A new robot controller model called an ASM-network, built from adaptive sensorimotor maps, enables a robot to learn object discrimination without explicit representations or external rewards. The model combines a mechanism that generates continuous motor activity from past sensorimotor trajectories with an evaluative mechanism that reinforces trajectories supporting higher-order sensorimotor coordinations. In a minimal cognition task, a single robot learned through random exploration and repetition of supportive trajectories. The results demonstrate that recognizable learning behavior can emerge from enactive principles, adapting based on the internal requirements of the action-generating mechanism.
Intentional binding, a measure of how people perceive time between their actions and outcomes, was tested in individual, human-computer, and human-human joint button-pressing tasks using haptic devices. Contrary to expectations from we-agency theory, the overall strength of binding did not differ between partner types. However, within human pairs, participants who reported a stronger sense of agency showed stronger binding, linked to leader-follower movement dynamics. No such link appeared in human-computer interactions. The findings indicate that temporal binding primarily reflects sensorimotor predictability rather than social context or intentionality, and may serve as a signature of how partners co-regulate their actions.