SCALa: A blueprint for computational models of language acquisition in social context

Abstract

Theories and data on language acquisition suggest a range of cues are used, ranging from information on structure found in the linguistic signal itself, to information gleaned from the environmental context or through social interaction. We propose a blueprint for computational models of the early language learner (SCALa, for Socio-Computational Architecture of Language Acquisition) that makes explicit the connection between the kinds of information available to the social learner and the computational mechanisms required to extract language-relevant information and learn from it. SCALa integrates a range of views on language acquisition, further allowing us to make precise recommendations for future large-scale empirical research.

A systematic review suggests marked differences in the prevalence of infant‐directed vocalization across groups of populations

“…Infant‐directed speech may also be a way in which caregivers respond to children's needs and signals, and thus promote attachment according to some theories (e.g., Ainsworth, 1979; Bowlby, 1986; but see Keller, 2016). However, for both learning about the (social) world and attachment, infants’ key experiences include non‐verbal communicative signals, which would need to be integrated to make testable predictions regarding potential consequences of any diversity in experiences (Tsuji et al., 2021).…”

“…However, for both learning about the (social) world and attachment, infants' key experiences include non-verbal communicative signals, which would need to be integrated to make testable predictions regarding potential consequences of any diversity in experiences (Tsuji et al, 2021).…”

Communicative Feedback in language acquisition

“…Besides, we believe such models should not focus exclusively on CF. Children learn both from the statistical regularities of the input and from social interaction; a helpful model of language acquisition should ideally integrate and contrast both components Lowe et al, 2020;Tsuji et al, 2021). For example, provided a computational proof of concept using reinforcement learning as instantiation of CF in the case of meaning learning.…”

Realistic and broad-scope learning simulations: first results and challenges

“…However, there has been surprisingly slow progress in solving some long-standing controversies regarding the basic mechanisms that underlie language acquisition. For instance, do infants learn language primarily from extracting statistics over speech inputs (Romberg & Saffran, 2010; Saffran & Kirkham, 2018), from examining cross-situational correlations over multisensory inputs (Smith & Yu, 2008; Suanda, Mugwanya & Namy, 2014; Yu & Smith, 2017; Zhang, Chen & Yu, 2019), or by relying on social interactions and feedback (Tomasello, 2003; Tsuji, Cristia & Dupoux, 2021; Yu & Ballard, 2007)? Do they learn by leveraging discrete linguistic categories or continuous sensory representations (Kuhl et al, 2008; McMurray, 2021)?…”

“…Here, far from making a comprehensive survey of these approaches, we attempt to classify them into types and sort them along dimensions that outline their respective strengths and weaknesses with regard to addressing basic questions/controversies about learning mechanisms. Familiar types are verbal frameworks (among others: The competition model: MacWhinney & MacWhinney, 1987; WRAPSA: Jusczyk, 1993; Usage-based theory: Tomasello, 2005; NML-e: Kuhl et al, 2008; PRIMIR: Werker & Curtin, 2005), which weave a narrative around a large body of experimental research using verbally defined concepts, sometimes complemented by box-and-arrow schemas (e.g., the ScALA framework from Tsuji et al, 2021). Correlational approaches (e.g., Fernald, Marchman & Weisleder, 2013; Hart & Risley, 1995; Swingley & Humphrey, 2018) aim to identify the main variables that predict language development outcomes through statistical models.…”