Dataset Information

Producing Spatial Words Is Not Enough: Understanding the Relation Between Language and Spatial Cognition.

ABSTRACT: Prior research has investigated the relation between children's language and spatial cognition by assessing the quantity of children's spatial word production, with limited attention to the context in which children use such words. This study tested whether 4-year-olds children's (N = 41, primarily white middle class) adaptive use of task-relevant language across contexts predicted their spatial skills. Children were presented with a spatial scene description task, four spatial tasks, and vocabulary assessments. Children's adaptive use of task-relevant language was more predictive of their spatial skills than demographic and language factors (e.g., quantity of spatial words produced). These findings identify new links between language and spatial cognition and highlight the importance of understanding the quality, not just quantity, of children's language use.

SUBMITTER: Miller HE

PROVIDER: S-EPMC5422135 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Producing Spatial Words Is Not Enough: Understanding the Relation Between Language and Spatial Cognition.

Miller Hilary E HE Vlach Haley A HA Simmering Vanessa R VR

Child development 20161108 6

Prior research has investigated the relation between children's language and spatial cognition by assessing the quantity of children's spatial word production, with limited attention to the context in which children use such words. This study tested whether 4-year-olds children's (N = 41, primarily white middle class) adaptive use of task-relevant language across contexts predicted their spatial skills. Children were presented with a spatial scene description task, four spatial tasks, and vocabu ...[more]

PMID: 27859021

Similar Datasets

Project description:Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as Rad-SpRL. In Rad-SpRL, common radiological entities tied to spatial relations are encoded through four spatial roles: Trajector, Landmark, Diagnosis, and Hedge, all identified in relation to a spatial preposition (or Spatial Indicator). We annotated a total of 2,000 chest X-ray reports following Rad-SpRL. We then propose a deep learning-based natural language processing (NLP) method involving word and character-level encodings to first extract the Spatial Indicators followed by identifying the corresponding spatial roles. Specifically, we use a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) neural network as the baseline model. Additionally, we incorporate contextualized word representations from pre-trained language models (BERT and XLNet) for extracting the spatial information. We evaluate both gold and predicted Spatial Indicators to extract the four types of spatial roles. The results are promising, with the highest average F1 measure for Spatial Indicator extraction being 91.29 (XLNet); the highest average overall F1 measure considering all the four spatial roles being 92.9 using gold Indicators (XLNet); and 85.6 using predicted Indicators (BERT pre-trained on MIMIC notes). The corpus is available in Mendeley at http://dx.doi.org/10.17632/yhb26hfz8n.1 and https://github.com/krobertslab/datasets/blob/master/Rad-SpRL.xml.

Dataset Information

Producing Spatial Words Is Not Enough: Understanding the Relation Between Language and Spatial Cognition.

Publications

Producing Spatial Words Is Not Enough: Understanding the Relation Between Language and Spatial Cognition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure