Can Automated Gesture Recognition Support the Study of Child Language Development?

Children's prelinguistic gestures play a central role in their communicative development. Early gesture use has been shown to be predictive of both concurrent and later language ability, making the identification of gestures in video data at scale a potentially valuable tool for both theoretical and clinical purposes. We describe a new dataset consisting of videos of 72 infants interacting with their caregivers at 11&12 months, annotated for the appearance of 12 different gesture types. We propose a model based on deep convolutional neural networks to classify these. The model achieves 48.32% classification accuracy overall, but with significant variation between gesture types. Critically, we found strong (0.7 or above) rank order correlations between by-child gesture counts from human and machine coding for 7 of the 12 gestures (including the critical gestures of declarative pointing, hold outs and gives). Given the challenging nature of the data - recordings of many different dyads in different environments engaged in diverse activities - we consider these results a very encouraging first attempt at the task, and evidence that automatic or machine-assisted gesture identification could make a valuable contribution to the study of cognitive development

A Statistical Approach to the Semantics of Verb-Particles

This paper describes a distributional approach to the semantics of verb-particle constructions (e.g. put up, make off ). We report first on a framework for implementing and evaluating such models. We then go on to report on the implementation of some techniques for using statistical models acquired from corpus data to infer the meaning of verb-particle constructions

Modelling Children's Sentence Recall using an Encoder-Decoder Network

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing

Synthesis of [α‐ 14 C]‐benzylguanidine

[α‐ 14 C]‐benzylguanidine ( 1 ) having a specific activity of 14 mCi/mmol was synthesized in 55% yield (isolated) by reacting [α‐ 14 C]‐benzylamine hydrochloride and cyanamide in 1‐butanol.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90205/1/2580201010_ftp.pd

Expression of the Plasma Cell Transcriptional Regulator Blimp-1 by Dark Zone Germinal Center B Cells During Periods of Proliferation

Long-lived plasma cells (PCs) develop in germinal centers (GCs) by the differentiation of affinity matured B cells. Antibody affinity maturation involves iterative rounds of somatic hypermutation in dark zones (DZs) and selection in light zones (LZs), however the details of where, when and how PC commitment occurs are not well-understood. Fate bifurcation at the time of selection is one possibility, with the very highest affinity GC B cells differentiating as an alternative to DZ re-entry. However, how this model fits with a need to also retain these clones in the response is not clear. Here, we show that subsets of bona fide DZ cells express the plasma cell master regulator Blimp-1 at low levels during periods of proliferation. Ex vivo culture experiments demonstrate that these cells are not yet committed to plasma cell differentiation but that they may be sensitized to go down that route. Contrary to models in which T cells directly select GC B cells to begin expressing Blimp-1, we found that expression of this transcriptional regulator occurred even when follicular helper T cells were ablated. We speculate that Blimp-1 may be induced during proliferation in the DZ, and that as such single selected cells might give rise to both GC and post-GC progeny