Abstract

It is widely argued that personalized instruction based on individual differences in learning styles or genetic predispositions could improve learning outcomes. However, this proposition has resisted clear demonstration in human studies, where it is difficult to control experience and quantify outcomes. Here, we take advantage of the tractable nature of vocal learning in songbirds (Lonchura striata domestica) to test the idea that matching instruction to individual genetic predispositions can enhance learning. We use both cross-fostering and computerized instruction with synthetic songs to demonstrate that matching the tutor song to individual predispositions can improve learning across genetic backgrounds. Moreover, we find that optimizing instruction in this fashion can equalize learning differences across individuals that might otherwise be construed as genetically determined. Our results demonstrate potent, synergistic interactions between experience and genetics in shaping song, and indicate the likely importance of such interactions for other complex learned behaviors.

Highlights

  • Recent studies in human populations have demonstrated strong genetic influences on academic achievement (Branigan et al, 2013; Lee et al, 2018; Okbay et al, 2016; Rietveld et al, 2013)

  • Within our genetically heterogeneous Bengalese finch colony, we found that there was a broad range in the quality of song learning

  • We quantified song learning using the Song Divergence (SD), a measure that estimates how much of the spectral content of syllables in the tutor song is absent from the learned song (Mets and Brainard, 2018b); an SD of 0

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Summary

Introduction

Recent studies in human populations have demonstrated strong genetic influences on academic achievement (Branigan et al, 2013; Lee et al, 2018; Okbay et al, 2016; Rietveld et al, 2013). This raises the question of whether genes place immutable bounds on achievement or whether experiential factors could amplify or dampen the impact of genetic variation. Birdsong affords an attractive system for studying how tailoring instruction based on genetic differences influences learning outcomes.

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