1. Genetics and Genomics
  2. Neuroscience
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Learning is enhanced by tailoring instruction to individual genetic differences

  1. David G Mets  Is a corresponding author
  2. Michael S Brainard  Is a corresponding author
  1. University of California, San Francisco, United States
  2. Howard Hughes Medical Institute, University of California, San Francisco, United States
Research Article
Cite this article as: eLife 2019;8:e47216 doi: 10.7554/eLife.47216
4 figures and 1 additional file


There is broad variation in the quality of learning across individuals.

(A) Spectrograms illustrating learning of song from different tutors. Adult song is composed of a series of discrete units of sound (syllables) separated by silence, with each syllable containing characteristic spectral content. Two fathers’ songs (top: i and ii) had distinct syllable structure that was well learned by their offspring (bottom: i and ii). (B) Spectrograms of songs exemplifying the range of variation in learning present under conventional rearing and tutoring conditions. Similarity between the learned songs of individuals (Tutee songs) and the song of their tutor (Tutor song) is indicated by Song Divergence (SD) scores at left. SD indicates spectral content from a tutor song that is missing from the learned song, such that lower SD scores indicate better learning (see Materials and methods). The song of a bird that had no exposure to a tutor (isolate song) is shown for comparison. (C) The distribution of learning outcomes for 142 birds that were reared and tutored in their home nests. A gamma distribution fit to the data (blue line) and the 50th percentile (green) and 80th percentile (red) indicate that learning outcomes were skewed towards good learning (smaller SD values). Red and black arrows correspond to the learned ‘tutee’ songs and ‘isolate’ song presented in panel B.

Figure 2 with 1 supplement
Learning outcomes are better when juveniles are tutored by their genetic fathers.

(A) Schematic representation of the experimental design. Parental pairs (middle row, blue birds) reared both their own genetic offspring (home-reared, left), and foster birds from different genetic backgrounds (cross-fostered, right). (B) Examples of learning outcomes for two nests. (B, top) Example songs from the resident male tutors in each nest. (B, middle, bottom) Example songs from birds that were either home-reared (B, middle) or cross-fostered (B, bottom) in nests where males sang the tutor songs indicated in B, top. The examples of learned songs are from birds that had the median Song Divergence (SD) scores for their cohort (home-reared or cross-fostered). Syllable labels are provided to facilitate comparisons, but do not reflect automated SD scores used to quantify song similarity. (C) Paired plot of median SD scores for home-reared and cross-fostered cohorts. Across eight nests, birds that were home-reared learned significantly better than birds that were cross-fostered (n = 8 parental pairs, 52 cross-fostered birds, 45 home-reared birds; Wilcoxon signed-rank test, p<0.005). Median SD scores for nests corresponding to spectrograms in panel B are shown in red.

Figure 2—figure supplement 1
Pedigree of all tutor pairs used in cross-fostering experiments.

Squares indicate male birds while circles indicate female birds. Colored symbols indicate members of breeding pairs that were used as tutoring families for cross-foster experiments presented in Figure 2. Membership in a specific breeding pair is indicted by shared color and shared numbers. Triangle symbols indicate purchases from an outside vendor. A total of five purchases from outside vendors are indicated by numbers. Purchases were made over a period of 7 years ranging from 2001 to 2007.

Learning outcomes are influenced by the relationship between tutor song tempo and genetic bias.

(A) Schematic of computer tutoring. Individuals from different genetic backgrounds (red and blue nests) were raised by non-singing females to prevent exposure to their fathers’ songs and were then tutored using computer-controlled playback of identical tutor stimuli. (B, top) Spectrogram of a full motif from the synthetic computer tutor song with a tempo of 8.5 syl/s. (B, bottom) Example songs for three tutees from different nests displaying variation in learning outcomes for song spectral content. The SD scores for the song repertoires of these individual birds are shown next to the example spectrograms. These tutees (from top to bottom) came from nests where the fathers’ song tempos were similar to (8.5 syl/s), faster than (9.8 syl/s), and slower than (7.2 syl/s) the tutor song. (C) The distribution of learning outcomes for 20 birds drawn from 13 different genetic backgrounds. A gamma distribution fit to the data (blue line) and the 50th percentile (orange) and 80th percentile (green) indicate that learning outcomes were skewed towards better learning. Red arrows correspond to the learned songs presented in panel B. (D) Mean (gray bars) and median (red dots) SD scores for birds that were grouped by the tempos of their genetic fathers’ songs. Learning was best for birds that had a genetic bias to sing near the tempo of the tutor song (fathers’ song tempos from 7.5 to 9.5 syl/s), while learning was worse for birds that were biased to sing slower than the tutor song (<7.5 syl/s; * denotes p<0.02, one-tailed t-test) or faster than the tutor song (>9.5 syl/s; p=0.06, one-tailed t-test). Additionally, birds that were biased to sing faster than the tutor song learned better than birds that were biased to sing slower than the tutor song (** denotes p<0.02, two-tailed t-test). All p-values were corrected for multiple testing using the Holm-Bonferroni procedure.

