The Experts below are selected from a list of 110445 Experts worldwide ranked by ideXlab platform
Arielle Borovsky - One of the best experts on this subject based on the ideXlab platform.
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when slowing down processing helps learning lexico Semantic Structure supports retention but interferes with disambiguation of novel object label mappings
Developmental Science, 2020Co-Authors: Arielle BorovskyAbstract:This project explores how children disambiguate and retain novel object-label mappings in the face of Semantic similarity. Burgeoning evidence suggests that Semantic Structure in the developing lexicon promotes word learning in ostensive contexts, whereas other findings indicate that Semantic similarity interferes with and temporarily slows familiar word recognition. This project explores how these distinct processes interact when mapping and retaining labels for novel objects (i.e., low-frequency objects that are unfamiliar to toddlers) via disambiguation from a Semantically similar familiar referent in 24-month-olds (N = 65). Toddlers' log-adjusted looking to labeled target objects (relative to distractor objects) was measured in three conditions: Familiar trials (familiar label spoken while viewing Semantically related familiar and novel objects), Disambiguation trials (unfamiliar label spoken while viewing Semantically similar familiar and unfamiliar object), and Retention trials (unfamiliar label spoken while viewing novel object pairs). Toddlers' individual vocabulary Structure was then compared to performance on each condition. Vocabulary Structure was measured at two levels: category-level Structure (Semantic density) for experimental items, and lexicon-level Structure (global clustering coefficient). The findings suggest, consistent with prior results, that Semantic density interfered with known word recognition, and facilitated unfamiliar word retention. Children did not show a significant novel word preference during disambiguation, and disambiguation behavior was not impacted by Semantic Structure. These findings connect seemingly disparate mechanisms of Semantic interference in processing and Semantic leveraging in word learning. Semantic interference momentarily slows word recognition and resolution of referential uncertainty for novel label-object mappings. Nevertheless, this slowing might support retention by enabling comparison between related objects.
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vocabulary size and Structure affects real time lexical recognition in 18 month olds
PLOS ONE, 2019Co-Authors: Arielle Borovsky, Ryan PetersAbstract:The mature lexicon encodes Semantic relations between words, and these connections can alternately facilitate and interfere with language processing. We explore the emergence of these processing dynamics in 18-month-olds (N = 79) using a novel approach that calculates individualized Semantic Structure at multiple granularities in participants’ productive vocabularies. Participants completed two interleaved eye-tracked word recognition tasks involving Semantically unrelated and related picture contexts, which sought to measure the impact of lexical facilitation and interference on processing, respectively. Semantic Structure and vocabulary size differentially impacted processing in each task. Category level Structure facilitated word recognition in 18-month-olds with smaller productive vocabularies, while overall lexical connectivity interfered with word recognition for toddlers with relatively larger vocabularies. The results suggest that, while Semantic Structure at multiple granularities is measurable even in small lexicons, mechanisms of Semantic interference and facilitation are driven by the development of Structure at different granularities. We consider these findings in light of accounts of adult word recognition that posits that different levels of Structure index strong and weak activation from nearby and distant Semantic neighbors. We also consider further directions for developmental change in these patterns.
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Semantic Structure in vocabulary knowledge interacts with lexical and sentence processing in infancy
Child Development, 2016Co-Authors: Arielle Borovsky, Erica M Ellis, Julia L Evans, Jeffrey L ElmanAbstract:Although the size of a child's vocabulary associates with language-processing skills, little is understood regarding how this relation emerges. This investigation asks whether and how the Structure of vocabulary knowledge affects language processing in English-learning 24-month-old children (N = 32; 18 F, 14 M). Parental vocabulary report was used to calculate Semantic density in several early-acquired Semantic categories. Performance on two language-processing tasks (lexical recognition and sentence processing) was compared as a function of Semantic density. In both tasks, real-time comprehension was facilitated for higher density items, whereas lower density items experienced more interference. The findings indicate that language-processing skills develop heterogeneously and are influenced by the Semantic network surrounding a known word.
Qingming Huang - One of the best experts on this subject based on the ideXlab platform.
