Shovels and Swords: How realistic and fantastical themes affect children's word learning

Cataloged from PDF version of article.Research has shown that storybooks and play sessions help preschool children learn vocabulary, thereby benefiting their language and school readiness skills. But the kind of content that leads to optimal vocabulary learning – realistic or fantastical – remains largely unexplored. We investigate this issue as part of a large-scale study of vocabulary learning in low-income classrooms. Preschoolers (N = 154) learned 20 new words over the course of a two-week intervention. These words were taught using either realistic (e.g., farms) or fantastical (e.g., dragons) storybooks and toys. Children learned the new words in both conditions, and their comprehension knowledge did not differ across conditions. However, children who engaged in stories and play with a fantastical theme showed significantly greater gains in their production knowledge. Reasons for and implications of this result are discussed

Stochastic Dynamics of Lexicon Learning in an Uncertain and Nonuniform World

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.Comment: 7 pages, 3 figures. Version 2 contains additional discussion and will appear in Phys. Rev. Let

Chimpanzees modify intentional gestures to coordinate a search for hidden food

Humans routinely communicate to coordinate their activities, persisting and elaborating signals to pursue goals that cannot be accomplished individually. Communicative persistence is associated with complex cognitive skills such as intentionality, because interactants modify their communication in response to another's understanding of their meaning. Here we show that two language-trained chimpanzees effectively use intentional gestures to coordinate with an experimentally naive human to retrieve hidden food, providing some of the most compelling evidence to date for the role of communicative flexibility in successful coordination in nonhumans. Both chimpanzees (named Panzee and Sherman) increase the rate of nonindicative gestures when the experimenter approaches the location of the hidden food. Panzee also elaborates her gestures in relation to the experimenter's pointing, which enables her to find food more effectively than Sherman. Communicative persistence facilitates effective communication during behavioural coordination and is likely to have been important in shaping language evolution

Beyond the Bayley: Neurocognitive Assessments of Development During Infancy and Toddlerhood

The use of global, standardized instruments is conventional among clinicians and researchers interested in assessing neurocognitive development. Exclusively relying on these tests for evaluating effects may underestimate or miss specific effects on early cognition. The goal of this review is to identify alternative measures for possible inclusion in future clinical trials and interventions evaluating early neurocognitive development. The domains included for consideration are attention, memory, executive function, language and socio-emotional development. Although domain-based tests are limited, as psychometric properties have not yet been well-established, this review includes tasks and paradigms that have been reliably used across various developmental psychology laboratories

The Novel Object and Unusual Name (NOUN) database: a collection of novel images for use in experimental research

Many experimental research designs require images of novel objects. Here we introduce the Novel Object and Unusual Name (NOUN) Database. This database contains 64 primary novel object images and additional novel exemplars for ten basic- and nine global-level object categories. The objects’ novelty was confirmed by both self-report and a lack of consensus on questions that required participants to name and identify the objects. We also found that object novelty correlated with qualifying naming responses pertaining to the objects’ colors. Results from a similarity sorting task (and subsequent multidimensional scaling analysis on the similarity ratings) demonstrated that the objects are complex and distinct entities that vary along several featural dimensions beyond simply shape and color. A final experiment confirmed that additional item exemplars comprise both sub- and superordinate categories. These images may be useful in a variety of settings, particularly for developmental psychology and other research in language, categorization, perception, visual memory and related domains

The use of airborne laser scanning to develop a pixel-based stratification for a verified carbon offset project

Background The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland. Monitoring, reporting, and verifying carbon stocks and fluxes at a project level is the single largest direct cost of a forest carbon offset project. There are now many methods for estimating forest stocks with high accuracy that use both Airborne Laser Scanning (ALS) and high-resolution optical remote sensing data. However, many of these methods are not appropriate for use under existing carbon offset standards and most have not been field tested. Results This paper presents a pixel-based forest stratification method that uses both ALS and optical remote sensing data to optimally partition the variability across an ~10,000 ha forest ownership in Mendocino County, CA, USA. This new stratification approach improved the accuracy of the forest inventory, reduced the cost of field-based inventory, and provides a powerful tool for future management planning. This approach also details a method of determining the optimum pixel size to best partition a forest. Conclusions The use of ALS and optical remote sensing data can help reduce the cost of field inventory and can help to locate areas that need the most intensive inventory effort. This pixel-based stratification method may provide a cost-effective approach to reducing inventory costs over larger areas when the remote sensing data acquisition costs can be kept low on a per acre basis