Vocal learning is constrained by the statistics of sensorimotor experience

Abstract

The brain uses sensory feedback to correct behavioral errors. Larger errors by definition require greater corrections, and many models of learning assume that larger sensory feedback errors drive larger motor changes. However, an alternative perspective is that larger errors drive learning less effectively because such errors fall outside the range of errors normally experienced and are therefore unlikely to reflect accurate feedback. This is especially crucial in vocal control because auditory feedback can be contaminated by environmental noise or sensory processing errors. A successful control strategy must therefore rely on feedback to correct errors while disregarding aberrant auditory signals that would lead to maladaptive vocal corrections. We hypothesized that these constraints result in compensation that is greatest for smaller imposed errors and least for larger errors. To test this hypothesis, we manipulated the pitch of auditory feedback in singing Bengalese finches. We found that learning driven by larger sensory errors was both slower than that resulting from smaller errors and showed less complete compensation for the imposed error. Additionally, we found that a simple principle could account for these data: the amount of compensation was proportional to the overlap between the baseline distribution of pitch production and the distribution experienced during the shift. Correspondingly, the fraction of compensation approached zero when pitch was shifted outside of the song's baseline pitch distribution. Our data demonstrate that sensory errors drive learning best when they fall within the range of production variability, suggesting that learning is constrained by the statistics of sensorimotor experience.multisensory integration | sensorimotor learning | motor variability E rror correction based on sensory feedback is a ubiquitous mechanism for maintaining behavioral performance (1-4). However, sensory information is vulnerable to environmental noise and to errors in sensory encoding arising at the periphery or during central processing of the sensory signal (5). Such contamination can result in large mismatches between the actual and expected sensory feedback that do not necessarily reflect errors in performance. The brain must therefore decide whether to modify behavior based on sensory feedback (and risk "adapting" to signals that do not accurately reflect performance) or ignore sensory input (and risk leaving errors uncorrected). This problem is especially important in vocal behaviors, in which performance can have significant consequences for the success of an organism and auditory feedback is vulnerable to extrinsic acoustic signals and errors in auditory encoding.During development, both humans and songbirds learn by processes of vocal imitation that rely heavily on auditory feedback (2). Early vocalizations bear little resemblance to mature song or speech, resulting in large differences (errors) between experienced auditory feedback and the sensory goal. With practice, vocalizations become m...

Variability in action: Contributions of a songbird cortical-basal ganglia circuit to vocal motor learning and control

“…In adults, recent work indicates that the song variability continues to be significant for song plasticity. Trial-by-trial variability in adult song can enable reinforcement learning, and the degree of variability can constrain the amount of plasticity (Tumer and Brainard, 2007;Andalman and Fee, 2009;Charlesworth et al, 2011;Warren et al, 2011;Sober and Brainard, 2012).…”

“…Reinforcement models propose the existence of motor ''exploration'' to find actions that result in favorable outcomes and ''exploitation'' to eventually perform just those favorable actions. Moreover, several recent studies have shown that individual differences in the degree of motor variability can predict or constrain the ability to learn or alter motor skills (Sober and Brainard, 2012;Wu et al, 2014). While a handful of studies have described different patterns of basal ganglia activity between behavioral exploration and exploitation states in mammals (Graybiel, 2005;Barnes et al, 2005;Sheth et al, 2011), in many respects, the study of songbirds has been crucial to our current understanding of the nature of trial-by-trial variability, how it is generated in cortical-basal ganglia circuits and its role in behavioral learning and plasticity (Scharff and Nottebohm, 1991;Kao et al, 2005;Ö lveczky et al, 2005;Kao and Brainard, 2006;Tumer and Brainard, 2007;Andalman and Fee, 2009;Mooney, 2009;Stepanek and Doupe, 2010;Fee and Goldberg, 2011;Goldberg and Fee, 2011;Kojima and Doupe, 2011;Warren et al, 2011;Sober and Brainard, 2012).…”

Comparison of body-powered voluntary opening and voluntary closing prehensor for activities of daily life

“…This has significant implications for therapy requirements for users in a clinical or research situation when changing control paradigms. Recent studies suggest that subjects' ability to learn a new paradigm depends on their confidence using the old paradigm: subjects can learn new paradigms more quickly if they are less confident in their old paradigms [28]. It is therefore likely that patients or subjects using one style of device who chose to then begin using both types of device, or a device that allows switching between modes, would need significant occupational therapy training-similar to the training that they received for their initial, single-mode device.…”

Cultural niche construction of repertoire size and learning strategies in songbirds

“…Finally, we made the assumptions that there can be cultural mutation between T and t when b T?T and b t?t are not equal to 0 or 1 (Table 1) and that a short or simple repertoire (t) can be more easily learned by a juvenile bird than a long or complex repertoire (T): b t?t [ b T?T . In other words, a juvenile whose father or tutor has a smaller repertoire (t) will only rarely produce a larger repertoire (T), perhaps through copy error, improvisation, or eavesdropping (Beecher et al 2007;Sober and Brainard 2012). However, a juvenile whose father/tutor has a large repertoire (T) might, with a larger probability (1-b T?T [ 1-b t?t ), learn a smaller repertoire (t) than his tutor, perhaps because of the challenges of learning a large repertoire or because he was not exposed to his father's entire repertoire (Botero et al 2008).…”