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learning Semantic Structure preserved embeddings for cross modal retrieval
ACM Multimedia, 2018Co-Authors: Shuhui Wang, Qingming HuangAbstract:This paper learns Semantic embeddings for multi-label cross-modal retrieval. Our method exploits the Structure in Semantics represented by label vectors to guide the learning of embeddings. First, we construct a Semantic graph based on label vectors which incorporates data from both modalities, and enforce the embeddings to preserve the local Structure of this Semantic graph. Second, we enforce the embeddings to well reconstruct the labels, i.e., the global Semantic Structure. In addition, we encourage the embeddings to preserve local geometric Structure of each modality. Accordingly, the local and global Semantic Structure consistencies as well as the local geometric Structure consistency are enforced, simultaneously. The mappings between inputs and embeddings are designed to be nonlinear neural network with larger capacity and more flexibility. The overall objective function is optimized by stochastic gradient descent to gain the scalability on large datasets. Experiments conducted on three real world datasets clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.
Suzanne Bakken - One of the best experts on this subject based on the ideXlab platform.
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integration of nursing assessment concepts into the medical entities dictionary using the loinc Semantic Structure as a terminology model
American Medical Informatics Association Annual Symposium, 2001Co-Authors: Bethany J Cieslowski, James J Cimino, David Wajngurt, Suzanne BakkenAbstract:Recent investigations have tested the applicability of various terminology models for the representing nursing concepts including those related to nursing diagnoses, nursing interventions, and standardized nursing assessments as a prerequisite for building a reference terminology that supports the nursing domain. We used the Semantic Structure of Clinical LOINC (Logical Observations, Identifiers, Names, and Codes) as a reference terminology model to support the integration of standardized assessment terms from two nursing terminologies into the Medical Entities Dictionary (MED), the concept-oriented, metadata dictionary at New York Presbyterian Hospital. Although the LOINC Semantic Structure was used previously to represent laboratory terms in the MED, selected hierarchies and Semantic slots required revisions in order to incorporate the nursing assessment concepts. This project was an initial step in integrating nursing assessment concepts into the MED in a manner consistent with evolving standards for reference terminology models. Moreover, the revisions provide the foundation for adding other types of standardized assessments to the MED.
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evaluation of the clinical loinc logical observation identifiers names and codes Semantic Structure as a terminology model for standardized assessment measures
Journal of the American Medical Informatics Association, 2000Co-Authors: Suzanne Bakken, James J Cimino, Robert Emmons Haskell, Rita Kukafka, Cindi Matsumoto, Garrett K Chan, Stanley M HuffAbstract:Objective: The purpose of this study was to test the adequacy of the Clinical LOINC (Logical Observation Identifiers, Names, and Codes) Semantic Structure as a terminology model for standardized assessment measures. Methods: After extension of the definitions, 1,096 items from 35 standardized assessment instruments were dissected into the elements of the Clinical LOINC Semantic Structure. An additional coder dissected at least one randomly selected item from each instrument. When multiple scale types occurred in a single instrument, a second coder dissected one randomly selected item representative of each scale type. Results: The results support the adequacy of the Clinical LOINC Semantic Structure as a terminology model for standardized assessments. Using the revised definitions, the coders were able to dissect into the elements of Clinical LOINC all the standardized assessment items in the sample instruments. Percentage agreement for each element was as follows: component, 100 percent; property, 87.8 percent; timing, 82.9 percent; system/sample, 100 percent; scale, 92.6 percent; and method, 97.6 percent. Discussion: This evaluation was an initial step toward the representation of standardized assessment items in a manner that facilitates data sharing and re-use. Further clarification of the definitions, especially those related to time and property, is required to improve inter-rater reliability and to harmonize the representations with similar items already in LOINC.
Chika Sumiyoshi - One of the best experts on this subject based on the ideXlab platform.
