The Development of Structured Vocalizations in Songbirds and Humans: A Comparative Analysis

Lipkind, Dina; Geambaşu, Andreea; Levelt, Clara C.

doi:10.1111/tops.12414

Cited by 17 publications

(22 citation statements)

References 64 publications

(75 reference statements)

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“…Although some studies found evidence for a language ability-aggression relation at around 18 months (Dionne et al, 2003;Girard et al, 2014), research into this association during infancy and early toddlerhood is limited. Reason for this might be that the assessment of language development this early (i.e., during infancy), which focuses on babbling, vocalizing, and comprehension and production of first words (Lipkind et al, 2019), is very complex in itself, thereby leaving a much smaller number of studies that link these abilities to behavior problems.…”

Section: Introductionmentioning

confidence: 99%

The Role of Inhibitory Control, Attention and Vocabulary in Physical Aggression Trajectories From Infancy to Toddlerhood

et al. 2020

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Physical aggression has its origin very early in development, but no studies to date have examined physical aggression trajectories starting before the age of 1.5 years. This study examined whether cognition plays a role in the development of physical aggression from infancy onward. In a sample of 182 mother-child dyads (94 boys; 88 girls), child physical aggression was assessed by maternal report using the Physical Aggression Scale for Early Childhood at 12, 20, and 30 months. Children performed cognitive tasks measuring inhibitory control and attention, and mothers rated children's vocabulary at 12 and 30 months. Results showed that differential development of physical aggression already starts at 12 months of age: low-stable, low-increasing, moderate-decreasing and high-stable trajectory groups were identified. Inhibitory control, attention and vocabulary at 12 months and development of these abilities from 12 to 30 months were selectively related to the likelihood of following the low-increasing and moderate-decreasing trajectories compared to the low-stable physical aggression trajectory. This study is the first to show that specific aspects of cognition and cognitive development are related to differential physical aggression development from infancy onward.

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Section: Introductionmentioning

confidence: 99%

The Role of Inhibitory Control, Attention and Vocabulary in Physical Aggression Trajectories From Infancy to Toddlerhood

et al. 2020

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“…The way in which these vocal units emerge shows remarkable similarities between infants and zebra finches (a much used model species for examining vocal learning), and these observations indicate an important role for motor variability in both species. In contrast to what is commonly assumed, Lipkind et al () suggest that songbird subsong and its development into a more structured song is more comparable to the phonation stage in infants than to human babbling. Observational and experimental data on the development of vocal unit combinations show more parallels between the species, like the transitioning from a repetitive to a diverse production of units.…”

Section: Insights From Comparative Agl Studiesmentioning

confidence: 79%

“…The above‐mentioned studies focus on the abilities for detecting and discriminating structural patterns using some kind of perceptual discrimination task. A different comparative approach is taken by Lipkind et al (). They focus on development of sequences in sound production in human infants and songbirds.…”

Section: Insights From Comparative Agl Studiesmentioning

confidence: 99%

“…Observational and experimental data on the development of vocal unit combinations show more parallels between the species, like the transitioning from a repetitive to a diverse production of units. Finally, Lipkind et al () argue that the idea that words and song motifs are not directly comparable (Yip, ) should be reappraised, based on observed similarities between the development of these fixed sequences of units in birds and infants.…”

Section: Insights From Comparative Agl Studiesmentioning

confidence: 99%

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Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross‐Species, and Computational Approaches

Cate

Gervain

Levelt

et al. 2020

Topics in Cognitive Science

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Human languages all have a grammar, that is, rules that determine how symbols in a language can be combined to create complex meaningful expressions. Despite decades of research, the evolutionary, developmental, cognitive, and computational bases of grammatical abilities are still not fully understood. “Artificial Grammar Learning” (AGL) studies provide important insights into how rules and structured sequences are learned, the relevance of these processes to language in humans, and whether the cognitive systems involved are shared with other animals. AGL tasks can be used to study how human adults, infants, animals, or machines learn artificial grammars of various sorts, consisting of rules defined typically over syllables, sounds, or visual items. In this introduction, we distill some lessons from the nine other papers in this special issue, which review the advances made from this growing body of literature. We provide a critical synthesis, identify the questions that remain open, and recognize the challenges that lie ahead. A key observation across the disciplines is that the limits of human, animal, and machine capabilities have yet to be found. Thus, this interdisciplinary area of research firmly rooted in the cognitive sciences has unearthed exciting new questions and venues for research, along the way fostering impactful collaborations between traditionally disconnected disciplines that are breaking scientific ground.

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“…Supporting this view, it has been found that learning the motifs and syntax involves independent mechanisms (Lipkind et al 2017). The computational study of hierarchical structures and compositional behaviour can also lead to insights into the development of human locomotion and language as there are striking conceptual parallels (Dominici et al 2011;Lipkind et al 2013;Ding et al 2015;Lipkind et al 2019).…”

Section: Introductionmentioning

confidence: 94%

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Maes

Barahona

Clopath

2020

Preprint

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Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

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The Development of Structured Vocalizations in Songbirds and Humans: A Comparative Analysis

Cited by 17 publications

References 64 publications

The Role of Inhibitory Control, Attention and Vocabulary in Physical Aggression Trajectories From Infancy to Toddlerhood

The Role of Inhibitory Control, Attention and Vocabulary in Physical Aggression Trajectories From Infancy to Toddlerhood

Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross‐Species, and Computational Approaches

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

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