Dataset Information

Talker-specific predictions during language processing.

ABSTRACT: Language comprehension is shaped by world knowledge. After hearing about "a farm animal," meanings of typical ("cow") versus atypical exemplars ("ox") are more accessible, as evidenced by N400 responses. Moreover, atypical exemplars elicit a larger post-N400 frontal positivity than typical and incongruous ("ivy") exemplars, indexing the integration of unexpected information. Do listeners adapt this category knowledge to specific talkers? We first replicated typicality effects in the auditory modality. Then, we extended the design to a two-talker context: talkers alternated cueing (Bob: "Susan, name a farm animal") and answering (Susan: "cow"). Critically, participants first heard interviews in which one talker revealed strong associations with atypical exemplars (Susan works on an ox farm). We observed increased frontal positivity to a typical exemplar ("cow") said by Susan compared to Bob, indicating participants appreciated that the typical exemplar was atypical for Susan. These results suggest that comprehenders can tailor their expectations to the talker.

SUBMITTER: Ryskin R

PROVIDER: S-EPMC7943179 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Talker-specific predictions during language processing.

Ryskin Rachel R Ng Shukhan S Mimnaugh Katherine K Brown-Schmidt Sarah S Federmeier Kara D KD

Language, cognition and neuroscience 20190627 6

Language comprehension is shaped by world knowledge. After hearing about "a farm animal," meanings of typical ("cow") versus atypical exemplars ("ox") are more accessible, as evidenced by N400 responses. Moreover, atypical exemplars elicit a larger post-N400 frontal positivity than typical and incongruous ("ivy") exemplars, indexing the integration of unexpected information. Do listeners adapt this category knowledge to specific talkers? We first replicated typicality effects in the auditory mod ...[more]

PMID: 33693050

Similar Datasets

Project description:BackgroundThere has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus.PurposeWe examined the potential of Radiopaedia to serve as a general radiology corpus to produce radiology specific word embeddings that could be used to enhance performance on a NLP task on radiological text.Materials and methodsEmbeddings of dimension 50, 100, 200, and 300 were trained on articles collected from Radiopaedia using a GloVe algorithm and evaluated on analogy completion. A shallow neural network using input from either our trained embeddings or pre-trained Wikipedia 2014 + Gigaword 5 (WG) embeddings was used to label the Radiopaedia articles. Labeling performance was evaluated based on exact match accuracy and Hamming loss. The McNemar's test with continuity and the Benjamini-Hochberg correction and a 5×2 cross validation paired two-tailed t-test were used to assess statistical significance.ResultsFor accuracy in the analogy task, 50-dimensional (50-D) Radiopaedia embeddings outperformed WG embeddings on tumor origin analogies (p < 0.05) and organ adjectives (p < 0.01) whereas WG embeddings tended to outperform on inflammation location and bone vs. muscle analogies (p < 0.01). The two embeddings had comparable performance on other subcategories. In the labeling task, the Radiopaedia-based model outperformed the WG based model at 50, 100, 200, and 300-D for exact match accuracy (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively) and Hamming loss (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively).ConclusionWe have developed a set of word embeddings from Radiopaedia and shown that they can preserve relevant medical semantics and augment performance on a radiology NLP task. Our results suggest that the cultivation of a radiology-specific corpus can benefit radiology NLP models in the future.

Dataset Information

Talker-specific predictions during language processing.

Publications

Talker-specific predictions during language processing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets