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Learning the microstructure of successful behavior.

Charlesworth JD, Tumer EC, Warren TL, Brainard MS - Nat. Neurosci. (2011)

Bottom Line: However, successful performance of many motor skills, such as speech articulation, also requires learning behavioral trajectories that vary continuously over time.A simple principle predicted the detailed structure of learning: birds learned to produce the average of the behavioral trajectories associated with successful outcomes.This learning rule accurately predicted the structure of learning at a millisecond timescale, demonstrating that the nervous system records fine-grained details of successful behavior and uses this information to guide learning.

View Article: PubMed Central - PubMed

Affiliation: W M Keck Center for Integrative Neuroscience, University of California, San Francisco, California, USA. jcharles@phy.ucsf.edu

ABSTRACT
Reinforcement signals indicating success or failure are known to alter the probability of selecting between distinct actions. However, successful performance of many motor skills, such as speech articulation, also requires learning behavioral trajectories that vary continuously over time. Here, we investigated how temporally discrete reinforcement signals shape a continuous behavioral trajectory, the fundamental frequency of adult Bengalese finch song. We provided reinforcement contingent on fundamental frequency performance only at one point in the song. Learned changes to fundamental frequency were maximal at this point, but also extended both earlier and later in the fundamental frequency trajectory. A simple principle predicted the detailed structure of learning: birds learned to produce the average of the behavioral trajectories associated with successful outcomes. This learning rule accurately predicted the structure of learning at a millisecond timescale, demonstrating that the nervous system records fine-grained details of successful behavior and uses this information to guide learning.

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Inter-syllable differences in the structure of variation predict differences in learninga. Natural pattern of fundamental frequency variation for two syllables, expressed as residuals from the mean. Each panel depicts the fundamental frequency performance on 30 consecutive renditions of the syllable during baseline song. The variability for syllable B (green) appears to exhibit faster temporal fluctuations than for syllable A (gray). b. The time scale of variability for syllables A (gray) and B (green, dashed). Coefficient of determination (r2) traces depict the extent to which a deviation from the mean at a specific time in the syllable determines the deviations from the mean at surrounding times in the syllable. A more rapid decay in the coefficient of determination indicates a more rapid time scale of variability (e.g. syllable B relative to syllable A). c. Actual learning in syllables A and B, compared with predictions of learning calculated as before. d. We compared the actual learning for a syllable with predictions based on FF variation for that syllable (“syllable-specific” variation, as before) or predictions based on FF variation for syllables targeted in all other experiments (“general” variation). Prediction error was quantified as mean distance from the actual structure of learning. Predictions using syllable-specific variation had significantly less error than predictions using general variation (Wilcoxon signed-rank test; p=0.005, n=28; horizontal lines denote means).
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Figure 5: Inter-syllable differences in the structure of variation predict differences in learninga. Natural pattern of fundamental frequency variation for two syllables, expressed as residuals from the mean. Each panel depicts the fundamental frequency performance on 30 consecutive renditions of the syllable during baseline song. The variability for syllable B (green) appears to exhibit faster temporal fluctuations than for syllable A (gray). b. The time scale of variability for syllables A (gray) and B (green, dashed). Coefficient of determination (r2) traces depict the extent to which a deviation from the mean at a specific time in the syllable determines the deviations from the mean at surrounding times in the syllable. A more rapid decay in the coefficient of determination indicates a more rapid time scale of variability (e.g. syllable B relative to syllable A). c. Actual learning in syllables A and B, compared with predictions of learning calculated as before. d. We compared the actual learning for a syllable with predictions based on FF variation for that syllable (“syllable-specific” variation, as before) or predictions based on FF variation for syllables targeted in all other experiments (“general” variation). Prediction error was quantified as mean distance from the actual structure of learning. Predictions using syllable-specific variation had significantly less error than predictions using general variation (Wilcoxon signed-rank test; p=0.005, n=28; horizontal lines denote means).

Mentions: If the average structure of successful behavior indeed determines the structure of learning, then syllables with different patterns of fundamental frequency variation should exhibit different learning. Here, we found that some syllables exhibited slower fundamental frequency fluctuations than others (Fig. 5a–b) and we took advantage of this to test whether these natural differences in variation predict differences in learning.


Learning the microstructure of successful behavior.

Charlesworth JD, Tumer EC, Warren TL, Brainard MS - Nat. Neurosci. (2011)

Inter-syllable differences in the structure of variation predict differences in learninga. Natural pattern of fundamental frequency variation for two syllables, expressed as residuals from the mean. Each panel depicts the fundamental frequency performance on 30 consecutive renditions of the syllable during baseline song. The variability for syllable B (green) appears to exhibit faster temporal fluctuations than for syllable A (gray). b. The time scale of variability for syllables A (gray) and B (green, dashed). Coefficient of determination (r2) traces depict the extent to which a deviation from the mean at a specific time in the syllable determines the deviations from the mean at surrounding times in the syllable. A more rapid decay in the coefficient of determination indicates a more rapid time scale of variability (e.g. syllable B relative to syllable A). c. Actual learning in syllables A and B, compared with predictions of learning calculated as before. d. We compared the actual learning for a syllable with predictions based on FF variation for that syllable (“syllable-specific” variation, as before) or predictions based on FF variation for syllables targeted in all other experiments (“general” variation). Prediction error was quantified as mean distance from the actual structure of learning. Predictions using syllable-specific variation had significantly less error than predictions using general variation (Wilcoxon signed-rank test; p=0.005, n=28; horizontal lines denote means).
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getmorefigures.php?uid=PMC3045469&req=5

Figure 5: Inter-syllable differences in the structure of variation predict differences in learninga. Natural pattern of fundamental frequency variation for two syllables, expressed as residuals from the mean. Each panel depicts the fundamental frequency performance on 30 consecutive renditions of the syllable during baseline song. The variability for syllable B (green) appears to exhibit faster temporal fluctuations than for syllable A (gray). b. The time scale of variability for syllables A (gray) and B (green, dashed). Coefficient of determination (r2) traces depict the extent to which a deviation from the mean at a specific time in the syllable determines the deviations from the mean at surrounding times in the syllable. A more rapid decay in the coefficient of determination indicates a more rapid time scale of variability (e.g. syllable B relative to syllable A). c. Actual learning in syllables A and B, compared with predictions of learning calculated as before. d. We compared the actual learning for a syllable with predictions based on FF variation for that syllable (“syllable-specific” variation, as before) or predictions based on FF variation for syllables targeted in all other experiments (“general” variation). Prediction error was quantified as mean distance from the actual structure of learning. Predictions using syllable-specific variation had significantly less error than predictions using general variation (Wilcoxon signed-rank test; p=0.005, n=28; horizontal lines denote means).
Mentions: If the average structure of successful behavior indeed determines the structure of learning, then syllables with different patterns of fundamental frequency variation should exhibit different learning. Here, we found that some syllables exhibited slower fundamental frequency fluctuations than others (Fig. 5a–b) and we took advantage of this to test whether these natural differences in variation predict differences in learning.

Bottom Line: However, successful performance of many motor skills, such as speech articulation, also requires learning behavioral trajectories that vary continuously over time.A simple principle predicted the detailed structure of learning: birds learned to produce the average of the behavioral trajectories associated with successful outcomes.This learning rule accurately predicted the structure of learning at a millisecond timescale, demonstrating that the nervous system records fine-grained details of successful behavior and uses this information to guide learning.

View Article: PubMed Central - PubMed

Affiliation: W M Keck Center for Integrative Neuroscience, University of California, San Francisco, California, USA. jcharles@phy.ucsf.edu

ABSTRACT
Reinforcement signals indicating success or failure are known to alter the probability of selecting between distinct actions. However, successful performance of many motor skills, such as speech articulation, also requires learning behavioral trajectories that vary continuously over time. Here, we investigated how temporally discrete reinforcement signals shape a continuous behavioral trajectory, the fundamental frequency of adult Bengalese finch song. We provided reinforcement contingent on fundamental frequency performance only at one point in the song. Learned changes to fundamental frequency were maximal at this point, but also extended both earlier and later in the fundamental frequency trajectory. A simple principle predicted the detailed structure of learning: birds learned to produce the average of the behavioral trajectories associated with successful outcomes. This learning rule accurately predicted the structure of learning at a millisecond timescale, demonstrating that the nervous system records fine-grained details of successful behavior and uses this information to guide learning.

Show MeSH
Related in: MedlinePlus