Multiplex lexical networks reveal patterns in early word acquisition in children

Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N = 529 words/nodes are connected according to four relationship: (i) free association, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We investigate the topology of the resulting multiplex and then proceed to evaluate single layers and the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the multiplex topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex structure is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition

Small Worlds and Semantic Network Growth in Typical and Late Talkers

Network analysis has demonstrated that systems ranging from social networks to electric power grids often involve a small world structure-with local clustering but global ac cess. Critically, small world structure has also been shown to characterize adult human semantic networks. Moreover, the connectivity pattern of these mature networks is consistent with lexical growth processes in which children add new words to their vocabulary based on the structure of the language-learning environment. However, thus far, there is no direct evidence that a child's individual semantic network structure is associated with their early language learning. Here we show that, while typically developing children's early networks show small world structure as early as 15 months and with as few as 55 words, children with language delay (late talkers) have this structure to a smaller degree. This implicates a maladaptive bias in word acquisition for late talkers, potentially indicating a preference for “oddball” words. The findings provide the first evidence of a link between small-world connectivity and lexical development in individual children

Predictive Modeling to Capture the Words a Toddler Will Learn Next

Network models of language provide a systematic way of linking a child’s current vocabulary knowledge processes to the structure and connectivity of properties of language which promote future lexical learning. Using network growth models, we explore the relational role of language and the influence of linguistic structure on language learning. Previous research has proposed that language is learned by a process of semantic differentiation that can be modeled through a network process of preferential attachment, with highly connected nodes being learned earliest. This model accounts for high-level lexical network structure and also captures empirical age of acquisition reports. Alternately, language learning may be driven by contextual diversity, or the diverse contexts and meanings of unknown words in the environment. In this thesis, we test these and other ideas by extending these models to acquisition trajectories of individual children, predicting the individual words a child is likely to learn next. We explore how the definition of a graph, the assumed network growth process, and measures of node importance affect our ability to model acquisition. We not only construct a theoretical framework for network models of acquisition but also test the ability of these models to account for learning and development. This work suggests that network models provide a framework for understanding the cognitive and developmental processes of language acquisition. Neural network models, often called connectionist models, offer another independent approach to modeling learning and development. We focus on associations in a child’s current vocabulary that might be relevant and even facilitatory to the learning process of young children by constructing predictive models. The associative learning framework of our neural network models allow for different types and timescales of learning to be captured. A key idea to data-driven neural network models of acquisition is that there are strong similarities among the way in which children learn, but the differences between children are also predictive. Assuming that there are different types of language learners and that the vocabulary (together with child age) at any time point reflects the type of learner a particular child is, machine learning models can provide a powerful and predictive tool to aid with classification and diagnostics of a child’s learning trajectory. Focusing specifically on using a child’s vocabulary to predict future lexical learning. We explore a variety of representations of a child’s current vocabulary knowledge, including those from a productive vocabulary report as well as representations based on natural language processing algorithms, adult norms, and phonemic content. We find that individual words in a child’s vocabulary are informative in predicting future vocabulary growth using a neural network model. These results additionally suggest the need to consider differences amongst learners. Our best performing model has information not only about a child’s own vocabulary knowledge but also about the normative acquisition trends of words in that child’s vocabulary. These two types of information improve predictive accuracy and suggest potential diagnostic and interventional tools for helping bridge the lexical differences of language delayed children and their age-matched peers

Small Worlds and Semantic Network Growth in Typical and Late Talkers

Network analysis has demonstrated that systems ranging from social networks to electric power grids often involve a small world structure-with local clustering but global ac cess. Critically, small world structure has also been shown to characterize adult human semantic networks. Moreover, the connectivity pattern of these mature networks is consistent with lexical growth processes in which children add new words to their vocabulary based on the structure of the language-learning environment. However, thus far, there is no direct evidence that a child's individual semantic network structure is associated with their early language learning. Here we show that, while typically developing children's early networks show small world structure as early as 15 months and with as few as 55 words, children with language delay (late talkers) have this structure to a smaller degree. This implicates a maladaptive bias in word acquisition for late talkers, potentially indicating a preference for “oddball” words. The findings provide the first evidence of a link between small-world connectivity and lexical development in individual children

Multiplex lexical networks reveal patterns in early word acquisition in children

Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N = 529 words/nodes are connected according to four relationship: (i) free association, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We investigate the topology of the resulting multiplex and then proceed to evaluate single layers and the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the multiplex topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex structure is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition

Testing sequential patterns in human mate choice using speed dating

Choosing appropriate mates from the sequential stream of possible partners we encounter over time is a crucial and challenging adaptive problem. But getting data on mate search is also challenging. Speed-dating provides an accelerated microcosm of such data which we can use to test models of sequential mate search. Here we use such data to assess search heuristics including fixed threshold models and mechanisms that adjust aspiration levels for mates in response to previous experiences of success or failure on the mating market. We find that initial thresholds related to own attractiveness combined with experience-based threshold adjustment can account for most of the offers made during speed-dating