In the face of limited computational resources, bounded rational decision theory predicts that information-processing should be concentrated on actions that make a significant contribution in terms of the utility achieved. Accordingly, information-processing can be simplified by choosing stereotypic actions that lead to satisfactory performance over a range of different inputs rather than choosing a specific action for each input. Such a set of similar inputs with similar action responses would then correspond to an abstraction that can be harnessed with possibly negligible loss in utility, but with potentially considerable savings in information-processing effort. Here we test this prediction in an identification task, where human subjects were asked to estimate the roundness of ellipses varying from a straight line to a perfect circle. Crucially, when reporting their estimates, subjects could choose between three different levels of precision corresponding to three levels of abstraction in a decision-making hierarchy. To induce changes in level selection, we manipulated the information-processing resources available at the perceptual and action stages by varying the difficulty of identifying the stimulus and by enforcing different response times in the action stage. In line with theoretical predictions, we find that subjects adapt their abstraction level depending on the available resources. We compare subjects' behavior to the maximum efficiency predicated by the bounded rational decision-making model and investigate possible sources of inefficiency.

The Nash equilibrium concept has previously been shown to be an important tool to understand human sensorimotor interactions, where different actors vie for minimizing their respective effort while engaging in a multi-agent motor task. However, it is not clear how such equilibria are reached. Here, we compare different reinforcement learning models to human behavior engaged in sensorimotor interactions with haptic feedback based on three classic games, including the prisoner’s dilemma, and the symmetric and asymmetric matching pennies games. We find that a discrete analysis that reduces the continuous sensorimotor interaction to binary choices as in classical matrix games does not allow to distinguish between the different learning algorithms, but that a more detailed continuous analysis with continuous formulations of the learning algorithms and the game-theoretic solutions affords different predictions. In particular, we find that Q-learning with intrinsic costs that disfavor deviations from average behavior explains the observed data best, even though all learning algorithms equally converge to admissible Nash equilibrium solutions. We therefore conclude that it is important to study different learning algorithms for understanding sensorimotor interactions, as such behavior cannot be inferred from a game-theoretic analysis alone, that simply focuses on the Nash equilibrium concept, as different learning algorithms impose preferences on the set of possible equilibrium solutions due to the inherent learning dynamics.

In most Brain-Computer Interfaces (BCI) experimental paradigms based on Motor Imageries (MI), subjects perform continuous motor imagery (CMI), i.e. a repetitive and prolonged intention of movement, for a few seconds. To improve efficiency such as detecting faster a motor imagery, the purpose of this study is to show the difference between a discrete motor imagery (DMI), i.e. a single short MI, and a CMI. The results of experiment involving 13 healthy subjects suggest that a DMI generates a robust post-MI event-related synchronization (ERS). Moreover event-related desynchronization (ERD) produced by DMI seems less variable in certain cases compared to a CMI.

In this article, we study how combined motor imageries can be detected to deliver more commands in a Brain-Computer Interface for controlling a robotic arm. Motor imageries are a major way to deliver commands in BCI. Nevertheless only a few systems use more than three motor imageries: right hand, left hand and feet. Combining them allow to get four additional commands. We present an electrophysiological study to show that i) simple motor imageries have mainly an electrical modulation over the cortical area related the body part involved in the imagined movement and that ii) combined motor imageries reflect a superposition of the electrical activity of simple motor imageries. A shrinkage linear discriminant analysis has been used to test as a first step how a resting state and seven motor imageries can be detected. 11 healthy subjects participated in the experiment for which an intuitive assignment has been done to associate motor imageries and movements of the robotic arm with 7 degrees of freedom.