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

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.

The potential use of Forbrain® in stuttering: A single-case study

“…This is in line with the socalled corollary discharge view of motor control, according to which the motor (e.g., vocal) system sends an efferent copy or corollary discharge of the sound it aims producing, so that the encoding of the auditory input resulting from the self-produced sounds is attenuated in the auditory system (Scott, 2013;Wolpert et al, 1995). When the received input does not match the efferent copy of the motor command, a prediction error is generated (Hahnloser & Narula, 2017) that forces a system readjustment. The results obtained here with Forbrain ® fit well with this proposal.…”

“…This apparent contradiction is reconcilable in the context of the phono-articulatory loop, which features the existence of two parallel premotor systems for speech production (Ritto, Costa, et al, 2016), and supports Forbrain@ in fact as a device of AAF. Indeed, the sensory processing is altered as a consequence of motor adaptation to altered visual, somatosensory and auditory feedback (Ostry & Gribble, 2016), and conversely, motor output is fine-grained adjusted as a consequence of distorted sensory input (Hahnloser & Narula, 2017). This is in line with the socalled corollary discharge view of motor control, according to which the motor (e.g., vocal) system sends an efferent copy or corollary discharge of the sound it aims producing, so that the encoding of the auditory input resulting from the self-produced sounds is attenuated in the auditory system (Scott, 2013;Wolpert et al, 1995).…”

“…These forms of AAF have been shown also to affect speech in fluent speaking adults and stutterers (Martin, Siegel, Johnson, & Haroldson, 1984). A recent proposal posits that AAF may work through a mechanism of motor compensation, so that a compensatory motor command is issued to correct the perceived shift in the acoustic input based on a predictive internal model of the expected output (Hahnloser & Narula, 2017;Ostry & Gribble, 2016). Further, the so-called Spt area in the planum temporale of the left temporal lobe has been proposed as the candidate brain region to support these predictive interactions (Hickok, Houde, & Rong 2011).…”

What drives the perceptual change resulting from speech motor adaptation? Evaluation of hypotheses in a Bayesian modeling framework

“…However, the three possible origins of incomplete compensation (discussed in Section “Three suggested origins for incomplete compensation”) actually suggest three possible mechanisms whereby different magnitudes of sensory error would result in different degrees of compensation completeness. First, at the level of the sensory motor mappings, larger sensory errors may drive slower update in order to avoid a faulty reorganization of the learned mapping in the case of totally unexpected and inappropriate sensory signals (see for instance the work of [ 72 ] for a modeling approach in line with this idea). Second, at the level of the relative weighting of sensory pathways, the magnitude of sensory errors could disadvantage the pathway with larger errors, assuming that large unexpected errors would arise from inaccurate sensors, which would then be considered unreliable.…”

Spontaneous variability predicts adaptive motor response in vocal pitch control

“…Intriguingly, the vocal 77 variability in birdsongs is not simply due to the intrinsic noise in the peripheral motor system, but a certain 78 amount of them is 'actively' generated by a dedicated circuit that is required for song learning ( the active generation of variability in the motor processes is likely to suit to the adaptation-related motor 83 exploration (Dhawale et al, 2017). Such mechanism for songbirds' vocal control could be shared with humans 84 (Hahnloser and Narula, 2017), especially when taking into account behavioral and neural parallels between 85 these two species for vocalization development (Doupe and Kuhl, 1999;Kuhl, 2004;Lipkind et al, 2013;86 Tchernichovski and Marcus, 2014; Prather et al, 2017). 87…”