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Although identifying the referents of single words is often cited as a key challenge for getting word learning off the ground, it overlooks the fact that young learners consistently encounter words in the context of other words. How does this company help or hinder word learning? Prior investigations into early word learning from children's real‐world language input have yielded conflicting results, with some influential findings suggesting an advantage for words that keep a diverse company of other words, and others suggesting the opposite. Here, we sought to triangulate the source of this conflict, comparing different measures of diversity and approaches to controlling for correlated effects of word frequency across multiple languages. The results were striking: while different diversity measures on their own yielded conflicting results, once nonlinear relationships with word frequency were controlled, we found convergent evidence that contextual consistency supports early word learning. Research Highlights: The words children learn occur in a sea of other words. The company words keep ranges from highly variable to highly consistent and circumscribed.Prior findings conflict over whether variability versus consistency helps early word learning.Accounting for correlated effects of word frequency resolved the conflict across multiple languages.Results reveal convergent evidence that consistency helps early word learning. [ABSTRACT FROM AUTHOR]

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre‐registered analyses, we present a linguistic version of the False Belief Task to both human participants and a large language model, GPT‐3. Both are sensitive to others' beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans nor does it explain the full extent of their behavior—despite being exposed to more language than a human would in a lifetime. This suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible. [ABSTRACT FROM AUTHOR]

Languages around the world use a recurring strategy to discuss abstract concepts: describe them metaphorically, borrowing language from more concrete domains. We "plan ahead" to the future, "count up" to higher numbers, and "warm" to new friends. Past work has found that these ways of talking have implications for how we think, so that shared systems of linguistic metaphors can produce shared conceptualizations. On the other hand, these systematic linguistic metaphors might not just be the cause but also the effect of shared, non‐linguistic ways of thinking. Here, we present a case study of a variety of American English in which a shared, non‐linguistic conceptualization of time has become crystallized as a new system of linguistic metaphors. Speakers of various languages, including English, conceptualize time as a lateral timeline, with the past leftward and the future rightward. Until now, this conceptualization has not been documented in the speech of any language. In two studies, we document how members of the U.S. military, but not U.S. civilians, talk about time using conventionalized lateral metaphors (e.g., "move the meeting right" to mean "move the meeting later"). We argue that, under the right cultural circumstances, implicit mental representations become conventionalized metaphors in language. [ABSTRACT FROM AUTHOR]

In contrast to clear stimulatory effects of rising temperature, recent studies of the effects of CO2 on planktonic bacteria have reported conflicting results. To better understand the potential impact of predicted climate scenarios on the development and performance of bacterial communities, we performed bifactorial mesocosm experiments (pCO2 and temperature) with Baltic Sea water, during a diatom dominated bloom in autumn and a mixed phytoplankton bloom in summer. The development of bacterial community composition (BCC) followed well-known algal bloom dynamics. A principal coordinate analysis (PCoA) of bacterial OTUs (operational taxonomic units) revealed that phytoplankton succession and temperature were the major variables structuring the bacterial community whereas the impact of pCO2 was weak. Prokaryotic abundance and carbon production, and organic matter concentration and composition were partly affected by temperature but not by increased pCO2. However, pCO2 did have significant and potentially direct effects on the relative abundance of several dominant OTUs; in some cases, these effects were accompanied by an antagonistic impact of temperature. Our results suggest the necessity of high-resolution BCC analyses and statistical analyses at the OTU level to detect the strong impact of CO2 on specific bacterial groups, which in turn might also influence specific organic matter degradation processes. [ABSTRACT FROM AUTHOR]

Many animals can be trained to perform novel tasks. People, too, can be trained, but sometime in early childhood people transition from being trainable to something qualitatively more powerful-being programmable. We argue that such programmability constitutes a leap in the way that organisms learn, interact, and transmit knowledge, and that what facilitates or enables this programmability is the learning and use of language. We then examine how language programs the mind and argue that it does so through the manipulation of embodied, sensorimotor representations. The role language plays in controlling mental representations offers important insights for understanding its origin and evolution. [ABSTRACT FROM AUTHOR]

A recent pyrosequencing study along the whole Baltic Sea salinity transect identified members of the V errucomicrobia class S partobacteria as an important component of Baltic Sea bacterioplankton. In this study, catalysed reporter deposition-fluorescence in situ hybridization was used for cellular quantification. The published probes VER47 and SPA714 were optimized for samples from the Baltic Sea and a new, specific probe ( SPA476) was used to quantify the dominant spartobacterial lineage ' LD29'. The results confirmed that in the brackish surface waters of the Baltic Sea S partobacteria comprise an important component, constituting up to 12% of all bacteria. The positive correlation and physical association of S partobacteria with phytoplankton suggest their involvement in the utilization of phytoplankton-derived organic matter in the Baltic Sea. [ABSTRACT FROM AUTHOR]

Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different ‘stances’, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

There is mounting evidence that language comprehension involves the activation of mental imagery of the content of utterances (Barsalou, 1999; Bergen, Chang, & Narayan, 2004; Bergen, Narayan, & Feldman, 2003; Narayan, Bergen, & Weinberg, 2004; Richardson, Spivey, McRae, & Barsalou, 2003; Stanfield & Zwaan, 2001; Zwaan, Stanfield, & Yaxley, 2002). This imagery can have motor or perceptual content. Three main questions about the process remain under-explored, however. First, are lexical associations with perception or motion sufficient to yield mental simulation, or is the integration of lexical semantics into larger structures, like sentences, necessary? Second, what linguistic elements (e.g., verbs, nouns, etc.) trigger mental simulations? Third, how detailed are the visual simulations that are performed? A series of behavioral experiments address these questions, using a visual object categorization task to investigate whether up- or down-related language selectively interferes with visual processing in the same part of the visual field (following Richardson et al., 2003). The results demonstrate that either subject nouns or main verbs can trigger visual imagery, but only when used in literal sentences about real space—metaphorical language does not yield significant effects—which implies that it is the comprehension of the sentence as a whole and not simply lexical associations that yields imagery effects. These studies also show that the evoked imagery contains detail as to the part of the visual field where the described scene would take place. [ABSTRACT FROM AUTHOR]

For many scientific and engineering applications, it is often desirable to use unstructured grids to represent complex geometries. Unfortunately, the data structures required to represent discretizations on such grids typically result in extremely inefficient performance on current high-performance architectures. Here, we introduce a grid framework using patch-wise, regular refinement that retains the flexibility of unstructured grids, while achieving performance comparable to that seen with purely structured grids. This approach leads to a grid hierarchy suitable for use with geometric multigrid methods, thus combining asymptotically optimal algorithms with extremely efficient data structures to obtain a powerful technique for large scale simulations. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]