concept of predictive coding; conventional and regulatory T cells predict the antigen concentration and excessive immune response, respectively. Their prediction error signals, possibly via cytokines, induce their differentiation to memory T cells. Through numerical simulations, we found that the immune system identifies antigen risks depending on the concentration and input rapidness of the antigen. Further, our model reproduced history-dependent discrimination, as in allergy onset and subsequent therapy. Taken together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens.

Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs

Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in aberrant predictive processing, as schizophrenic patients show relatively less attenuated neuronal activity to self-produced tones, than healthy controls. Here, we tested the hypothesis that this aberrant predictive mechanism would manifest itself in the temporal irregularity of neuronal signals. For that purpose, we here introduce an event-related potential (ERP) study model analysis that consists of an EEG real-time model equation, eeg(t) and a frequency Laplace transformed Transfer Function (TF) equation, eeg(s). Combining circuit analysis with control and cable theory, we estimate the temporal model representations of auditory ERPs to reveal neural mechanisms that make predictions about self-generated sensations. We use data from 49 schizophrenic patients (SZ) and 32 healthy control (HC) subjects in an auditory ‘prediction’ paradigm; i.e., who either pressed a button to deliver a sound tone (epoch a), or just heard the tone without button press (epoch b). Our results show significantly higher degrees of temporal irregularity or imprecision between different trials of the ERP from the Cz electrode (N100, P200) in SZ compared to HC (Levene’s test, p < 0.0001) as indexed by altered latency, lower similarity/correlation of single trial time courses (using dynamic time warping), and longer settling times to reach steady state in the intertrial interval. Using machine learning, SZ vs HC could be highly accurately classified (92%) based on the temporal parameters of their ERPs’ TF models, using as features the poles of the TF rational functions. Together, our findings show temporal irregularity or imprecision between single trials to be abnormally increased in SZ. This may indicate a general impairment of SZ, related to precisely predicting the sensory consequences of one’s actions.

Active inference, morphogenesis, and computational psychiatry

Active inference is a leading theory in neuroscience that provides a simple and neuro-biologically plausible account of how action and perception are coupled in producing (Bayes) optimal behavior; and has been recently used to explain a variety of psychopathological conditions. In parallel, morphogenesis has been described as the behavior of a (non-neural) cellular collective intelligence solving problems in anatomical morphospace. In this article, we establish a link between the domains of cell biology and neuroscience, by analyzing disorders of morphogenesis as disorders of (active) inference. The aim of this article is three-fold. We want to: (i) reveal a connection between disorders of morphogenesis and disorders of active inference as apparent in psychopathological conditions; (ii) show how disorders of morphogenesis can be simulated using active inference; (iii) suggest that active inference can shed light on developmental defects or aberrant morphogenetic processes, seen as disorders of information processing, and perhaps suggesting novel intervention and repair strategies. We present four simulations illustrating application of these ideas to cellular behavior during morphogenesis. Three of the simulations show that the same forms of aberrant active inference (e.g., deficits of sensory attenuation and low sensory precision) that have been used to explain psychopathological conditions (e.g., schizophrenia and autism) also produce familiar disorders of development and morphogenesis when implemented at the level of the collective behavior of a group of cells. The fourth simulation involves two cells with too high precision, in which we show that the reduction of concentration signaling and sensitivity to the signals of other cells treats the development defect. Finally, we present the results of an experimental test of one of the model's predictions in early Xenopus laevis embryos: thioridazine (a dopamine antagonist that may reduce sensory precision in biological systems) induced developmental (anatomical) defects as predicted. The use of conceptual and empirical tools from neuroscience to understand the morphogenetic behavior of pre-neural agents offers the possibility of new approaches in regenerative medicine and evolutionary developmental biology.

Impact of Personalized Avatars and Motion Synchrony on Embodiment and Users’ Subjective Experience: Empirical Study

Background

Embodiment through a virtual avatar is a key element for people to feel that they are in the virtual world.

Objective

This study aimed to elucidate the interaction between 2 methods of eliciting embodiment through a virtual avatar: motion synchronization and appearance similarity between a human and avatar, to understand embodiment (agency, body ownership, and self-location) and subjective experience (presence, simulator sickness, and emotion) in virtual reality.

Methods

Using a full-body motion capture system, 24 participants experienced their virtual avatars with a 3D-scanned face and size-matched body from a first-person perspective. This study used a 2 (motion; sync and async) × 2 (appearance; personalized and generic) within-subject design.

Results

The results indicated that agency and body ownership increased when motion and appearance were matched, whereas self-location, presence, and emotion were affected by motion only. Interestingly, if the avatar’s appearance was similar to the participants (personalized avatar), they formed an agency toward the avatar’s motion that was not performed by themselves.

Conclusions

Our findings would be applicable in the field of behavioral therapy, rehabilitation, and entertainment applications, by eliciting higher agency with a personalized avatar.

Théories du complot et COVID-19 : comment naissent les croyances complotistes ?

