Abstract
Natural behaviors, such as foraging, tool use, social interaction, birdsong, and language, exhibit branching sequential structure. Such structure should be learnable if it can be inferred from the statistics of early experience. We report that juvenile zebra finches learn such sequential structure in song. Song learning in finches has been extensively studied, and it is generally believed that young males acquire song by imitating tutors (Zann, 1996). Variability in the order of elements in an individual’s mature song occurs, but the degree to which variation in a zebra finch’s song follows statistical regularities has not been quantified, as it has typically been dismissed as production error (Sturdy et al., 1999). Allowing for the possibility that such variation in song is non-random and learnable, we applied a novel analytical approach, based on graph-structured finite-state grammars, to each individual’s full corpus of renditions of songs. This method does not assume syllable-level correspondence between individuals. We find that song variation can be described by probabilistic finite-state graph grammars that are individually distinct, and that the graphs of juveniles are more similar to those of their fathers than to those of other adult males. This grammatical learning is a new parallel between birdsong and language. Our method can be applied across species and contexts to analyze complex variable learned behaviors, as distinct as foraging, tool use, and language.
Highlights
In altricial species developing individuals are often surrounded by a highly structured environment
The finding that in zebra finch song collocations of syllables (COL)+i is more appropriate than the other candidate grammars that we considered, including, in particular, COL-i, suggests that in analyzing a bird’s song it is prudent to avoid dismissing out of hand parts of the song corpus such as the introductory notes and any other potential sources of non-random and perhaps meaningful variability
The second of the two additional measures of distance between graphs, CNAFeat, is based on a family of graph features used in computational network analysis (CNA); the particular features we considered have been used for characterizing brain dynamics and are part of the Brain Connectivity Toolbox (BCT; Rubinov and Sporns, 2010)
Summary
In altricial species developing individuals are often surrounded by a highly structured environment. To compare statistical regularities in behaviors among multiple subjects, one needs computational tools capable of (i) detecting and describing the structure of behavior and (ii) comparing the results across individuals. When used together, these tools can reveal common patterns, quantify individual differences, and, for acquired behaviors, help elucidate the mechanisms of learning (see, e.g., Visser et al, 2007). These tools can reveal common patterns, quantify individual differences, and, for acquired behaviors, help elucidate the mechanisms of learning (see, e.g., Visser et al, 2007) We used two such tools – a group of models of grammar acquisition that is
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