Predictive steering: integration of artificial motor signals in self-motion estimation

Area of Science:

• Neuroscience
• Human motor control
• Sensory integration

Background:

• Self-motion perception integrates vestibular, visual, and predicted sensory signals.
• It's unclear if this framework applies to artificial motor signals, such as driving.

Purpose of the Study:

• To investigate if humans build an internal model of steering-vestibular reafference mapping.
• To determine if artificial motor signals are integrated similarly to self-generated motion.

Main Methods:

• Participants actively controlled a motion platform's velocity via steering.
• A stationary group controlled a line's alignment.
• Gain between steering and velocity changed abruptly to probe adaptation.

Main Results:

• Active steering participants adapted rapidly to gain changes, showing minimal displacement error.
• Stationary participants showed significant deviations after gain changes.
• Vestibular feedback facilitated online steering control and rapid adaptation.

Conclusions:

• Vestibular feedback enables rapid adaptation to gain changes, updating the internal model.
• Humans integrate artificial motor signals into self-motion estimation, similar to self-generated motion.
• This supports a unified model for active and passive self-motion perception.