scholarly journals Remotely Close Associations: Openness to Experience and Semantic Memory Structure

2020 ◽  
Author(s):  
Alexander P. Christensen
Keyword(s):  
Semantic Memory ◽  
Graphical Models ◽  
Verbal Fluency ◽  
Semantic Network ◽  
Semantic Networks ◽  
Openness To Experience ◽  
Semantic Structure ◽  
Semantic Network Analysis ◽  
Creative Cognition ◽  
Semantic Associations

Openness to experience—the enjoyment of novel experiences, ideas, and unconventional perspectives—has shown several connections to cognition that suggest open people might have different cognitive processes than those low in openness. People high in openness are more creative, have broader general knowledge, and show greater cognitive flexibility. The associative structure of semantic memory might be one such cognitive process that people in openness differ in. In this study, 497 people completed a measure of openness to experience and verbal fluency. Three groups of high (n = 115), moderate (n = 121), and low (n = 118) openness were created to construct semantic networks—graphical models of semantic associations that provide quantifiable representations of how these associations are organized—from their verbal fluency responses. The groups were compared on graph theory measures of their respective semantic networks. The semantic network analysis revealed that as openness increased, the rigidity of the semantic structure decreased and the interconnectivity increased, suggesting greater flexibility of associations. Semantic structure also became more condensed and had better integration, which facilitates open people’s ability to reach more unique associations. These results were supported by open people coming up with more individual and unique responses, starting with less conventional responses, and having a flatter frequency proportion slope than less open people. In summary, the semantic network structure of people high in openness to experience supports the retrieval of remote concepts via short associative pathways, which promotes unique combinations of disparate concepts that are key for creative cognition.

scholarly journals Remotely Close Associations: Openness to Experience and Semantic Memory Structure

2018 ◽  
Vol 32 (4) ◽  
pp. 480-492 ◽  
Author(s):  
Alexander P. Christensen ◽  
Yoed N. Kenett ◽  
Katherine N. Cotter ◽  
Roger E. Beaty ◽  
Paul J. Silvia
Keyword(s):  
Semantic Memory ◽  
Cognitive Processes ◽  
Verbal Fluency ◽  
Semantic Networks ◽  
Openness To Experience ◽  
Memory Structure ◽  
Personality Psychology ◽  
Verbal Fluency Task ◽  
Neo Five Factor Inventory ◽  
Fluency Task

Openness to experience—the enjoyment of novel experiences and ideas—has many connections to cognitive processes. People high in openness to experience, for example, tend to be more creative and have broader general knowledge than people low in openness to experience. In the current study, we use a network science approach to examine if the organization of semantic memory differs between high and low groups of openness to experience. A sample of 516 adults completed measures of openness to experience (from the NEO Five–Factor Inventory–3 and Big Five Aspect Scales) and a semantic verbal fluency task. Next, the sample was split into half to form high ( n = 258) and low ( n = 258) openness to experience groups. Semantic networks were then constructed on the basis of their verbal fluency responses. Our results revealed that the high openness to experience group's network was more interconnected, flexible, and had better local organization of associations than the low openness to experience group. We also found that the high openness to experience group generated more responses on average and provided more unique responses than the low openness to experience group. Taken together, our results indicate that openness to experience is related to semantic memory structure. © 2018 European Association of Personality Psychology

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.

Parsing, Semantic Networks, and Political Authority Using Syntactic Analysis to Extract Semantic Relations from Dutch Newspaper Articles

2008 ◽  
Vol 16 (4) ◽  
pp. 428-446 ◽  
Author(s):  
Wouter van Atteveldt ◽  
Jan Kleinnijenhuis ◽  
Nel Ruigrok
Keyword(s):  
Network Analysis ◽  
Political Communication ◽  
Semantic Network ◽  
Semantic Networks ◽  
Syntactic Analysis ◽  
Semantic Network Analysis ◽  
Political Actors ◽  
Name Matching ◽  
Automatic Coding ◽  
Newspaper Articles

Analysis of political communication is an important aspect of political research. Thematic content analysis has yielded considerable success both with manual and automatic coding, but Semantic Network Analysis has proven more difficult, both for humans and for the computer. This article presents a system for an automated Semantic Network Analysis of Dutch texts. The system automatically extracts relations between political actors based on the output of syntactic analysis of Dutch newspaper articles. Specifically, the system uses pattern matching to find source constructions and determine the semantic agent and patient of relations, and name matching and anaphora resolution to identify political actors. The performance of the system is judged by comparing the extracted relations to manual codings of the same material. Results on the level of measurement indicate acceptable performance. We also estimate performance at the levels of analysis by using a case study of media authority, resulting in good correlations between the theoretical variables derived from the automatic and manual analysis. Finally, we test a number of substantive hypotheses with regression models using the automatic and manual output, resulting in highly similar models in each case. This suggests that our method has sufficient performance to be used to answer relevant political questions in a valid way.

scholarly journals Small Semantic Networks in Individuals with Autism Spectrum Disorder Without Intellectual Impairment: A Verbal Fluency Approach

2020 ◽  
Vol 50 (11) ◽  
pp. 3967-3987 ◽  
Author(s):  
Felicitas Ehlen ◽  
Stefan Roepke ◽  
Fabian Klostermann ◽  
Irina Baskow ◽  
Pia Geise ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Verbal Fluency ◽  
Semantic Network ◽  
Cluster Formation ◽  
Semantic Relatedness ◽  
Semantic Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Intellectual Impairment ◽  
Fitting In

