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.

The nervous system is sensitive to statistical regularities of the external world and forms internal models of these regularities to predict environmental dynamics. Given the inherently social nature of human behavior, being capable of building reliable predictive models of others' actions may be essential for successful interaction. While social prediction might seem to be a daunting task, the study of human motor control has accumulated ample evidence that our movements follow a series of kinematic invariants, which can be used by observers to reduce their uncertainty during social exchanges. Here, we provide an overview of the most salient regularities that shape biological motion, examine the role of these invariants in recognizing others' actions, and speculate that anchoring socially-relevant perceptual decisions to such kinematic invariants provides a key computational advantage for inferring conspecifics' goals and intentions.

Comment in Phys Life Rev. 2022 Dec;43:20-22.

Joint engagement in music often facilitates positive social interaction, effectively shifting participants' perspective from the individual to the collective. The result is tight coordination and uniformity between participants, but at the same time, also remarkable flexibility and creativity. How does music achieve such a fine balance between the strict alignment necessary for coordination, and the substantial latitude necessary for experimentation? To address this question, I propose to analyze joint music engagement within the tight-loose theoretical framework broadly used in the social sciences. Tight-loose theory was originally developed for distinguishing between two archetypical cultural tendencies. On the one hand, tightness, which denotes stringent adherence to social norms, and on the other hand, looseness, which refers to a more flexible and less restrictive attitude towards norms. I posit that the flexible form of collaboration characteristic of musical interaction is due to a coexistence of tightness and looseness within joint engagement in music. I argue that the tight aspects of music can be attributed to its rhythmic structure, which requires continuous and precise temporal alignment between participants. Indeed, when experienced on its own, outside of a musical context, interpersonal synchrony has been repeatedly shown to enhance diverse positive social capacities such as bonding, collaboration and affiliation between interacting individuals, but at the expense of increasing conformity, blind obedience and even hostility towards non-group members. These effects are consistent with synchrony driving a tight interaction, inducing a sense of common group membership (CGM), which can endow music with necessary rigor and order. In contrast, the loose side of music may pertain to the ambiguity in meaning and intention expressed by music, which leaves ample room for interpretation and improvisation. I thus propose that the combined tight-loose nature of music, can simultaneously enhance positive social behaviors and reduce negative ones, leading to a tolerant form of group membership (TGM).

Reputation and reciprocity are key mechanisms for cooperation in human societies, often going hand in hand to favor prosocial behavior over selfish actions. Here we review recent researches at the interface of physics and evolutionary game theory that explored these two mechanisms. We focus on image scoring as the bearer of reputation, as well as on various types of reciprocity, including direct, indirect, and network reciprocity. We review different definitions of reputation and reciprocity dynamics, and we show how these affect the evolution of cooperation in social dilemmas. We consider first-order, second-order, as well as higher-order models in well-mixed and structured populations, and we review experimental works that support and inform the results of mathematical modeling and simulations. We also provide a synthesis of the reviewed researches along with an outlook in terms of six directions that seem particularly promising to explore in the future.

Mechanotherapy is a groundbreaking approach to impact carcinogenesis. Cells sense and respond to mechanical stimuli, translating them into biochemical signals in a process known as mechanotransduction. The impact of stress on tumor growth has been studied in the last three decades, and many papers highlight the role of mechanics as a critical self-inducer of tumor fate at the in vitro and in vivo biological levels. Meanwhile, mathematical models attempt to determine laws to reproduce tumor dynamics. This review discusses biological mechanotransduction mechanisms and mathematical-biomechanical models together. The aim is to provide a common framework for the different approaches that have emerged in the literature from the perspective of tumor avascularity and to provide insight into emerging mechanotherapies that have attracted interest in recent years.

Motor neuroscience and physics-based character animation (PBCA) approach human and humanoid control from different perspectives. The primary goal of PBCA is to control the movement of a ragdoll (humanoid or animal) applying forces and torques within a physical simulation. The primary goal of motor neuroscience is to understand the contribution of different parts of the nervous system to generate coordinated movements. We review the functional principles and the functional anatomy of human motor control and the main strategies used in PBCA. We then explore common research points by discussing the functional anatomy and ongoing debates in motor neuroscience from the perspective of PBCA. We also suggest there are several benefits to be found in studying sensorimotor integration and human-character coordination through closer collaboration between these two fields.

My critical review [1] elicited a welcome diversity of perspectives across the 12 commentaries now published [2-13]. In total 28 co-authors were inspired to contribute. In addition to engaging with the critical perspectives of my review, several of the commentaries take the debates and discussions into insightful and potentially important supplementary domains that I highlight in what follows. I have extracted a number of major themes in which I detected overlaps in the foci of different commentaries, and I use these to organise my replies. I hope that our shared efforts will constitute some degree of 'cultural evolution' in our science, as suggested in the title of this reply to commentaries.

