How do infants use their knowledge of native language sound patterns when learning words? There is ample evidence of infants' precocious acquisition of native language sound structure during the first years of life, but much less evidence concerning how they apply this knowledge to the task of associating sounds with meanings in word learning. To address this question, 18-month-olds were presented with two phonotactically legal object labels (containing sound sequences that occur frequently in English) or two phonotactically illegal object labels (containing sound sequences that never occur in English), paired with novel objects. Infants were then tested using a looking-while-listening measure. The results revealed that infants looked at the correct objects after hearing the legal labels, but not the illegal labels. Furthermore, vocabulary size was related to performance. Infants with larger receptive vocabularies displayed greater differences between learning of legal and illegal labels than infants with smaller vocabularies. These findings provide evidence that infants' knowledge of native language sound patterns influences their word learning.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032547PMC
http://dx.doi.org/10.1111/j.1532-7078.2010.00046.xDOI Listing

Publication Analysis

Top Keywords

word learning
12
native language
12
language sound
12
knowledge native
8
sound patterns
8
evidence infants'
8
object labels
8
labels sound
8
sound sequences
8
sequences occur
8

Similar Publications

Theoretical work has suggested close associations between morphological awareness (MA) and reading skills in Chinese; however, the nature and direction of these time-ordered links are little known. This study examined the interplays of MA and reading skills using a continuous-time modeling approach to three waves of two-year longitudinal data from first- (N = 149; 69 girls) and third-grade (N = 142; 74 girls) Chinese children. Results showed that (a) increases in MA predicted subsequent increases in reading skills (i.

View Article and Find Full Text PDF

This article details the development of a next-word prediction model utilizing federated learning and introduces a mechanism for detecting backdoor attacks. Federated learning enables multiple devices to collaboratively train a shared model while retaining data locally. However, this decentralized approach is susceptible to manipulation by malicious actors who control a subset of participating devices, thereby biasing the model's outputs on specific topics, such as a presidential election.

View Article and Find Full Text PDF

Temporal logic inference for interpretable fault diagnosis of bearings via sparse and structured neural attention.

ISA Trans

January 2025

State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:

This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.

View Article and Find Full Text PDF

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks).

View Article and Find Full Text PDF

Spatial skills like block building and puzzle making are associated with later growth in science, technology, engineering, and mathematics learning. How these early spatial experiences-both in concrete and digital platforms-boost children's spatial skills remains a mystery. This study examined how children with low- and high-parental education use corrective feedback in a series of spatial assembly tasks.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!