Figure 4 with 3 supplements
Matching tutor song tempo to the genetic bias of an individual results in better learning.

(A) Cohorts of individuals from ‘slow’, ‘medium’ and ‘fast’ genetic backgrounds (left) were tutored with ‘slow’, ‘medium’ and ‘fast’ synthetic tutor songs (bottom). The normalized, average Song Divergence for each of the nine cohorts is indicated in grayscale, with darker shades indicating better learning. (B) Across the three genetic backgrounds, learning outcomes were best when tutor-song tempo and genetic bias were matched (purple vs. orange and green). If unmatched, tutor song was learned better if tutor song was slower than genetic bias for song tempo (orange vs. green). For all panels, error bars are standard error. ** denotes p<0.02, two-tailed t-test. All p-values were corrected for multiple testing using Holm-Bonferroni procedure.

Figure 4—figure supplement 1
Birds from a genetic background biased to sing at a medium song tempo learn best when provided with a tutor song at a medium song tempo.

(A) Spectrograms of each unique syllable that made up the synthetic tutor song, and corresponding labels for identification. Synthetic tutor songs that differed in tempo were constructed by varying the gaps between syllables, without altering either the durations or spectral content of the syllables themselves. (B) Examples of learned songs produced by three birds from a genetic background biased to sing at a medium song tempo (7.8 syl/s) that were tutored on songs with slow, medium or fast tempos (Figure 4A, ‘medium’ population). Although syllables from the tutor song were recognizable in each of the learned songs, the best learning was for the individual that was tutored with a medium tempo song (8.5 syl/s), most closely matched to the genetic background. Summary data are quantified in Figure 4 and Figure 4—figure supplement 1.

Figure 4—figure supplement 2
Quantification of song learning for specific tutor-song tempo father-song tempo pairings.

(A) Cohorts of individuals from each of three genetic backgrounds (‘slow’, cyan; ‘medium’, red; ‘fast’, blue) were tutored with songs of one of three tempos (‘slow’, 6.5 syl/s; ‘medium’, 8.5 syl/s; ‘fast’, 10.5 syl/s). For each of the three genetic backgrounds, birds that heard a tutor song that was matched to their genetic background learned best. Bars indicate the average Song Divergence for birds from each of the nine categorical combinations of tutor-song tempo and father-song tempo (as shown in Figure 4A). While the average learning across all tutor song tempos was worse for the slow genetic background than for the fast genetic background, the slow birds actually learned better than the fast birds when each group was provided with the optimal tutor song for that group (compare slow birds tutored at 6.5 syl/s with fast birds tutored at 10.5 syl/s). (B) Genetic background (father’s song tempo) considered alone explained only 4.1% of the variation in SD scores across the population of tutored birds. (C) Tutor song tempo (slow, medium or fast) considered alone explained only 0.28% of the variation in SD scores. For all panels, error bars indicate standard error.

Figure 4—figure supplement 3
Birds did not deviate from good syllable copying in order to optimize song tempo.

Data are from 20 birds tutored with a synthetic song that had a tempo of 8.5 syl/s. These individuals were drawn from a diversity of genetic backgrounds. (A) Quality of song copying (Song Divergence in bits) as a function of learned song tempo. If birds were learning faster songs at the expense of good syllable copying, we would expect increasing Song Divergence as song tempo increases, however this trend is not apparent (expectation indicated in inset). (B) Quality of song copying as a function of the absolute difference between the learned song tempo and the tutor song tempo. If birds were learning to match the tutor song tempo at the expense of good syllable copying, we would expect the songs with the smallest difference between learned tempo and tutor tempo to have the largest SD scores (expectation indicated in inset). In contrast, a regression between these two variables (red) results in a significant positive slope. This regression was fit with the ordinary least squares algorithm and results in a slope of 0.577, with a p value of 0.0013, and an r-squared of 0.446.


Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

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