In recent years, researchers in social cognition have found the `perceptual crossing paradigm' to be both a theoretical and practical advance towards meeting particular challenges. This paradigm has been used to analyze the type of interactive processes that emerge in minimal interactions and it has allowed progress towards understanding of the principles of social cognition processes. In this paper, we analyze whether some critical aspects of these interactions could not have been observed by previous studies. We consider alternative indicators that could complete, or even lead us to rethink, the current interpretation of the results obtained from both experimental and simulated modelling in the fields of social interactions and minimal perceptual crossing. In particular, we discuss the possibility that previous experiments have been analytically constrained to a short-term dynamic type of player response. Additionally, we propose the possibility of considering these experiments from a more suitable framework based on the use and analysis of long-range correlations and fractal dynamics. We will also reveal evidence supporting the idea that social interactions are deployed along many scales of activity. Specifically, we propose that the fractal structure of the interactions could be a more adequate framework to understand the type of social interaction patterns generated in a social engagement.

This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.

This paper outlines a methodological approach for designing adaptive agents driving themselves near points of criticality. Using a synthetic approach we construct a conceptual model that, instead of specifying mechanistic requirements to generate criticality, exploits the maintenance of an organizational structure capable of reproducing critical behavior. Our approach exploits the well-known principle of universality, which classifies critical phenomena inside a few universality classes of systems independently of their specific mechanisms or topologies. In particular, we implement an artificial embodied agent controlled by a neural network maintaining a correlation structure randomly sampled from a lattice Ising model at a critical point. We evaluate the agent in two classical reinforcement learning scenarios: the Mountain Car benchmark and the Acrobot double pendulum, finding that in both cases the neural controller reaches a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour, maximizing the mutual information between neurons and sensorimotor patterns. Finally, we discuss the possible applications of this synthetic approach to the comprehension of deeper principles connected to the pervasive presence of criticality in biological and cognitive systems

The concept of agency is of fundamental importance for Cognitive Science. However, usual definitions of agency are loose and the work to capture and measure it using mathematical tools is still in its infancy. Recently, the framework of integrated information theory has been proposed to capture the causal boundaries of biological autonomous systems. Here, we test measures of integrated information theory in a minimal model to test its capacity to identify and delimit an autonomous agent interacting with an environment. Doing so, we reformulate some aspects of current definitions of agency using insights from integrated information in our models. Specifically, we propose a redefinition of how we capture the ability of an agent to modulate its interaction with the environment in terms of the control of the emergent causal structure of the agent-environment system. In this way, we propose an operational definition of agency based on the capacity of a system to modulate its causal boundary, extending and reducing it by functionally open and closing sensorimotor loops, and coupling the agent to different environmental processes. This allows us to formulate a tentative measure for our definition of agency and test it in minimal models of sensorimotor interaction, which we test in a minimal agent evolved to solve a simple task. The concept of agency is of fundamental importance for Cognitive Science. However, usual definitions of agency are loose and the work to capture and measure it using mathematical tools is still in its infancy. Recently, the framework of integrated information theory has been proposed to capture the causal boundaries of biological autonomous systems. Here, we test measures of integrated information theory in a minimal model to test its capacity to identify and delimit an autonomous agent interacting with an environment. Doing so, we reformulate some aspects of current definitions of agency using insights from integrated information in our models. Specifically, we propose a redefinition of how we capture the ability of an agent to modulate its interaction with the environment in terms of the control of the emergent causal structure of the agent-environment system. In this way, we propose an operational definition of agency based on the capacity of a system to modulate its causal boundary, extending and reducing it by functionally open and closing sensorimotor loops, and coupling the agent to different environmental processes. This allows us to formulate a tentative measure for our definition of agency and test it in minimal models of sensorimotor interaction, which we test in a minimal agent evolved to solve a simple task. The concept of agency is of fundamental importance for Cognitive Science. However, usual definitions of agency are loose and the work to capture and measure it using mathematical tools is still in its infancy. Recently, the framework of integrated information theory has been proposed to capture the causal...