A graph theory model of the semantic structure of attitudes

The semantic structure underlying the attitudes of pretreatment and posttreatment drug addicts was modeled using a network analysis of free word associations. Measures of graph theoretic properties were used to assess structural differences in the associative networks of the two populations. These measures modeled the information processes of associative networks proposed in the spreading activation theory of semantic processing. As expected based on graph theory, the structure of the associative networks of post-treatment subjects was more dense, less constrained, and more hierarchically organized by the self concept. In a test of the network model, the subjects' evaluations of concepts in the associative network were found to be a function of their evaluations of semantically similar concepts. Although preliminary and limited, the results suggest that graph theory may provide a broad mathematical foundation for diverse models of cognitive systems.The research was funded by a grant from the National Institute for Drug Abuse to the Institute of Cultural Studies, Inc.Daytop Village.     

How semantic memory structure and intelligence contribute to creative thought: a network science approach

The associative theory of creativity states that creativity is associated with differences in the structure of semantic memory, whereas the executive theory of creativity emphasises the role of top-down control for creative thought. For a powerful test of these accounts, individual semantic memory structure was modelled with a novel method based on semantic relatedness judgements and different criteria for network filtering were compared. The executive account was supported by a correlation between creative ability and broad retrieval ability. The associative account was independently supported, when network filtering was based on a relatedness threshold, but not when it was based on a fixed edge number or on the analysis of weighted networks. In the former case, creative ability was associated with shorter average path lengths and higher clustering of the network, suggesting that the semantic networks of creative people show higher small-worldness.     

Properties of the organization of memory for people: Evidence from dream reports

Steyvers and Tenenbaum (2005) showed that semantic networks for words have three organizational properties: short average path lengths, high clustering, and power law degree distribution. If these are general properties of memory organization, they would apply to memory for other complex material, including people and relations between them. In addition, if during dreaming, characters are generated via knowledge in the dreamer’s memory, the three properties would be found in a relational network of characters in dreams. In dream reports from three individuals, two characters in the same dream were considered affiliated. Resulting social networks have the three properties, with the power law holding when low degrees are omitted. One network with a treelike outline is different from the other two. Results suggest associative memory has the three properties, and demonstrate that dream reports are a potentially valuable source for information about social networks.     

Development of brain functional connectivity and its relation to infant sustained attention in the first year of life

The study of brain functional connectivity is crucial to understanding the neural mechanisms underlying the improved behavioral performance and amplified ERP responses observed during infant sustained attention. Previous investigations on the development of functional brain connectivity during infancy are primarily confined to the use of functional and structural MRI techniques. The current study examined the relation between infant sustained attention and brain functional connectivity and their development during infancy with high‐density EEG recordings. Fifty‐nine infants were tested at 6 (N = 15), 8 (N =14), 10 (N = 17), and 12 (N = 13) months. