scholarly journals Estimating semantic networks of groups and individuals from fluency data

2018 ◽  
Author(s):  
Jeffrey C Zemla ◽  
Joseph L. Austerweil
Keyword(s):  
Memory Retrieval ◽  
Process Model ◽  
Semantic Network ◽  
Estimation Method ◽  
Semantic Networks ◽  
Error Rates ◽  
Semantic Fluency ◽  
Moderate Amount ◽  
Classic Theory ◽  
Network Estimation

One popular and classic theory of how the mind encodes knowledge is an as- sociative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individual or group. We propose a novel method (dubbed U-INVITE) for estimating semantic networks from semantic fluency data (listing items from a category) based on a censored random walk model of mem- ory retrieval. We compare this method to several other methods in the literature for estimating networks from semantic fluency data. In simulations, we find that U- INVITE can recover semantic networks with low error rates given only a moderate amount of data. U-INVITE is the only known method derived from a psychologi- cally plausible process model of memory retrieval and one of two known methods that are consistent estimators of this process: if semantic memory retrieval is con- sistent with this process, the procedure will eventually estimate the true network (given enough data). We conduct the first exploration of different methods for esti- mating psychologically-valid semantic networks by comparing people’s similarity judgments of edges estimated by each network estimation method. We conclude with a discussion of best practices for estimating networks from fluency data.

scholarly journals Comparing the semantic networks of children with cochlear implants and children with typical hearing: Extending Kenett et al. (2013) by considering Hearing Age.

2021 ◽  
Author(s):  
Michaela Socher ◽  
ulrika löfkvist ◽  
Malin Wass
Keyword(s):  
Cochlear Implants ◽  
Semantic Network ◽  
Semantic Networks ◽  
Spoken Language ◽  
Chronological Age ◽  
Perceptual Quality ◽  
Semantic Fluency ◽  
Verbal Fluency Task ◽  
Significant Difference ◽  
Fluency Task

Purpose: Kenett et al. (2013) report that the sematic network of children with CI is less structured compared to the sematic network of children with TH. This study aims to evaluate if such differences are only evident if children with CI are compared to children with TH matched on chronological age, or also if they are compared to children with TH matched on hearing age. Method: The performance of a group of children with CI on a verbal fluency task was compared to the performance of a group of chronological-age matched children with TH. Subsequently, computational network analysis was used to compare the semantic network structure of the groups. The same procedure was applied to compare a group of children with CI to a group of hearing-age matched children with TH. Results: Children with CI performed significantly more poorly than children with TH matched on chronological age on a semantic fluency task and exhibited a significantly less structured semantic network. No significant difference in performance on a semantic fluency task was found between children with CI and children with TH matched on hearing-age. However, the structure of the semantic network differed significantly for the hearing age matched groups. Conclusions: Although the groups perform on the same level on a sematic fluency task, the semantic network for spoken language of children with CI is less structured compared to children with TH matched on hearing age. Reasons for this might be differences in the (perceptual) quality and the quantity of spoken language input.

The structure of L2 lexical-semantic networks as seen from a social network perspective

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuefang Feng ◽  
Jie Liu
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Semantic Network ◽  
Semantic Networks ◽  
Analysis Tool ◽  
Semantic Fluency ◽  
Efl Learners ◽  
Lexical Semantic ◽  
Fluency Task

Abstract This study employed a social network analysis tool to investigate the organization of L2 lexical-semantic networks. A total of 49 Chines EFL learners of English completed a semantic fluency task in English. A lexical-semantic network was established on the data collected from the semantic fluency task. We conducted a CONCOR analysis to distinguish the central words from the peripheral ones in the lexical-semantic network. The relevance of three distributional features to the centrality of the words in the L2 lexical-semantic network was examined respectively. In addition, we analyzed the general explanatory effect of each of the three features on centrality. The results based on the distributional features are significantly correlational and report positive explanatory effects. In addition, words of similar distributional features were found to connect in a way that reflects semantic feature effects. Finally, theoretical, methodological, and pedagogical implications of the findings were discussed.

scholarly journals Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

2021 ◽  
Vol 11 (14) ◽  
pp. 6368
Author(s):  
Fátima A. Saiz ◽  
Garazi Alfaro ◽  
Iñigo Barandiaran ◽  
Manuel Graña
Keyword(s):  
Ad Hoc ◽  
Data Augmentation ◽  
Semantic Network ◽  
Semantic Networks ◽  
Stereo Image ◽  
Generative Adversarial Networks ◽  
Specific Class ◽  
Adversarial Networks ◽  
Augmentation Techniques ◽  
Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

