Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Moving word learning to a novel space: A dynamic systems view of referent selection and retention

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child’s active engagement with objects and people supports referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children’s knowledge of words and objects grows. Together this work suggests understanding systems for perception, action, attention, and memory and their complex interaction is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work

Slowing down fast mapping:Redefining the dynamics of word learning

In this article, we review literature on word learning and propose a theoretical account of how lexical knowledge and word use emerge and develop over time. We contend that the developing lexical system is built on processes that support children's in-the-moment word usage interacting with processes that create long-term learning. We argue for a new characterization of word learning in which simple mechanisms like association and competition, and the interaction between the two, guide children's selection of referents and word use in the moment. This in turn strengthens and refines the network of relationships in the lexicon, improving referent selection and use in future encounters with words. By integrating in-the-moment word use with long-term learning through simple domain-general mechanisms, this account highlights the dynamic nature of word learning and creates a broader framework for understanding language and cognitive development more generally

Remembering New Words: Integrating Early Memory Development into Word Learning

In order to successfully acquire a new word, young children must learn the correct associations between labels and their referents. For decades, word-learning researchers have explored how young children are able to form these associations. However, in addition to learning label-referent mappings, children must also remember them. Despite the importance of memory processes in forming a stable lexicon, there has been little integration of early memory research into the study of early word learning. After discussing what we know about how young children remember words over time, this paper reviews the infant memory development literature as it relates to early word learning, focusing on changes in retention duration, encoding, consolidation, and retrieval across the first 2 years of life. A third section applies this review to word learning and presents future directions, arguing that the integration of memory processes into the study of word learning will provide researchers with novel, useful insights into how young children acquire new words

Goldilocks Forgetting in Cross-Situational Learning

Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a "Goldilocks" zone of forgetting: an optimum store-loss ratio that is neither too aggressive nor too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, into the language acquisition process

Word learning and executive functions in preschool children : bridging the gap between vocabulary acquisition and domain-general cognitive processes

Language development in children depends on domain-specific language mechanisms, biases and domain-general cognitive development (i.e., executive functions [EFs]). Additionally, both language proficiency and EFs underpin new learning and predict academic success and lifelong wellbeing. However, despite the intuitive assumption that EFs are involved in the development of language, the relationship between word-learning abilities and EFs is not fully understood. Therefore, the present thesis addresses this gap by examining novel word learning under three different scenarios integrating three main EF components. The current thesis investigated 1) whether children learn and retain words differently depending on the word-learning scenario, and 2) whether EFs in the non-linguistic domain predict word learning in children. More specifically, the present study assessed the impact of three different word-learning scenarios and EF measurements on novel word learning outcomes in 4-year-old children in Greater Sydney, Australia. Participants were 47 children from diverse language backgrounds, including monolinguals (n= 28) and heterogeneous bilinguals (n=19). The present study demonstrates that 4-year-old children are successful at learning words across three word-learning scenarios: Mutual Exclusivity (ME), Cross Situational Word Learning (CSW)L and an eBook. Crucially, different word-learning scenarios foster different learning outcomes, with eBook reading and disambiguation via ME facilitating rapid and more accurate word learning, while CSWL yielded less success. We conclude that at this crucial age prior to entering formal schooling in Australia, children benefit from contextual information and referential input during the word-learning experience. Four-year-olds easily disambiguate and learn novel label-to-referent associations when presented alongside a familiar referent in virtue of the ME assumption. They also successfully activate attentional resources to detect and learn novel label-to-referent associations among abundant visual and auditory input when listening and observing a colourful eBook. These findings should be considered in early childhood education settings to support lexical acquisition in children. In addition, findings point out a bidimensional structure of EF in 4-year-old children, with one of the dimensions corresponding to a composite construct comprised of inhibition and flexibility, while the other dimension corresponds to working memory. However, our analyses did not reveal visuospatial memory, inhibition or flexibility as significant predictors for any of the word-learning scenarios. Altogether, the present thesis advances the knowledge of children’s cognitive structure and their relationship with different word-learning scenarios, providing foundations to help further bridge the research gap between word learning and cognitive processes

The advent and fall of a vocabulary learning bias from communicative efficiency

Biosemiosis is a process of choice-making between simultaneously alternative options. It is well-known that, when sufficiently young children encounter a new word, they tend to interpret it as pointing to a meaning that does not have a word yet in their lexicon rather than to a meaning that already has a word attached. In previous research, the strategy was shown to be optimal from an information theoretic standpoint. In that framework, interpretation is hypothesized to be driven by the minimization of a cost function: the option of least communication cost is chosen. However, the information theoretic model employed in that research neither explains the weakening of that vocabulary learning bias in older children or polylinguals nor reproduces Zipf’s meaning-frequency law, namely the non-linear relationship between the number of meanings of a word and its frequency. Here we consider a generalization of the model that is channeled to reproduce that law. The analysis of the new model reveals regions of the phase space where the bias disappears consistently with the weakening or loss of the bias in older children or polylinguals. The model is abstract enough to support future research on other levels of life that are relevant to biosemiotics. In the deep learning era, the model is a transparent low-dimensional tool for future experimental research and illustrates the predictive power of a theoretical framework originally designed to shed light on the origins of Zipf’s rank-frequency law.DCC and RFC are supported by the grant TIN2017-89244-R from MINECO (Ministerio de Economía, Industria y Competitividad). RFC is also supported by the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Peer ReviewedPostprint (published version

Global and local statistical regularities control visual attention to object sequences

Many previous studies have shown that both infants and adults are skilled statistical learners. Because statistical learning is affected by attention, learners' ability to manage their attention can play a large role in what they learn. However, it is still unclear how learners allocate their attention in order to gain information in a visual environment containing multiple objects, especially how prior visual experience (i.e., familiarly of objects) influences where people look. To answer these questions, we collected eye movement data from adults exploring multiple novel objects while manipulating object familiarity with global (frequencies) and local (repetitions) regularities. We found that participants are sensitive to both global and local statistics embedded in their visual environment and they dynamically shift their attention to prioritize some objects over others as they gain knowledge of the objects and their distributions within the task

Logical word learning: The case of kinship

We examine the conceptual development of kinship through the lens of program induction. We present a computational model for the acquisition of kinship term concepts, resulting in the first computational model of kinship learning that is closely tied to developmental phenomena. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish, and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We then conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to understand the influence of logical simplicity and children’s learning environment on the order of acquisition of kinship terms, providing novel predictions for the learning trajectories of these words. We conclude with a discussion of how this model framework generalizes beyond kinship terms, as well as a discussion of its limitations

From simple to complex categories: how structure and label information guides the acquisition of category knowledge

Categorization is a fundamental ability of human cognition, translating complex streams of information from the all of different senses into simpler, discrete categories. How do people acquire all of this category knowledge, particularly the kinds of rich, structured categories we interact with every day in the real-world? In this thesis, I explore how information from category structure and category labels influence how people learn categories, particular for the kinds of computational problems that are relevant to real-world category learning. The three learning problems this thesis covers are: semi-supervised learning, structure learning and category learning with many features. Each of these three learning problems presents a different kinds of learning challenge, and through a combination of behavioural experiments and computational modeling, this thesis illustrates how the interplay between structure and label information can explain how humans can acquire richer kinds of category knowledge.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Psychology, 201