Understanding language requires more than the use of fixed conventions and more than decoding combinatorial structure. Instead, comprehenders make exquisitely sensitive inferences about what ...utterances mean given their knowledge of the speaker, language, and context. Building on developments in game theory and probabilistic modeling, we describe the rational speech act (RSA) framework for pragmatic reasoning. RSA models provide a principled way to formalize inferences about meaning in context; they have been used to make successful quantitative predictions about human behavior in a variety of different tasks and situations, and they explain why complex phenomena, such as hyperbole and vagueness, occur. More generally, they provide a computational framework for integrating linguistic structure, world knowledge, and context in pragmatic language understanding.
Predicting Pragmatic Reasoning in Language Games Frank, Michael C.; Goodman, Noah D.
Science (American Association for the Advancement of Science),
05/2012, Letnik:
336, Številka:
6084
Journal Article
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One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have ...been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.
Large language models (LLMs) show intriguing emergent behaviors, yet they receive around four or five orders of magnitude more language data than human children. What accounts for this vast ...difference in sample efficiency? Candidate explanations include children’s pre-existing conceptual knowledge, their use of multimodal grounding, and the interactive, social nature of their input.
Large language models (LLMs) show intriguing emergent behaviors, yet they receive around four or five orders of magnitude more language data than human children. What accounts for this vast difference in sample efficiency? Candidate explanations include children’s pre-existing conceptual knowledge, their use of multimodal grounding, and the interactive, social nature of their input.
The MacArthur-Bates Communicative Development Inventories (CDIs) are a widely used family of parent-report instruments for easy and inexpensive data-gathering about early language acquisition. CDI ...data have been used to explore a variety of theoretically important topics, but, with few exceptions, researchers have had to rely on data collected in their own lab. In this paper, we remedy this issue by presenting Wordbank, a structured database of CDI data combined with a browsable web interface. Wordbank archives CDI data across languages and labs, providing a resource for researchers interested in early language, as well as a platform for novel analyses. The site allows interactive exploration of patterns of vocabulary growth at the level of both individual children and particular words. We also introduce wordbankr, a software package for connecting to the database directly. Together, these tools extend the abilities of students and researchers to explore quantitative trends in vocabulary development.
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible ...as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods and that the mapping of these neural network models' hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.
Predicting the birth of a spoken word Roy, Brandon C.; Frank, Michael C.; DeCamp, Philip ...
Proceedings of the National Academy of Sciences - PNAS,
10/2015, Letnik:
112, Številka:
41
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Children learn words through an accumulation of interactions grounded in context. Although many factors in the learning environment have been shown to contribute to word learning in individual ...studies, no empirical synthesis connects across factors. We introduce a new ultradense corpus of audio and video recordings of a single child’s life that allows us to measure the child’s experience of each word in his vocabulary. This corpus provides the first direct comparison, to our knowledge, between different predictors of the child’s production of individual words. We develop a series of new measures of the distinctiveness of the spatial, temporal, and linguistic contexts in which a word appears, and show that these measures are stronger predictors of learning than frequency of use and that, unlike frequency, they play a consistent role across different syntactic categories. Our findings provide a concrete instantiation of classic ideas about the role of coherent activities in word learning and demonstrate the value of multimodal data in understanding children’s language acquisition.
The opportunistic pathogenic mold Aspergillus fumigatus is an increasing cause of morbidity and mortality in immunocompromized and in part immunocompetent patients. A. fumigatus can grow in ...multicellular communities by the formation of a hyphal network encased in an extracellular matrix. Here, we describe the proteome and transcriptome of planktonic- and biofilm-grown A. fumigatus mycelium after 24 and 48 h. A biofilm- and time-dependent regulation of many proteins and genes of the primary metabolism indicates a developmental stage of the young biofilm at 24 h, which demands energy. At a matured biofilm phase, metabolic activity seems to be reduced. However, genes, which code for hydrophobins, and proteins involved in the biosynthesis of secondary metabolites were significantly upregulated. In particular, proteins of the gliotoxin secondary metabolite gene cluster were induced in biofilm cultures. This was confirmed by real-time PCR and by detection of this immunologically active mycotoxin in culture supernatants using HPLC analysis. The enhanced production of gliotoxin by in vitro formed biofilms reported here may also play a significant role under in vivo conditions. It may confer A. fumigatus protection from the host immune system and also enable its survival and persistence in chronic lung infections such as aspergilloma.
Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. ...Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities.
•We measure how well cross-situational learners store encode multiple referents.•Learners encode both one strong hypothesis and weaker distributional approximations.•The fidelity of these ...representations degrades with increasing ambiguity and delay.•These results are predicted by a model that unifies previously opposed accounts.
Word-object co-occurrence statistics are a powerful information source for vocabulary learning, but there is considerable debate about how learners actually use them. While some theories hold that learners accumulate graded, statistical evidence about multiple referents for each word, others suggest that they track only a single candidate referent. In two large-scale experiments, we show that neither account is sufficient: Cross-situational learning involves elements of both. Further, the empirical data are captured by a computational model that formalizes how memory and attention interact with co-occurrence tracking. Together, the data and model unify opposing positions in a complex debate and underscore the value of understanding the interaction between computational and algorithmic levels of explanation.
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