La pandémie COVID-19 causée par le nouveau coronavirus SARS-CoV-2 a provoqué une crise mondiale sans précédent, associée à une prolifération de théories du complot. La prévalence de ces croyances complotistes a participé à affaiblir la crédibilité des mesures gouvernementales de santé publique, limiter l'accès des citoyens aux sources d’information fiables, et perturber la réponse des systèmes de santé face à la crise sanitaire. Plusieurs hypothèses ont été proposées en psychologie et en sciences sociales pour comprendre la genèse de ces croyances dans des contextes épidémiques, associant l’implication conjointe de facteurs psychologiques (comme la volonté de préserver sa sécurité, de maintenir une image de soi positive, ou encore de renforcer son rôle social) et sociodémographiques (comme les caractéristiques générationnelles, socioculturelles et politiques des individus). Toutefois, les découvertes récentes en sciences cognitives à propos des mécanismes élémentaires de traitement de l'information offrent un nouvel éclairage sur la génération des croyances conspirationnistes à travers les époques et les cultures. Dans cet article, nous proposons une définition de la théorie du complot et une classification des croyances complotistes pendant la pandémie de COVID-19. Nous montrons comment les mécanismes de génération et de mise à jour des croyances permettent d'expliquer la genèse des théories du complot, et nous proposons plusieurs hypothèses étayées par les recherches contemporaines en sciences cognitives et en sciences sociales à propos des théories du complot.

Intention-based predictive information modulates auditory deviance processing

The human brain is highly responsive to (deviant) sounds violating an auditory regularity. Respective brain responses are usually investigated in situations when the sounds were produced by the experimenter. Acknowledging that humans also actively produce sounds, the present event-related potential study tested for differences in the brain responses to deviants that were produced by the listeners by pressing one of two buttons. In one condition, deviants were unpredictable with respect to the button-sound association. In another condition, deviants were predictable with high validity yielding correctly predicted deviants and incorrectly predicted (mispredicted) deviants. Temporal principal component analysis revealed deviant-specific N1 enhancement, mismatch negativity (MMN) and P3a. N1 enhancements were highly similar for each deviant type, indicating that the underlying neural mechanism is not affected by intention-based expectation about the self-produced forthcoming sound. The MMN was abolished for predictable deviants, suggesting that the intention-based prediction for a deviant can overwrite the prediction derived from the auditory regularity (predicting a standard). The P3a was present for each deviant type but was largest for mispredicted deviants. It is argued that the processes underlying P3a not only evaluate the deviant with respect to the fact that it violates an auditory regularity but also with respect to the intended sensorial effect of an action. Overall, our results specify current theories of auditory predictive processing, as they reveal that intention-based predictions exert different effects on different deviance-specific brain responses.

[Multidimensional and computational theory of mood]

What is mood? Despite its crucial place in psychiatric nosography and cognitive science, it is still difficult to delimit its conceptual ground. The distinction between emotion and mood is ambiguous: mood is often presented as an affective state that is more prolonged and less intense than emotion, or as an affective polarity distinguishing high and low mood swinging around a baseline. However, these definitions do not match the clinical reality of mood disorders such as unipolar depression and bipolar disorder, and do not allow us to understand the effect of mood on behaviour, perception and cognition. In this paper, we propose a multidimensional and computational theory of mood inspired by contemporary hypotheses in theoretical neuroscience and philosophy of emotion. After suggesting an operational distinction between emotion and mood, we show how a succession of emotions can cumulatively generate congruent mood over time, making mood an emerging state from emotion. We then present how mood determines mental and behavioral states when interacting with the environment, constituting a dispositional state of emotion, perception, belief, and action. Using this theoretical framework, we propose a computational representation of the emerging and dispositional dimensions of mood by formalizing mood as a layer of third-order Bayesian beliefs encoding the precision of emotion, and regulated by prediction errors associated with interoceptive predictions. Finally, we show how this theoretical framework sheds light on the processes involved in mood disorders, the emergence of mood congruent beliefs, or the mechanisms of antidepressant treatments in clinical psychiatry.

Inference of affordances and active motor control in simulated agents

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations.

Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence

What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference-affective self-modelling-are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to "tune" organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.

The parallelism of cognitive economy and physiological economy: A rationality-based dual process theory

Researchers recognize the affinity of habits-as-heuristics and habits-as-routines. This paper argues that the affinity should not be surprising, as both kinds of habits are the outcome of rational choice. The paper finds that the dual process theory, once reconstructed as based on rational choice, reveals that the affinity runs deep, as three-fold parallelism: i) the cognitive economy responsible for habits-as-heuristics parallels what this paper calls the “physiological economy” responsible for habits-as-routines; ii) the occasional slipup of heuristics generated by the cognitive economy parallels the occasional slipup of routines of the physiological economy; and iii) the breakdown of heuristics of the cognitive economy parallels the breakdown of routines of the physiological economy. The rationality-based dual process theory can explain—whereas the single process theory cannot—why slipups do not induce the decision makers to abandon the pertinent habit, but breakdowns do.

Simulating Developmental and Individual Differences of Drawing Behavior in Children Using a Predictive Coding Model

Predictive coding has recently been proposed as a mechanistic approach to explain human perception and behavior based on the integration of perceptual stimuli (bottom-up information) and the predictions about the world based on previous experience (top-down information). However, the gap between the computational accounts of cognition and evidence of behavioral studies remains large. In this study, we used a computational model of drawing based on the mechanisms of predictive coding to systematically investigate the effects of the precision of top-down and bottom-up information when performing a drawing completion task. The results indicated that sufficient precision of both signals was required for the successful completion of the stimuli and that a reduced precision in either sensory or prediction (i.e., prior) information led to different types of atypical drawing behavior. We compared the drawings produced by our model to a dataset of drawings created by children aged between 2 and 8 years old who drew on incomplete drawings. This comparison revealed that a gradual increase in children's precision of top-down and bottom-up information as they develop effectively explains the observed change of drawing style from scribbling toward representational drawing. Furthermore, individual differences that are prevalent in children's drawings, might arise from different developmental pathways regarding the precision of these two signals. Based on these findings we propose a theory of how both general and individual development of drawing could be explained in a unified manner within the framework of predictive coding.