A Bayesian Account of Vocal Adaptation to Pitch-Shifted Auditory Feedback

PLoS One. 2017 Jan 30;12(1):e0169795. doi: 10.1371/journal.pone.0169795. eCollection 2017.

Abstract

Motor systems are highly adaptive. Both birds and humans compensate for synthetically induced shifts in the pitch (fundamental frequency) of auditory feedback stemming from their vocalizations. Pitch-shift compensation is partial in the sense that large shifts lead to smaller relative compensatory adjustments of vocal pitch than small shifts. Also, compensation is larger in subjects with high motor variability. To formulate a mechanistic description of these findings, we adapt a Bayesian model of error relevance. We assume that vocal-auditory feedback loops in the brain cope optimally with known sensory and motor variability. Based on measurements of motor variability, optimal compensatory responses in our model provide accurate fits to published experimental data. Optimal compensation correctly predicts sensory acuity, which has been estimated in psychophysical experiments as just-noticeable pitch differences. Our model extends the utility of Bayesian approaches to adaptive vocal behaviors.

MeSH terms

  • Adaptation, Physiological*
  • Algorithms
  • Animals
  • Bayes Theorem
  • Feedback, Sensory / physiology*
  • Humans
  • Models, Theoretical
  • Pitch Perception / physiology*
  • Songbirds / physiology
  • Stochastic Processes
  • Vocalization, Animal / physiology*

Grant support

Support was provided by the Swiss National Science Foundation Project, 31003A_156976/1 [http://www.snf.ch/en/funding/projects/projects-in-all-disciplines/Pages/default.aspx]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.