Not Only Size Matters: Early‐Talker and Late‐Talker Vocabularies Support Different Word‐Learning Biases in Babies and Networks

Colunga, Eliana; Sims, Clare E.

doi:10.1111/cogs.12409

Cited by 35 publications

(37 citation statements)

References 35 publications

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“…is suggests the possibility that there are attentional changes during the course of learning and, potentially, that later talkers or younger children learn differently than their peers. Again, there is empirical evidence from behavioral tests with toddlers to suggest that this is the case [30][31][32] is type of network modeling framework may allow for us to not only model differences in these groups but to explain the process of acquisition that leads to these differences.…”

Section: Discussion and Future Directionmentioning

confidence: 99%

Network Growth Modeling to Capture Individual Lexical Learning

Beckage

Colunga

2019

Complexity

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Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words play a larger role in predicting network growth as these young learners add new words to their lexicon. We also examine how the importance of these semantic and phonological representations changes during the course of development. We propose a novel and significant theoretical framework for network growth models of acquisition and test the ability of these models to predict what words a specific child is likely to learn approximately one month in the future. We find that which acquisition model best fits is influenced by the underlying network representation, the assumed process of growth, and the network centrality measure used to relate the cognitive underpinnings of acquisition to network growth. The joint importance of representation, process, and the contribution of individual words to the predictive accuracy of the network model highlights the complex and multifaceted nature of early acquisition, provides new tools, and suggests experimental hypotheses for studying lexical acquisition.

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Section: Discussion and Future Directionmentioning

confidence: 99%

Network Growth Modeling to Capture Individual Lexical Learning

Beckage

Colunga

2019

Complexity

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“…Organized knowledge about word and object meanings in semantic memory has long been proposed to affect other cognitive processes (McRae & Jones, ; Tulving, ). A large literature in adults and children shows that semantic knowledge supports information encoding and retrieval (Bjorklund & Jacobs, ; Bower, Clark, Lesgold, & Winzenz, ), word learning (Beckage, Smith, & Hills, ; Colunga & Sims, ; Xu & Tenenbaum, ), language processing (Borovsky, Ellis, Evans, & Elman, , ; Federmeier & Kutas, ), inferential reasoning (Fincher‐Kiefer, ; Fisher, Godwin, & Matlen, ; Gobbo & Chi, ; Medin, Lynch, Coley, & Atran, ), and acquisition of new knowledge (Cliftion & Slowiaczek, ; Pearson, Hansen, & Gordon, ; Vosniadou & Brewer, ). These far‐reaching influences into other cognitive processes are fundamentally important early in development as they support the ability to learn and generalize new knowledge.…”

Section: Introductionmentioning

confidence: 99%

When Stronger Knowledge Slows You Down: Semantic Relatedness Predicts Children's Co‐Activation of Related Items in a Visual Search Paradigm

Vales

Fisher

2019

Cognitive Science

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A large literature suggests that the organization of words in semantic memory, reflecting meaningful relations among words and the concepts to which they refer, supports many cognitive processes, including memory encoding and retrieval, word learning, and inferential reasoning. The co‐activation of related items has been proposed as a mechanism by which semantic knowledge influences cognition, and contemporary accounts of semantic knowledge propose that this co‐activation is graded—that it depends on how strongly related the items are in semantic memory. Prior research with adults yielded evidence supporting this prediction; however, there is currently no evidence of graded co‐activation early in development. This study provides the first evidence that in children the co‐activation of related items depends on their relational strength in semantic memory. Participants (N = 84, age range: 3–9 years) were asked to identify a target (e.g., bone) amid distractors. Children's responses were slowed down by the presence of a related distractor (e.g., puppy) relative to unrelated distractors (e.g., flower)—suggesting that children co‐activated related items upon hearing the name of the target. Importantly, the degree of this co‐activation was predicted by the strength of the target–distractor relation, such that distractors more strongly related to the targets slowed down children to a larger extent. These findings have important implications for understanding how organized semantic knowledge affects other cognitive processes across development.

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“…While prior research had prioritized the importance of building a large vocabulary in toddlerhood (Lee, 2011;Marchman & Bates, 1994), an emerging area of research has begun to explore the impacts of variability in the semantic structure within the child's lexicon (Beckage et al, 2011;Colunga & Sims, 2017;Stella, Beckage, & Brede, 2017). Recent advances in network science have yielded exciting findings that suggest that the course of vocabulary development is shaped by processes that help children learn words that share similarity with their existing vocabulary and environment (Hills, Maouene, Riordan, & Smith, 2010;Hills et al, 2009 (Bergelson & Aslin, 2017;Vales & Fisher, 2019).…”

Section: Discussionmentioning

confidence: 99%

When slowing down processing helps learning: Lexico‐semantic structure supports retention, but interferes with disambiguation of novel object‐label mappings

Borovsky

2020

Developmental Science

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This project explores how children disambiguate and retain novel object-label mappings in the face of semantic similarity. Burgeoning evidence suggests that semantic structure in the developing lexicon promotes word learning in ostensive contexts, whereas other findings indicate that semantic similarity interferes with and temporarily slows familiar word recognition. This project explores how these distinct processes interact when mapping and retaining labels for novel objects (i.e., low-frequency objects that are unfamiliar to toddlers) via disambiguation from a semantically similar familiar referent in 24-month-olds (N = 65). Toddlers' log-adjusted looking to labeled target objects (relative to distractor objects) was measured in three conditions: Familiar trials (familiar label spoken while viewing semantically related familiar and novel objects), Disambiguation trials (unfamiliar label spoken while viewing semantically similar familiar and unfamiliar object), and Retention trials (unfamiliar label spoken while viewing novel object pairs). Toddlers' individual vocabulary structure was then compared to performance on each condition. Vocabulary structure was measured at two levels: category-level structure (semantic density) for experimental items, and lexicon-level structure (global clustering coefficient). The findings suggest, consistent with prior results, that semantic density interfered with known word recognition, and facilitated unfamiliar word retention. Children did not show a significant novel word preference during disambiguation, and disambiguation behavior was not impacted by semantic structure. These findings connect seemingly disparate mechanisms of semantic interference in processing and semantic leveraging in word learning. Semantic interference momentarily slows word recognition and resolution of referential uncertainty for novel label-object mappings. Nevertheless, this slowing might support retention by enabling comparison between related objects.

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Not Only Size Matters: Early‐Talker and Late‐Talker Vocabularies Support Different Word‐Learning Biases in Babies and Networks

Cited by 35 publications

References 35 publications

Network Growth Modeling to Capture Individual Lexical Learning

Network Growth Modeling to Capture Individual Lexical Learning

When Stronger Knowledge Slows You Down: Semantic Relatedness Predicts Children's Co‐Activation of Related Items in a Visual Search Paradigm

When slowing down processing helps learning: Lexico‐semantic structure supports retention, but interferes with disambiguation of novel object‐label mappings

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