Learning-induced autonomy of sensorimotor systems

Increasing Autonomy of Learning Sensorimotor Transformations with Dynamic Neural Fields

In this paper, we introduce a neuraldynamic architecture that enables autonomous learning of sensory-motor mappings in a closed behavioral loop. Dynamic neural fields ensure stability of perceptual and motor representations, a neural-dynamic representation of the condition-of-satisfaction autonomously terminates the current action and enables activation of the next action, triggering a transien...

Modeling Sensorimotor Learning with Linear Dynamical Systems

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the curr...

Sensorimotor Learning

Cybernetic Autonomy: an Analysis and Critique of Adaptive Learning Systems

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Though personalized learning has been a goal of educators since the days of Aristotle and private tutors, it is only relatively recently that technological and socio-cultural drivers have made personalized learning at scale possible. The development of digital technologies that allow for the analysis of large volumes of student data, combined with greater a...

Computations underlying sensorimotor learning.

The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning fr...