Dataset Information

Microsaccades are sensitive to word structure: A novel approach to study language processing.

ABSTRACT: Microsaccades are miniature eye movements that occur involuntarily during fixation. They are typically inhibited following stimulus onset and are released from inhibition about 300?ms post-stimulus. Microsaccade-inhibition is modulated by low level features of visual stimuli, but it is currently unknown whether they are sensitive to higher level, abstract linguistic properties. To address this question, we measured the timing of microsaccades while subjects were presented with written Hebrew words and pronounceable nonwords (pseudowords). We manipulated the underlying structure of pseudowords such that half of them contained real roots while the other half contained invented roots. Importantly, orthographic similarity to real words was equated between the two conditions. Microsaccade onset was significantly slower following real-root compared to invented-root stimuli. Similar results were obtained when considering post-stimulus delay of eye blinks. Moreover, microsaccade-delay was positively and significantly correlated with measures of real-word similarity. These findings demonstrate, for the first time, sensitivity of microsaccades to linguistic structure. Because microsaccades are involuntary and can be measured in the absence of overt response, our results provide initial evidence that they can be used as a novel physiological measure in the study of language processes in healthy and clinical populations.

SUBMITTER: Yablonski M

PROVIDER: S-EPMC5479819 | biostudies-other | 2017 Jun

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Microsaccades are sensitive to word structure: A novel approach to study language processing.

Yablonski Maya M Polat Uri U Bonneh Yoram S YS Ben-Shachar Michal M

Scientific reports 20170621 1

Microsaccades are miniature eye movements that occur involuntarily during fixation. They are typically inhibited following stimulus onset and are released from inhibition about 300 ms post-stimulus. Microsaccade-inhibition is modulated by low level features of visual stimuli, but it is currently unknown whether they are sensitive to higher level, abstract linguistic properties. To address this question, we measured the timing of microsaccades while subjects were presented with written Hebrew wor ...[more]

PMID: 28638094

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.

Project description:BackgroundWord embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the ability of the vector representations being able to capture useful semantic properties and linguistic relationships between words. Different textual resources (e.g., Wikipedia and biomedical literature corpus) have been utilized in biomedical NLP to train word embeddings and these word embeddings have been commonly leveraged as feature input to downstream machine learning models. However, there has been little work on evaluating the word embeddings trained from different textual resources.MethodsIn this study, we empirically evaluated word embeddings trained from four different corpora, namely clinical notes, biomedical publications, Wikipedia, and news. For the former two resources, we trained word embeddings using unstructured electronic health record (EHR) data available at Mayo Clinic and articles (MedLit) from PubMed Central, respectively. For the latter two resources, we used publicly available pre-trained word embeddings, GloVe and Google News. The evaluation was done qualitatively and quantitatively. For the qualitative evaluation, we randomly selected medical terms from three categories (i.e., disorder, symptom, and drug), and manually inspected the five most similar words computed by embeddings for each term. We also analyzed the word embeddings through a 2-dimensional visualization plot of 377 medical terms. For the quantitative evaluation, we conducted both intrinsic and extrinsic evaluation. For the intrinsic evaluation, we evaluated the word embeddings' ability to capture medical semantics by measruing the semantic similarity between medical terms using four published datasets: Pedersen's dataset, Hliaoutakis's dataset, MayoSRS, and UMNSRS. For the extrinsic evaluation, we applied word embeddings to multiple downstream biomedical NLP applications, including clinical information extraction (IE), biomedical information retrieval (IR), and relation extraction (RE), with data from shared tasks.ResultsThe qualitative evaluation shows that the word embeddings trained from EHR and MedLit can find more similar medical terms than those trained from GloVe and Google News. The intrinsic quantitative evaluation verifies that the semantic similarity captured by the word embeddings trained from EHR is closer to human experts' judgments on all four tested datasets. The extrinsic quantitative evaluation shows that the word embeddings trained on EHR achieved the best F1 score of 0.900 for the clinical IE task; no word embeddings improved the performance for the biomedical IR task; and the word embeddings trained on Google News had the best overall F1 score of 0.790 for the RE task.ConclusionBased on the evaluation results, we can draw the following conclusions. First, the word embeddings trained from EHR and MedLit can capture the semantics of medical terms better, and find semantically relevant medical terms closer to human experts' judgments than those trained from GloVe and Google News. Second, there does not exist a consistent global ranking of word embeddings for all downstream biomedical NLP applications. However, adding word embeddings as extra features will improve results on most downstream tasks. Finally, the word embeddings trained from the biomedical domain corpora do not necessarily have better performance than those trained from the general domain corpora for any downstream biomedical NLP task.

Dataset Information

Microsaccades are sensitive to word structure: A novel approach to study language processing.

Publications

Microsaccades are sensitive to word structure: A novel approach to study language processing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets