A Compact Statistical Model of the Song Syntax in Bengalese Finch

Jin, Dezhe Z.; Кожевников, А. А.

doi:10.1371/journal.pcbi.1001108

Cited by 69 publications

(130 citation statements)

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“…That study did find decreases in syllable repetition rate, although a specific correlation with gap duration was not reported for those transitions. Furthermore, another modeling study of Bengalese finch song has suggested that syllable repetitions are controlled by a process (adaptation) that is separate from what controls other syllable transitions (Jin and Kozhevnikov 2011). On this basis, one may not expect the transition-timing correlation among syllable repeats per se but rather among other transitions; such an expectation implies other changes to transition structure in aging Bengalese finch song.…”

Section: Discussionmentioning

confidence: 99%

Development of temporal structure in zebra finch song

Glaze

Troyer

2013

Journal of Neurophysiology

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Glaze CM, Troyer TW. Development of temporal structure in zebra finch song. J Neurophysiol 109: 1025-1035, 2013. First published November 21, 2012 doi:10.1152/jn.00578.2012.-Zebra finch song has provided an excellent case study in the neural basis of sequence learning, with a high degree of temporal precision and tight links with precisely timed bursting in forebrain neurons. To examine the development of song timing, we measured the following four aspects of song temporal structure at four age ranges between 65 and 375 days posthatch: the mean durations of song syllables and the silent gaps between them, timing variability linked to song tempo, timing variability expressed independently across syllables and gaps, and transition probabilities between consecutive syllable pairs. We found substantial increases in song tempo between 65 and 85 days posthatch, due almost entirely to a shortening of gaps. We also found a decrease in tempo variability, also specific to gaps. Both the magnitude of the increase in tempo and the decrease in tempo variability were correlated on gap-by-gap basis with increases in the reliability of corresponding syllable transitions. Syllables had no systematic increase in tempo or decrease in tempo variability. In contrast to tempo parameters, both syllables and gaps showed an early sharp reduction in independent variability followed by continued reductions over the first year. The data suggest that links between syllable-based representations are strengthened during the later parts of the traditional period of song learning and that song rhythm continues to become more regular throughout the first year of life. Similar learning patterns have been identified in human sequence learning, suggesting a potentially rich area of comparative research. temporal structure; learning; birdsong; generative model SEQUENCE LEARNING is one of the touchstone questions in neuroscience (e.g., Lashley 1951; Hikosaka et al. 2002;Keele et al. 2003;Rhodes et al. 2004), and song learning in zebra finches has served as an excellent model system for understanding sequence learning and production. Adult songs are highly stereotyped and have a well-defined temporal structure spanning multiple time scales. In the zebra finch, song learning can be roughly divided into two overlapping processes (Immelmann 1969;Marler 1970;Konishi 1985;Brainard and Doupe 2000): "sensory acquisition," in which a young bird ϳ20 -65 days posthatch (dph) is exposed to the song of one or more tutors and forms an auditory template; and "sensorimotor learning," in which the juvenile bird ϳ35-90 dph learns to produce song based on that template. Once learned, songs are composed of several repeats of 500-to 1,000-ms-long "motifs," consisting of a sequence of three to seven "syllables," with 50-to 250-ms-long stereotyped vocalizations separated by silent gaps. In adult zebra finch song, the spectral features of individual song syllables are highly stereotyped, as are the timing and sequencing of syllable production. However, few studies have focu...

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Section: Discussionmentioning

confidence: 99%

Development of temporal structure in zebra finch song

Glaze

Troyer

2013

Journal of Neurophysiology

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“…[24,33]). As with the traditional Markov models, in generating an HMM sequence, successive elements are chosen probabilistically given the current state.…”

Section: Materials and Methods (A) Description Of The Stochastic Procementioning

confidence: 99%

Animal vocal sequences: not the Markov chains we thought they were

et al. 2014

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Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.

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“…For descriptive purpose, the n-th order Markov models and its variants are most appropriate [32]. On the other hand, the HMM can provide a powerful tool to infer neural mechanisms for song sequence generation [30,33]. However, all these models are finite-state grammars and the conclusion of studies of vocal structure in birds, including those of species with extensive and elaborate singing styles such as starlings [32,34] and nightingales [3], is that neither their vocal complexity nor that of any other animal species studied to date extends beyond that of a probabilistic finite-state grammar [35] (also see Hurford [36] for further discussion).…”

Section: The Structure Of Animal Vocalizations (A) Vocal Variations Amentioning

confidence: 99%

Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning

Cate

Okanoya

2012

Phil. Trans. R. Soc. B

135

103

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The domain of syntax is seen as the core of the language faculty and as the most critical difference between animal vocalizations and language. We review evidence from spontaneously produced vocalizations as well as from perceptual experiments using artificial grammars to analyse animal syntactic abilities, i.e. abilities to produce and perceive patterns following abstract rules. Animal vocalizations consist of vocal units (elements) that are combined in a species-specific way to create higher order strings that in turn can be produced in different patterns. While these patterns differ between species, they have in common that they are no more complex than a probabilistic finite-state grammar. Experiments on the perception of artificial grammars confirm that animals can generalize and categorize vocal strings based on phonetic features. They also demonstrate that animals can learn about the co-occurrence of elements or learn simple 'rules' like attending to reduplications of units. However, these experiments do not provide strong evidence for an ability to detect abstract rules or rules beyond finite-state grammars. Nevertheless, considering the rather limited number of experiments and the difficulty to design experiments that unequivocally demonstrate more complex rule learning, the question of what animals are able to do remains open.

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A Compact Statistical Model of the Song Syntax in Bengalese Finch

Cited by 69 publications

References 44 publications

Development of temporal structure in zebra finch song

Development of temporal structure in zebra finch song

Animal vocal sequences: not the Markov chains we thought they were

Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning

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