Biology subjects

Sanguineti, V.

Publications and source records attributed to Sanguineti, V..

3 recordsLinked to original sources

bioRxiv2024

Computational joint action: dynamical models to understand the development of joint coordination

Coordinating with others is part of our everyday experience. Previous studies using sensorimotor coordination games suggest that human dyads develop coordination strategies that can be interpreted as Nash equilibria. However, if the players are uncertain about what their partner is doing, they develop coordination strategies which are robust to the actual partners actions. This has suggested that humans select their actions based on an explicit prediction of what the partner will be doing - a partner model - which is probabilistic by nature. However, the mechanisms underlying the development of a joint coordination over repeated trials remain unknown. Very much like sensorimotor adaptation of individuals to external perturbations (eg force fields or visual rotations), dynamical models may help to understand how joint coordination develops over repeated trials. Here we present a general computational model - based on game theory and Bayesian estimation - designed to understand the mechanisms underlying the development of a joint coordination over repeated trials. Joint tasks are modeled as quadratic games, where each participants task is expressed as a quadratic cost function. Each participant predicts their partners next move (partner model) by optimally combining predictions and sensory observations, and selects their actions through a stochastic optimization of its expected cost, given the partner model. The model parameters include perceptual uncertainty (sensory noise), partner representation (retention rate and process noise), uncertainty in action selection and its rate of decay (which can be interpreted as the actions learning rate). The model can be used in two ways: (i) to simulate interactive behaviors, thus helping to make specific predictions in the context of a given joint action scenario; and (ii) to analyze the action time series in actual experiments, thus providing quantitative metrics that describe individual behaviors during an actual joint action. We demonstrate the model in a variety of joint action scenarios. In a sensorimotor version of the Stag Hunt game, the model predicts that different representations of the partner lead to different Nash equilibria. In a joint two via-point (2-VP) reaching task, in which the actions consist of complex trajectories, the model captures well the observed temporal evolution of performance. For this task we also estimated the model parameters from experimental observations, which provided a comprehensive characterization of individual dyad participants. Computational models of joint action may help identifying the factors preventing or facilitating the development of coordination. They can be used in clinical settings, to interpret the observed behaviors in individuals with impaired interaction capabilities. They may also provide a theoretical basis to devise artificial agents that establish forms of coordination that facilitate neuromotor recovery. Author summaryActing together (joint action) is part of everyday experience. But, how do we learn to coordinate with others and collaborate? Using a combination of experiments and computational models we show that through multiple repetitions of the same joint task we select the action which represents the best response to what we believe our opponent will do. Such a belief about our partner (partner model) is developed gradually, by optimally combining prior assumptions (how repeatable or how erratic our opponent behaves) with sensory information about our opponents past actions. Rooted in game theory and Bayesian estimation, the model accounts for the development of the mutual trust among partners which is essential for establishing a mutually advantageous collaboration, and explains how we combine decisions and movements in complex coordination scenarios. The model can be used as a generative tool, to simulate the development of coordination in a specific joint action scenario, and as an analytic tool to characterize the individual traits or defects in the ability to establish collaborations.

neuroscience↗

bioRxiv2020

Self-operated stimuli improve subsequent visual motion processing

Proper interpretation of visual information requires capturing the structural regularities in the visual signal and this frequently occurs in conjunction with movement. Perceptual interpretation is complicated both by transient perceptual changes that accompany motor activity, and as found in audition and somatosensation, by more persistent changes that accompany the learning of new movements. Here we asked whether motor learning also results in sustained changes to visual perception. We designed a reaching task in which participants directly controlled the visual information they received, which we term self-operated stimuli. Specifically, they trained to make movements in a number of directions. Directional information was provided by the motion of an intrinsically ambiguous moving stimulus which was directly tied to motion of the hand. We find that movement training improves perception of coherent stimulus motion, and that changes in movement are correlated with the perceptual change. No perceptual changes are observed in passive observers even when they are provided with an explicit strategy to solve perceptual grouping. Comparison of empirical perceptual data with simulations based on a Bayesian generative model of motion perception suggests that movement training promotes the fine-tuning of the internal representation of stimulus geometry. These results emphasize the role of sensorimotor interaction in determining the persistent properties in space and time that define a percept.

neuroscience↗

bioRxiv2018

Incomplete information about the partner affects the development of collaborative strategies in joint action

Physical interaction with a partner plays an essential role in our life experience and is the basis of many daily activities. When two physically coupled humans have different and partly conflicting goals, they face the challenge of negotiating some type of collaboration. This requires that both subjects understand their opponents state and current actions. But, how would the collaboration be affected if information about their opponent were unreliable or incomplete? Here we show that incomplete information about the partner affects not only the speed at which a collaborative strategy is achieved (less information, slower learning), but also the modality of the collaboration. In particular, incomplete or unreliable information leads to an interaction strategy characterized by alternating leader-follower roles. In contrast, more reliable information leads to a more synchronous behavior, in which no specific roles can be identified. Simulations based on a combination of game theory and Bayesian estimation suggested that synchronous behaviors denote optimal interaction (Nash equilibrium). Roles emerge as sub-optimal forms of interaction, which minimize the need to know about the partner. These findings suggest that physical interaction strategies are shaped by the trade-off of between the task requirements and the uncertainty of the information available about the opponent.\n\nAuthor summaryMany activities in daily life involve physical interaction with a partner or opponent. In many situations they have conflicting goals. Therefore, they need to negotiate some form of collaboration. Although very common, these situations have rarely been studied empirically. In this study, we specifically address what is a optimal collaboration and how it can be achieved. We also address how developing a collaboration is affected by uncertainty about the partner. Through a combination of empirical studies and computer simulations based on game theory, we show that subject pairs (dyads) are capable of developing stable collaborations, but the learned collaboration strategy depends on the reliability of the information about the partner. High-information dyads converge to the optimal strategies in game-theoretic sense. Low-information dyads converge to strategies that minimize the need to know about the partner. These findings are consistent with a game theoretic learning model which relies on estimates of partner actions, but not partner goals. This similarity sheds some light on the minimal computational machinery which is necessary to an intelligent agent in order to develop stable physical collaborations.

neuroscience↗