The Relation Between Preschoolers’ Vocabulary Development and Their Ability to Predict and Recognize Words

Abstract

By age 2, children are developing foundational language processing skills, such as quickly recognizing words and predicting words before they occur. How do these skills relate to children's structural knowledge of vocabulary? Multiple aspects of language processing were simultaneously measured in a sample of 2-to-5year-olds (N = 215): While older children were more fluent at recognizing words, at predicting words in a graded fashion, and at revising incorrect predictions, only revision was associated with concurrent vocabulary knowledge once age was accounted for. However, an exploratory longitudinal follow-up (N = 55) then found that word recognition and prediction skills were associated with rate of subsequent vocabulary development, but revision skills were not. We argue that prediction skills may facilitate language learning through enhancing processing speed.

The role of verbal and working memory skills in Turkish-speaking children’s morphosyntactic prediction

“…Child-assessed receptive vocabulary was also associated with 3-to 10-year-old English-speaking children's linguistic predictions based on the integration of inferred talker identity and action (e.g., an implied pirate vs. a princess identity talking about holding a sword vs. a wand; Borovsky & Creel, 2014). A recent study, however, showed that 2-to 5-year-old English-speaking children's receptive vocabulary skills, which were assessed by testing the children, were associated with their revision of erroneous predictions but not with their prediction skills, once age was controlled for (Gambi et al, 2021). A subsample of children in this study was found to have predictive associations between their word recognition and prediction skills (Time 1 mean age: 42 months, range: 25-60 months) and their subsequent receptive vocabulary growth within the next 5-10 months (Time 2 mean age: 50 months, range: 34-68 months).…”

“…Among the verbal predictors, only early productive vocabulary and language production skills were found to significantly contribute to the prediction effect. Figure 3 shows the increase in agent fixations in both conditions for the first and the third quartiles of the centered and scaled early productive vocabulary (TCDI) scores (Gambi et al, 2021). The children in the third quartile exhibited a larger difference in agent fixations between the two conditions and they started showing the prediction effect earlier, compared to the children in the first quartile.…”

“…The language processing efficiency measured with LWL in these studies might reflect an underlying skill, for example, speed of lexical access, speed of access to long-term representations, or speed of orienting visual attention based on verbal input, which is quite likely exploited during linguistic prediction. Indeed, a recent study suggested that the roles of prediction and word recognition skills in language development might overlap by showing that these skills similarly predicted subsequent vocabulary growth within 5-10 months (Gambi et al, 2021). Vocabulary skills, on the other hand, might be a proxy for such differences in processing efficiency.…”

Predicting Human Psychometric Properties Using Computational Language Models

“…Although TLMs were not primarily designed to compute in a human-like way, there are some reasons to suspect that they may have the ability to effectively model at least some aspects of human linguistic reasoning: They consistently demonstrate superior performance (at least compared to other LMs) on human-inspired linguistic benchmarks (Wang et al, 2018(Wang et al, , 2019, and they are typically pre-trained using a lengthy process designed to embed deep semantic knowledge, resulting in efficient encoding of semantic relationships (Zhou et al, 2020;Cui et al, 2020). Common optimization tasks for pre-training transformers, such as the masked LM task (Devlin et al, 2018) are quite similar to the word prediction tasks that are known to predict children's performance on other linguistic skills (Gambi et al, 2020). Finally, TLMs tend to outperform other LMs in recent work modeling human reading times, eye-tracking data, and other psychological and psycholinguistic phenomena (Schrimpf et al, 2020b,a;Hao et al, 2020;Merkx and Frank, 2021;Laverghetta Jr. et al, 2021).…”

Can Transformer Language Models Predict Psychometric Properties?

“…Although TLMs were not primarily designed to compute in a human-like way, there are some rea-sons to suspect that they may have the ability to effectively model at least some aspects of human linguistic reasoning: They consistently demonstrate superior performance (at least compared to other LMs) on human-inspired linguistic benchmarks (Wang et al, 2018(Wang et al, , 2019, and they are typically pre-trained using a lengthy process designed to embed deep semantic knowledge, resulting in efficient encoding of semantic relationships Petroni et al, 2019;Davison et al, 2019;. Common optimization tasks for pretraining transformers, such as the masked LM task (Devlin et al, 2018) are quite similar to the word prediction tasks that are known to predict children's performance on other linguistic skills (Borovsky et al, 2012;Neuman et al, 2011;Gambi et al, 2020). Finally, TLMs tend to outperform other LMs in recent work modeling human reading times, eye-tracking data, and other psychological and psycholinguistic phenomena (Merkx and Frank, 2021;Schrimpf et al, 2020b,a;Hao et al, 2020;Bhatia and Richie, 2020;.…”