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disorganization of Semantic memory underlies alogia in schizophrenia an analysis of verbal fluency performance in japanese subjects
Schizophrenia Research, 2005Co-Authors: Chika Sumiyoshi, Mie Matsui, Ikiko Yamashita, Masayoshi Kurachi, Tomiki Sumiyoshi, Shigeru Nohara, Shinichi NiwaAbstract:Abstract Patients with schizophrenia exhibit impaired Semantic memory as well as deficits in a wide range of language-related functions, such as verbal fluency, comprehension and production of complex sentences. Since language and memory disturbances may underlie some of the psychotic symptoms of schizophrenia, the present study investigated the specific association between alogia (i.e. poverty of speech, poverty of content of speech, blocking, and increased latency of response) and Semantic memory organization using the category fluency task (CFT) as a measure of verbal fluency. Thirty-eight patients with schizophrenia and an equal number of normal controls entered the study. Semantic Structure was derived from multidimensional scaling analysis using sequential word outputs from the CFT. Patients with schizophrenia revealed disorganized Semantic Structure (e.g. irregular association of category members) compared with controls, consistent with previous reports. The patients were then divided into two groups, i.e. alogia- and non-alogia subjects, based on the Alogia scores from the Scale for the Assessment of Negative Symptoms (SANS). The symptom-based analysis showed that the Semantic Structure for the alogia group (Alogia score ≤2) was more disorganized than that for the non-alogia group (Alogia score
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Semantic Structure in schizophrenia as assessed by the category fluency test effect of verbal intelligence and age of onset
Psychiatry Research-neuroimaging, 2001Co-Authors: Chika Sumiyoshi, Mie Matsui, Tomiki Sumiyoshi, Ikiko Yamashita, Sawako Sumiyoshi, Masayoshi KurachiAbstract:It has been reported that long-term memory function, including the Semantic Structure of category, is impaired in patients with schizophrenia. The present study was performed to determine: (1) whether the deficit in Semantic Structure in schizophrenia is independent of cultural backgrounds, and (2) the effect of age of onset and verbal intelligence on the degradation of Semantic Structure in these patients. Fifty-seven Japanese patients with schizophrenia and 33 normal control subjects entered the study. The Semantic Structure was derived by Multidimensional Scaling (MDS) analysis based on data from the ANIMAL category fluency test. The Semantic Structure was compared between: (1) schizophrenic patients as a whole vs. normal control subjects; (2) earlier onset (age of onset 7) vs. low Vocabulary score patient groups. Normal control subjects demonstrated the domestic/size dimension in Semantic Structure, while no such dimension was obtained in patients with schizophrenia. The subgroup comparisons revealed that the later onset or the high Vocabulary score group maintained a relatively intact Semantic Structure compared with the earlier onset or the low Vocabulary score group, respectively. These findings suggest that the deficit in Semantic Structure in patients with schizophrenia is commonly observed irrespective of cultural backgrounds, and that age of onset and the level of verbal intelligence are closely related to severity of degradation of the Semantic Structure in schizophrenia.
Brita Elvevag - One of the best experts on this subject based on the ideXlab platform.
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deriving Semantic Structure from category fluency clustering techniques and their pitfalls
Cortex, 2014Co-Authors: Wouter Voorspoels, Gert Storms, Julia Longenecker, Steven Verheyen, Daniel R Weinberger, Brita ElvevagAbstract:Assessing verbal output in category fluency tasks provides a sensitive indicator of cortical dysfunction. The most common metrics are the overall number of words produced and the number of errors. Two main observations have been made about the Structure of the output, first that there is a temporal component to it with words being generated in spurts, and second that the clustering pattern may reflect a search for meanings such that the ‘clustering’ is attributable to the activation of a specific Semantic field in memory. A number of sophisticated approaches to examining the Structure of this clustering have been developed, and a core theme is that the similarity relations between category members will reveal the mental Semantic Structure of the category underlying an individual's responses, which can then be visualized by a number of algorithms, such as MDS, hierarchical clustering, ADDTREE, ADCLUS or SVD. Such approaches have been applied to a variety of neurological and psychiatric populations, and the general conclusion has been that the clinical condition systematically distorts the Semantic Structure in the patients, as compared to the healthy controls. In the present paper we explore this approach to understanding Semantic Structure using category fluency data. On the basis of a large pool of patients with schizophrenia (n = 204) and healthy control participants (n = 204), we find that the methods are problematic and unreliable to the extent that it is not possible to conclude that any putative difference reflects a systematic difference between the Semantic representations in patients and controls. Moreover, taking into account the unreliability of the methods, we find that the most probable conclusion to be made is that no difference in underlying Semantic representation exists. The consequences of these findings to understanding Semantic Structure, and the use of category fluency data, in cortical dysfunction are discussed.
