Moving in time: Bayesian causal inference explains movement coordination to auditory beats.

Area of Science:

• Neuroscience
• Cognitive Science
• Auditory Perception

Background:

• Skilled movements often require synchronizing with auditory signals in complex environments.
• The mechanisms by which humans integrate or select between multiple auditory cues for movement control are not fully understood.

Purpose of the Study:

• To investigate how humans synchronize movements with potentially conflicting auditory timing signals.
• To determine if Bayesian inference explains the integration or separation of auditory cues for motor timing.

Main Methods:

• Participants tapped their fingers to two simultaneously presented metronomes with differing phase and temporal regularity.
• Behavioral data on synchronization choices (integrate vs. separate) and motor timing errors were collected.

Main Results:

• Bayesian inference accurately predicts when participants integrate or separate auditory timing cues.
• The proposed causal inference model provides a superior explanation of the observed motor timing behavior compared to alternative models.

Conclusions:

• Humans employ a Bayesian inference process to resolve the origins of auditory signals and control movement timing.
• This framework offers insights into the neural basis of sensorimotor synchronization in complex auditory environments.