Abstract Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored differences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fluency tasks (‘animals’, ‘human feature’, ‘verbs’, ‘r-words’) via curve fitting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n = 39), participants with ASD (n = 32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without affecting strength of associations or content.

scholarly journals Novel conceptual combination can dynamically reconfigure semantic memory networks

2020 ◽  
Author(s):  
Yoed Kenett ◽  
Sharon L. Thompson-Schill
Keyword(s):  
Semantic Memory ◽  
Semantic Network ◽  
Semantic Networks ◽  
Conceptual Combination ◽  
The Novel ◽  
Before And After ◽  
Conceptual Combinations ◽  
Nominal Compounds ◽  
Novel Processing ◽  
Shed Light

We do not simply have concepts; we use concepts. And, the way in which we use concepts can dynamically change the relations among them. One way to shed light on this dynamic nature is to examine how the novel processing of concepts—in our case, interpreting unfamiliar nominal compounds—might reconfigure semantic memory networks. We used network science tools to characterize properties of participants’ semantic networks (e.g., connectivity), and we compared these networks before and after participants constructed novel conceptual combinations. Furthermore, we contrasted combinations in which one attribute of one concept is used to describe another (attributive) with those in which a relation is identified to link two concepts (relational). We found that relational, but not attributive, combinations increased connectivity and lowered structure in the network. We suggest that constructing relational interpretations of compounds requires the generation of novel contexts, thus leading to greater restructuring of the semantic network.

scholarly journals Extraction and evaluation of action expression related to person perception using correlational analyses

2021 ◽  
Author(s):  
Okamoto Masahiro ◽  
Satoshi Eifuku
Keyword(s):  
Person Perception ◽  
Semantic Network ◽  
Real Life ◽  
Network Models ◽  
Semantic Networks ◽  
Point Of View ◽  
Social Knowledge ◽  
Correlational Approach ◽  
Semantic Associations ◽  
Methodological Principles

It is well known that people spontaneously infer traits when they observe behavior (spontaneous trait inference, STI). In order to make such inferences fast and efficient, our knowledge about others should be well organized. Along this line of thinking, it is suitable that our social knowledge is modeled as semantic networks in which traits are placed in the position of central nodes and linked to multiple behaviors on the basis of semantic associations. From the point of view of the semantic network models, researchers have examined their hypotheses by using cognitive memory tasks. For those tasks, researchers have to select a limited number of behavior-descriptive words/phrases as stimuli since there are vast amounts of behavior patterns in real life. There are, however, few methodological principles that adequately guide the sampling and selecting the stimuli and evaluating the semantic associations. In this setting, it seems required that words/phrases should be quantitatively sampled and selected and that the semantic associations should be objectively evaluated. A suitable approach for this purpose is the correlational analyses of free responses. In the present research, we provide evidence for the usefulness of the correlational analysis of free responses. First, we extracted behavior-descriptive words (verbs) that would exemplify trait concepts by using correspondence analysis, one of the correlational analyses (Study 1). Then, we examine the semantic associations between the extracted verbs with psychological experiments (Studies 2, 3). As a result, we found that the research participants identified the extracted verbs for specific traits, suggesting that the correlational approach is useful to reveal the organization of social knowledge. Finally, we discuss the limitations and issues of the correlational approach.

scholarly journals Revealing the Role of Divergent Thinking and Fluid Intelligence in Children’s Semantic Memory Organization

2020 ◽  
Vol 8 (4) ◽  
pp. 43
Author(s):  
Clara Rastelli ◽  
Antonino Greco ◽  
Chiara Finocchiaro
Keyword(s):  
Semantic Memory ◽  
Divergent Thinking ◽  
Network Science ◽  
Fluid Intelligence ◽  
Semantic Network ◽  
Memory Organization ◽  
Semantic Fluency ◽  
Creative Cognition ◽  
Science Methodology

The current theories suggest the fundamental role of semantic memory in creativity, mediating bottom-up (divergent thinking) and top-down (fluid intelligence) cognitive processes. However, the relationship between creativity, intelligence, and the organization of the semantic memory remains poorly-characterized in children. We investigated the ways in which individual differences in children’s semantic memory structures are influenced by their divergent thinking and fluid intelligence abilities. The participants (mean age 10) were grouped by their levels (high/low) of divergent thinking and fluid intelligence. We applied a recently-developed Network Science approach in order to examine group-based semantic memory graphs. Networks were constructed from a semantic fluency task. The results revealed that divergent thinking abilities are related to a more flexible structure of the semantic network, while fluid intelligence corresponds to a more structured semantic network, in line with the previous findings from the adult sample. Our findings confirm the crucial role of semantic memory organization in creative performance, and demonstrate that this phenomenon can be traced back to childhood. Finally, we also corroborate the network science methodology as a valid approach to the study of creative cognition in the developmental population.

scholarly journals Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

2019 ◽  
Author(s):  
Alexander P. Christensen ◽  
Yoed Kenett
Keyword(s):  
Network Analysis ◽  
Cognitive Processes ◽  
Semantic Network ◽  
Semantic Networks ◽  
Semantic Network Analysis ◽  
Real World Data ◽  
Analysis Pipeline ◽  
Psychological Phenomena ◽  
Semantic Data ◽  
R Packages

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. In this article, we aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate this pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing verbal fluency data. The third package, SemNeT, provides methods and measures for analyzing and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis.

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