Publications

In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor (SM) signals and, accordingly, attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of SM interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize the self (including proximo-distal arm-joint dependencies) and to assess motor to sensory influences, and the other by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger causality (GC), transfer entropy (TE), as well as a novel “Acceleration Transfer” (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed SM graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. This study results show that although TE can capture the directional dependencies, a rectified subtraction operation denoted, in this study, as AT is both sufficient and computationally cheaper.

JTD Keywords: agency, attention, autonomy, cognitive development, computational cognition, developmental psychology, sensorimotor learning, Agency, Attention, Autonomy, Cognitive development, Computational cognition, Developmental psychology, Model, Sensorimotor learning, Theory of mind

© 2020 The Authors Selective attention informs decision-making by biasing perceptual processing towards task-relevant stimuli. In experimental and computational literature, this is most often implemented through top-down excitation of selected stimuli. However, physiological and anatomical evidence shows that in certain situations, top-down signals could instead be inhibitory. In this study, we investigated how such an inhibitory mechanism of top-down attention compares with an excitatory one. We did so in a neurorobotics context where the agent was controlled using an established hierarchical architecture. We augmented the architecture with an attentional system that implemented top-down attention biasing as connection gains. We tested four models of top-down attention on the simulated agent performing a foraging task: without top-down biasing, with only excitatory top-down gain, with only inhibitory top-down gain, and with both excitatory and inhibitory top-down gain. We manipulated the reward-distractor ratio that was presented and assessed the agent's performance using accumulated rewards and the latency of the selection. Using these measures, we provide evidence that excitatory and inhibitory mechanisms of attention complement each other.

JTD Keywords: embodied cognition, foraging, inhibition, Embodied cognition, Foraging, Inhibition, Selective attention

Robot-assisted rehabilitation therapies usually focus on physical aspects rather than on cognitive factors. However, cognitive aspects such as attention, motivation, and engagement play a critical role in motor learning and thus influence the long-term success of rehabilitation programs. This paper studies motor-related EEG activity during the execution of robot-assisted passive movements of the upper limb, while participants either: i) focused attention exclusively on the task; or ii) simultaneously performed another task. Six healthy subjects participated in the study and results showed lower desynchronization during passive movements with another task simultaneously being carried out (compared to passive movements with exclusive attention on the task). In addition, it was proved the feasibility to distinguish between the two conditions.

JTD Keywords: Electrodes, Electroencephalography, Induction motors, Medical treatment, Robot sensing systems, Time frequency analysis, Biomechanics, Cognition, Electroencephalography, Medical robotics, Medical signal detection, Medical signal processing, Patient rehabilitation, Attention, Cognitive aspects, Desynchronization, Engagement, Motivation, Motor learning, Motor task, Motor-related EEG activity, Physical aspects, Robot-assisted passive movement detection, Robot-assisted rehabilitation therapies, Upper limb