Semantic Network Language Generation based on a Semantic Networks Serialization Grammar

2010 ◽  
Vol 13 (3) ◽  
pp. 307-341 ◽  
Author(s):  
Yintang Dai ◽  
Shiyong Zhang ◽  
Jidong Chen ◽  
Tianyuan Chen ◽  
Wei Zhang
Keyword(s):  
Semantic Network ◽  
Semantic Networks ◽  
Language Generation ◽  
Network Language

scholarly journals Structural differences in the semantic networks of younger and older adults

2018 ◽  
Author(s):  
Dirk U. Wulff ◽  
Thomas Hills ◽  
Rui Mata
Keyword(s):  
Older Adults ◽  
Cognitive Modeling ◽  
Semantic Network ◽  
Semantic Networks ◽  
Edge Weight ◽  
Younger Adults ◽  
Age Related ◽  
Weight Distributions ◽  
Path Lengths ◽  
First Time

Cognitive science invokes semantic networks to explain diverse phenomena from reasoning to memory retrieval and creativity. While diverse approaches are available, researchers commonly assume a single underlying semantic network that is shared across individuals. Yet, semantic networks are considered the product of experience implying that individuals who make different experiences should possess different semantic networks. By studying differences between younger and older adults, we demonstrate that this is the case. Using a network analytic approach and diverse empirical data, we present converging evidence of age-related differences in semantic networks of groups and, for the first time, individuals. Specifically, semantic networks of older adults exhibited larger degrees, less clustering, and longer path lengths. Furthermore, the edge weight distributions of older adults individual networks exhibited significantly more skew and higher entropy across node pairs and, except for unrelated node pairs, less inter-individual agreement, suggesting that older adults networks are generally more distinct than younger adults networks. Our results challenge the common conception of a single semantic network shared by individuals and highlight the importance of individual differences in cognitive modeling. They also present valuable benchmarks to discern between theories of age-related changes in cognitive performance.

Memory monitoring failure in confabulation: Evidence from the semantic illusion paradigm

2010 ◽  
Vol 16 (6) ◽  
pp. 1006-1017 ◽  
Author(s):  
IRENE P. KAN ◽  
KAREN F. LAROCQUE ◽  
GINETTE LAFLECHE ◽  
H. BRANCH COSLETT ◽  
MIEKE VERFAELLIE
Keyword(s):  
Autobiographical Memory ◽  
Correct Answer ◽  
Memory Retrieval ◽  
Process Model ◽  
Verification Task ◽  
Semantic Domain ◽  
Memory Monitoring ◽  
Retrieval Monitoring

AbstractSeveral prominent models of confabulation characterize the syndrome as a failure in controlled aspects of memory retrieval, such as pre-retrieval cue specification and post-retrieval monitoring. These models have been generated primarily in the context of studies of autobiographical memory retrieval. Less research has focused on the existence and mechanisms of semantic confabulation. We examined whether confabulation extends to the semantic domain, and if so, whether it could be understood as a monitoring failure. We focus on post-retrieval monitoring by using a verification task that minimizes cue specification demands. We used the semantic illusion paradigm that elicits erroneous endorsement of misleading statements (e.g., “Two animals of each kind were brought onto the Ark by Moses before the great flood”) even in controls, despite their knowing the correct answer (e.g., Noah). Monitoring demands were manipulated by varying semantic overlap between target and foils, ranging from high semantic overlap to unrelated. We found that semantic overlap modulated the magnitude of semantic illusion in all groups. Compared to controls, both confabulators and non-confabulators had greater difficulty monitoring semantically related foils; however, elevated endorsement of unrelated foils was unique to confabulators. We interpret our findings in the context of a two-process model of post-retrieval monitoring. (JINS, 2010, 16, 1006–1017.)