Sociality and timing are tightly interrelated in human interaction as seen in turn-taking or synchronised dance movements. Sociality and timing also show in communicative acts of other species that might be pleasurable, but also necessary for survival. Sociality and timing often co-occur, but their shared phylogenetic trajectory is unknown: How, when, and why did they become so tightly linked? Answering these questions is complicated by several constraints; these include the use of divergent operational definitions across fields and species, the focus on diverse mechanistic explanations (e.g., physiological, neural, or cognitive), and the frequent adoption of anthropocentric theories and methodologies in comparative research. These limitations hinder the development of an integrative framework on the evolutionary trajectory of social timing and make comparative studies not as fruitful as they could be. Here, we outline a theoretical and empirical framework to test contrasting hypotheses on the evolution of social timing with species-appropriate paradigms and consistent definitions. To facilitate future research, we introduce an initial set of representative species and empirical hypotheses. The proposed framework aims at building and contrasting evolutionary trees of social timing toward and beyond the crucial branch represented by our own lineage. Given the integration of cross-species and quantitative approaches, this research line might lead to an integrated empirical-theoretical paradigm and, as a long-term goal, explain why humans are such socially coordinated animals.

Creative cognition does not just involve cognitive processes in direct service of the main task objective (e.g., idea generation), but also metacognitive processes that monitor and regulate cognition adaptively (e.g., evaluation of ideas and task performance, or development and selection of task strategies). Although metacognition is vital for creative performance, relevant work is sparse, which may be partly due to persistent ambiguities in the theoretical conceptualization of creative metacognition. Therefore, this article proposes a systematic framework of creative metacognition (CMC), which builds on recent advancements in metacognition theory and extends them to meet the specifics of creative cognition. The CMC framework consists of two dynamic components-monitoring and control-and a more static component of metacognitive knowledge, each subsuming metacognitive processes applying to the level of task, performance, and responses. We describe the presumed function of these metacognitive components in the creative process, present evidence in support of each, and discuss their association with related constructs, such as creative self-beliefs. We further highlight the dynamic interplay of metacognitive processes across task performance and identify promising avenues for future research.

We advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

There is a need for alternative technologies that can deliver safe and nutritious foods at lower costs as compared to conventional processes. Pulsed electric field (PEF) technology has been utilised for a plethora of different applications in the life and physical sciences, such as gene/drug delivery in medicine and extraction of bioactive compounds in food science and technology. PEF technology for treating liquid foods involves engineering principles to develop the equipment, and quantitative biochemistry and microbiology techniques to validate the process. There are numerous challenges to address for its application in liquid foods such as the 5-log pathogen reduction target in food safety, maintaining the food quality, and scale up of this physical approach for industrial integration. Here, we present the engineering principles associated with pulsed electric fields, related inactivation models of microorganisms, electroporation and electropermeabilization theory, to increase the quality and safety of liquid foods; including water, milk, beer, wine, fruit juices, cider, and liquid eggs. Ultimately, we discuss the outlook of the field and emphasise research gaps.

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.

A prerequisite for copying innovative behaviour faithfully is the capacity of observers' brains, regarded as 'hierarchically mechanistic minds', to overcome cognitive 'surprisal' (see 2.), by maximising the evidence for their internal models, through active inference. Unlike modern humans, chimpanzees and other great apes show considerable limitations in their ability, or 'Zone of Bounded Surprisal', to overcome cognitive surprisal induced by innovative or unorthodox behaviour that rarely, therefore, is copied precisely or accurately. Most can copy adequately what is within their phenotypically habitual behavioural repertoire, in which technology plays scant part. Widespread intra- and intergenerational social transmission of complex technological innovations is not a hall-mark of great-ape taxa. 3 Ma, precursors of the genus Homo made stone artefacts, and stone-flaking likely was habitual before 2 Ma. After that time, early Homo erectus has left traces of technological innovations, though faithful copying of these and their intra- and intergenerational social transmission were rare before 1 Ma. This likely owed to a cerebral infrastructure of interconnected neuronal systems more limited than ours. Brains were smaller in size than ours, and cerebral neuronal systems ceased to develop when early Homo erectus attained full adult maturity by the mid-teen years, whereas its development continues until our mid-twenties nowadays. Pleistocene Homo underwent remarkable evolutionary adaptation of neurobiological propensities, and cerebral aspects are discussed that, it is proposed here, plausibly, were fundamental for faithful copying, which underpinned social transmission of technologies, cumulative learning, and culture. Here, observers' responses to an innovation are more important for ensuring its transmission than is an innovator's production of it, because, by themselves, the minimal cognitive prerequisites that are needed for encoding and assimilating innovations are insufficient for practical outcomes to accumulate and spread intra- and intergenerationally.