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Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions - such as probabilities and amounts of money to be earned during risky choices - are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes. Our results, based on behavioral measures and drift-diffusion models, provide evidence in favor of the framework where information about individual attributes independently impacts deliberation. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes - possibly alongside an additional comparison of expected value. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive level, decision processes might be more distributed than commonly assumed. Beyond our planned analyses, we also discovered that attribute salience affects people of different risk preference type in different ways: risk-averse participants seem to focus more on probability, except when monetary amount is particularly high; risk-neutral/seeking participants, in contrast, seem to focus more on monetary amount, except when probability is particularly low., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The freedom to choose between options is strongly linked to notions of free will. Accordingly, several studies have shown that individuals demonstrate a preference for choice, or the availability of multiple options, over and above utilitarian value. Yet we lack a decision-making framework that integrates preference for choice with traditional utility maximisation in free choice behaviour. Here we test the predictions of an inference-based model of decision-making in which an agent actively seeks states yielding entropy (availability of options) in addition to utility (economic reward). We designed a study in which participants freely navigated a virtual environment consisting of two consecutive choices leading to reward locations in separate rooms. Critically, the choice of one room always led to two final doors while, in the second room, only one door was permissible to choose. This design allowed us to separately determine the influence of utility and entropy on participants' choice behaviour and their self-evaluation of free will. We found that choice behaviour was better predicted by an inference-based model than by expected utility alone, and that both the availability of options and the value of the context positively influenced participants' perceived freedom of choice. Moreover, this consideration of options was apparent in the ongoing motion dynamics as individuals navigated the environment. In a second study, in which participants selected between rooms that gave access to three or four doors, we observed a similar pattern of results, with participants preferring the room that gave access to more options and feeling freer in it. These results suggest that free choice behaviour is well explained by an inference-based framework in which both utility and entropy are optimised and supports the idea that the feeling of having free will is tightly related to options availability., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Competing Interests: Declaration of Competing Interest None.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The paper introduces a new perspective on abstract concepts (e.g. "freedom") and their associate words representation, the Words As social Tools (WAT) view. Traditional theories conceptualize language as a way to index referents, a shortcut to access meaning, or a way to access meaning through words associations. WAT goes beyond these theories by identifying additional functions of words and language: words are tools helping us to perform actions and change the state of our social environment, and language is a means to improve our thought abilities, to control our behavior and plays a predictive role, helping us to form categories. Most importantly, WAT proposes that language and sociality - along with interoceptive and metacognitive processes - are key for the grounding of abstract concepts (ACs) that are more complex, variable, and more detached from perceptual and motor experience than concrete concepts (CCs). We highlight four tenets of WAT and discuss each of them in light of recent evidence: a. acquisition: compared to concrete concepts, the acquisition of abstract concepts relies more on social and linguistic inputs; b. brain representation: abstract concepts recruit more linguistic and social brain areas; c. mouth activation: due to the relevance of language for representing them, abstract concepts activate more the oral motor system; d. linguistic variability: abstract concepts are more affected by differences between spoken languages. We discuss evidence supporting these four tenets of WAT, and its advantages and limitations compared to other views on abstract concepts. Finally, we outline a conceptual proposal that specifies how internal models supporting the representation and processing of ACs can be grounded on interoceptive, metacognitive, social, and linguistic experience., (Copyright © 2018. Published by Elsevier B.V.)

Human communication is a traditional topic of research in many disciplines such as psychology, linguistics and philosophy, all of which mainly focused on language, gestures and deictics. However, these do not constitute the sole channels of communication, especially during online social interaction, where instead an additional critical role may be played by sensorimotor communication (SMC). SMC refers here to (often subtle) communicative signals embedded within pragmatic actions - for example, a soccer player carving his body movements in ways that inform a partner about his intention, or to feint an adversary; or the many ways we offer a glass of wine, rudely or politely. SMC is a natural form of communication that does not require any prior convention or any specific code. It amounts to the continuous and flexible exchange of bodily signals, with or without awareness, to enhance coordination success; and it is versatile, as sensorimotor signals can be embedded within every action. SMC is at the center of recent interest in neuroscience, cognitive psychology, human-robot interaction and experimental semiotics; yet, we still lack a coherent and comprehensive synthesis to account for its multifaceted nature. Some fundamental questions remain open, such as which interactive scenarios promote or do not promote SMC, what aspects of social interaction can be properly called communicative and which ones entail a mere transfer of information, and how many forms of SMC exist and what we know (or still don't know) about them from an empirical viewpoint. The present work brings together all these separate strands of research within a unified overarching, multidisciplinary framework for SMC, which combines evidence from kinematic studies of human-human interaction and computational modeling of social exchanges., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Studying aversive behaviour is critical for understanding negative emotions and associated psychopathologies. However a comprehensive picture of the mechanisms underlying aversion is lacking, with associative learning theories focusing on Pavlovian reactions and decision-making theoretic approaches on prospective functions. We propose a computational model of aversion that combines goal-directed and Pavlovian forms of control into a unifying framework in which their relative importance is regulated by factors such as threat distance and controllability. Using simulations, we test whether the model can reproduce available empirical findings and discuss its relevance to understanding factors underlying negative emotions such as fear and anxiety. Furthermore, the specific method used to construct the model permits a natural mapping from its components to brain structure and function. Our model provides a basis for a unifying account of aversion that can guide empirical and interventional study contexts., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)