Dataset Information

Predictable Events Enhance Word Learning in Toddlers.

ABSTRACT: Sensitivity to the predictability of the environment supports young children's learning in many domains [1, 2], including language [3-6]; perception [7, 8]; and the processing of objects, space, and time [1, 9]. Predictable regularities allow observers to generate expectations about upcoming events and to learn from violations of those expectations [10, 11]. Given the benefits of detecting both predictable and unpredictable events, a key question concerns which types of input facilitate learning in young children. In the current research, we assessed the effects of predictability on toddlers' word learning by embedding word-learning moments within events that were either predicted or violated predictions. 2-year-olds observed a continuous visual sequence in which novel objects were revealed from one of four locations in a predictable spatiotemporal pattern (1, 2, 3, 4). Objects were then labeled either during events that were predicted by the sequence (1, 2, 3, 4) or events that violated the sequence (1, 2, 3, 2). Results from two studies revealed better word learning for objects labeled during predictable events than objects labeled during unpredictable events. These findings suggest that predictable events create advantageous learning moments for toddlers, with implications for the role played by predictable input in early development.

SUBMITTER: Benitez VL

PROVIDER: S-EPMC6148368 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Predictable Events Enhance Word Learning in Toddlers.

Benitez Viridiana L VL Saffran Jenny R JR

Current biology : CB 20180816 17

Sensitivity to the predictability of the environment supports young children's learning in many domains [1, 2], including language [3-6]; perception [7, 8]; and the processing of objects, space, and time [1, 9]. Predictable regularities allow observers to generate expectations about upcoming events and to learn from violations of those expectations [10, 11]. Given the benefits of detecting both predictable and unpredictable events, a key question concerns which types of input facilitate learning ...[more]

PMID: 30122525

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Project description:In recent years, the popularity of tablets has skyrocketed and there has been an explosive growth in apps designed for children. Howhever, many of these apps are released without tests for their effectiveness. This is worrying given that the factors influencing children's learning from touchscreen devices need to be examined in detail. In particular, it has been suggested that children learn less from passive video viewing relative to equivalent live interaction, which would have implications for learning from such digital tools. However, this so-called video deficit may be reduced by allowing children greater influence over their learning environment. Across two touchscreen-based experiments, we examined whether 2- to 4-year-olds benefit from actively choosing what to learn more about in a digital word learning task. We designed a tablet study in which "active" participants were allowed to choose which objects they were taught the label of, while yoked "passive" participants were presented with the objects chosen by their active peers. We then examined recognition of the learned associations across different tasks. In Experiment 1, children in the passive condition outperformed those in the active condition (n = 130). While Experiment 2 replicated these findings in a new group of Malay-speaking children (n = 32), there were no differences in children's learning or recognition of the novel word-object associations using a more implicit looking time measure. These results suggest that there may be performance costs associated with active tasks designed as in the current study, and at the very least, there may not always be systematic benefits associated with active learning in touchscreen-based word learning tasks. The current studies add to the evidence that educational apps need to be evaluated before release: While children might benefit from interactive apps under certain conditions, task design and requirements need to consider factors that may detract from successful performance.

Project description:OBJECTIVE:Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can deter pediatric screening. The Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) is a widely used screener but requires follow-up questions and error-prone human scoring and interpretation. We consider an automated machine learning (ML) method for overcoming barriers to ASD screening, specifically using the feedforward neural network (fNN). METHODS:The fNN technique was applied using archival M-CHAT-R data of 14,995 toddlers (age 16-30 months, 46.51% male). The 20 M-CHAT-R items were inputs, and ASD diagnosis after follow-up and diagnostic evaluation (i.e., ASD or not ASD) was the output. The sample was divided into subgroups by race (i.e., white and black), sex (i.e., boys and girls), and maternal education (i.e., below and above 15 years of education completed) to examine subgroup differences. Each subgroup was evaluated for best-performing fNN models. RESULTS:For the total sample, best results yielded 99.72% correct classification using 18 items. Best results yielded 99.92% correct classification using 14 items for white toddlers and 99.79% correct classification using 18 items for black toddlers. In boys, best results yielded 99.64% correct classification using 18 items, whereas best results yielded 99.95% correct classification using 18 items in girls. For the case when maternal education is 15 years or less (i.e., associate degree and below), best results were 99.75% correct classification when using 16 items. Results were essentially the same when maternal education was 16 years or more (i.e., above associate degree); that is, 99.70% correct classification was obtained using 16 items. CONCLUSION:The ML method was comparable to the M-CHAT-R with follow-up items in accuracy of ASD diagnosis while using fewer items. Therefore, ML may be a beneficial tool in implementing automatic, efficient scoring that negates the need for labor-intensive follow-up and circumvents human error, providing an advantage over previous screening methods.

Dataset Information

Predictable Events Enhance Word Learning in Toddlers.

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Predictable Events Enhance Word Learning in Toddlers.

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