Dynamic Co-Evolutions of Peace Frames in the United States, Mainland China, and Hong Kong

2018 ◽  
pp. 145-168
Author(s):  
Ke Jiang ◽  
George A. Barnett ◽  
Laramie D. Taylor ◽  
Bo Feng
Keyword(s):  
United States ◽  
Hong Kong ◽  
Semantic Network ◽  
News Coverage ◽  
Mainland China ◽  
Semantic Networks ◽  
The United States ◽  
Semantic Network Analysis ◽  
Xinhua News Agency ◽  
South China Morning

This chapter employs semantic network analysis to investigate the online database LexisNexis to study the dynamic co-evolutions of peace frames embedded in the news coverage from the Associated Press (AP--United States), Xinhua News Agency (XH--Mainland China), and South China Morning Post (SCMP—Hong Kong). From 1995 to 2014, while the war and harmony frames were relatively stable in AP and XH respectively, there was a trend toward convergence of the use of war frames between AP and XH. The convergence of semantic networks of coverage of peace in AP and XH may have left more room for SCPM to develop a unique peace frame, and the divergence of semantic networks of coverage of peace in AP and XH may lead SCPM to develop strategies of balancing the frames employed by AP and XH, thus creating a hybrid peace frame.

scholarly journals Lack of Noradrenergic Modulation of Indirect Semantic Priming

2009 ◽  
Vol 21 (3-4) ◽  
pp. 137-143 ◽  
Author(s):  
Jacquelyne S. Cios ◽  
Regan F. Miller ◽  
Ashleigh Hillier ◽  
Madalina E. Tivarus ◽  
David Q. Beversdorf
Keyword(s):  
Problem Solving ◽  
Semantic Priming ◽  
Semantic Network ◽  
Reaction Times ◽  
Semantic Relatedness ◽  
Semantic Networks ◽  
Decision Task ◽  
Dopaminergic Drugs ◽  
Main Effect ◽  
Noradrenergic Modulation

Norepinephrine and dopamine are both believed to affect signal-to-noise in the cerebral cortex. Dopaminergic agents appear to modulate semantic networks during indirect semantic priming, but do not appear to affect problem solving dependent on access to semantic networks. Noradrenergic agents, though, do affect semantic network dependent problem solving. We wished to examine whether noradrenergic agents affect indirect semantic priming. Subjects attended three sessions: one each after propranolol (40 mg) (noradrenergic antagonist), ephedrine (25 mg) (noradrenergic agonist), and placebo. During each session, closely related, distantly related, and unrelated pairs were presented. Reaction times for a lexical decision task on the target words (second word in the pair) were recorded. No decrease in indirect semantic priming occurred with ephedrine. Furthermore, across all three drugs, a main effect of semantic relatedness was found, but no main effect of drug, and no drug/semantic relatedness interaction effect. These findings suggest that noradrenergic agents, with these drugs and at these doses, do not affect indirect semantic priming with the potency of dopaminergic drugs at the doses previously studied. In the context of this previous work, this suggests that more automatic processes such as priming and more controlled searches of the lexical and semantic networks such as problem solving may be mediated, at least in part, by distinct mechanisms with differing effects of pharmacological modulation.

Configurable Product Structure Model Based on Semantic Network

2013 ◽  
Vol 655-657 ◽  
pp. 2074-2079
Author(s):  
Xin Wang ◽  
Lin Gao ◽  
Chong Chong Ji
Keyword(s):  
Knowledge Representation ◽  
Structural Model ◽  
Semantic Network ◽  
Semantic Networks ◽  
Product Structure ◽  
Semantic Relation ◽  
Product Configuration ◽  
Configuration Design ◽  
Structure Model ◽  
Representation Form

Depending on the demand of structure model in product configuration design, product types that can be configured are described and analyzed. Based on semantic networks as a kind of available knowledge representation form and Extend A/O tree, structural model of configurable product is put forward. The structural relation, assembly relation and configuring option relation are included, semantic relation among assembly parts is also expressed. Finaly, configurable node model is proposed.

HYBRID HEURISTIC SEMANTIC NETWORKS IN NATURAL LANGUAGE UNDERSTANDING

1995 ◽  
Vol 03 (04) ◽  
pp. 389-400 ◽  
Author(s):  
DAN CORBETT
Keyword(s):  
Natural Language ◽  
Semantic Network ◽  
Semantic Networks ◽  
Hybrid Network ◽  
Language Understanding ◽  
English Sentence ◽  
Hybrid Heuristic ◽  
Natural Language Text ◽  
Working Together ◽  
Language Text

It has never been demonstrated that a pure semantic analysts of an English sentence can be accomplished without any aid from a syntactic analyzer. It has therefore become interesting to demonstrate semantic systems which can be guided by fast and efficient syntactic methods. We show that non-probabilistic, abductive techniques can be used in a hybrid network to correctly interpret the meaning of an English sentence. We discuss the implementation of an abductive system which uses heuristics working together with a semantic network in an attempt to eliminate uncertainty and ambiguity in natural language text.

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