Bounded rational response equilibria in human sensorimotor interactions

Thermodynamic fluctuation theorems govern human sensorimotor learning

The application of thermodynamic reasoning in the study of learning systems has a long tradition. Recently, new tools relating perfect thermodynamic adaptation to the adaptation process have been developed. These results, known as fluctuation theorems, have been tested experimentally in several physical scenarios and, moreover, they have been shown to be valid under broad mathematical conditions. Hence, although not experimentally challenged yet, they are presumed to apply to learning systems as well. Here we address this challenge by testing the applicability of fluctuation theorems in learning systems, more specifically, in human sensorimotor learning. In particular, we relate adaptive movement trajectories in a changing visuomotor rotation task to fully adapted steady-state behavior of individual participants. We find that human adaptive behavior in our task is generally consistent with fluctuation theorem predictions and discuss the merits and limitations of the approach.

From Bayes-optimal to heuristic decision-making in a two-alternative forced choice task with an information-theoretic bounded rationality model

Bayes optimal and heuristic decision-making schemes are often considered fundamentally opposed to each other as a framework for studying human choice behavior, although recently it has been proposed that bounded rationality may provide a natural bridge between the two when varying information-processing resources. Here, we investigate a two-alternative forced choice task with varying time constraints, where subjects have to assign multi-component symbolic patterns to one of two stimulus classes. As expected, we find that subjects' response behavior becomes more imprecise with more time pressure. However, we also see that their response behavior changes qualitatively. By regressing subjects' decision weights, we find that decisions allowing for plenty of decision time rely on weighing multiple stimulus features, whereas decisions under high time pressure are made mostly based on a single feature. While the first response pattern is in line with a Bayes-optimal decision strategy, the latter could be considered as an instantiation of heuristic decision-making with cue discounting. When fitting a bounded rational decision model with multiple feature channels and varying information-processing capacity to subjects' responses, we find that the model is able to capture subjects' behavioral change. The model successfully reflects the simplicity of heuristics as well as the efficiency of optimal decision making, thus acting as a bridge between the two approaches.

Authority or Autonomy? Exploring Interactions between Central and Peer Punishments in Risk-Resistant Scenarios

Game theory provides a powerful means to study human cooperation and better understand cooperation-facilitating mechanisms in general. In classical game-theoretic models, an increase in group cooperation constantly increases people's gains, implying that individual gains are a continuously varying function of the cooperation rate. However, this is inconsistent with the increasing number of risk-resistant scenarios in reality. A risk-resistant scenario means once a group does not successfully resist the risk, all individuals lose their resources, such as a community coping with COVID-19 and a village resisting a flood. In other words, individuals' gains are segmented about the collaboration rate. This paper builds a risk-resistant model to explore whether punishment still promotes collaboration when people resist risk. The results show that central and peer punishments can both encourage collaboration but with different characteristics under different risk-resistant scenarios. Specifically, central punishment constrains the collaboration motivated by peer punishment regardless of risk, while peer punishment limits the collaboration induced by central punishment only when the risk is high. Our findings provide insights into the balance between peer punishment from public autonomy and central punishment from central governance, and the proposed model paves the way for the development of richer risk-resistant models.

Bounded rational response equilibria in human sensorimotor interactions

The Nash equilibrium is one of the most central solution concepts to study strategic interactions between multiple players and has recently also been shown to capture sensorimotor interactions between players that are haptically coupled. While previous studies in behavioural economics have shown that systematic deviations from Nash equilibria in economic decision-making can be explained by the more general quantal response equilibria, such deviations have not been reported for the sensorimotor domain. Here we investigate haptically coupled dyads across three different sensorimotor games corresponding to the classic symmetric and asymmetric Prisoner's Dilemma, where the quantal response equilibrium predicts characteristic shifts across the three games, although the Nash equilibrium stays the same. We find that subjects exhibit the predicted deviations from the Nash solution. Furthermore, we show that taking into account subjects' priors for the games, we arrive at a more accurate description of bounded rational response equilibria that can be regarded as a quantal response equilibrium with non-uniform prior. Our results suggest that bounded rational response equilibria provide a general tool to explain sensorimotor interactions that include the Nash equilibrium as a special case in the absence of information processing limitations.