Measuring and modeling the motor system with machine learning

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues, where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.

Subjective feeling of re-experiencing past events using immersive virtual reality prevents a loss of episodic memory

INTRODUCTION

Personally meaningful past episodes, defined as episodic memories (EM), are subjectively re-experienced from the natural perspective and location of one's own body, as described by bodily self-consciousness (BSC). Neurobiological mechanisms of memory consolidation suggest how initially irrelevant episodes may be remembered, if related information makes them gain importance later in time, leading for instance, to a retroactive memory strengthening in humans.

METHODS

Using an immersive virtual reality system, we were able to directly manipulate the presence or absence of one's body, which seems to prevent a loss of initially irrelevant, self-unrelated past events.

RESULTS AND CONCLUSION

Our findings provide an evidence that personally meaningful memories of our past are not fixed, but may be strengthened by later events, and that body-related integration is important for the successful recall of episodic memories.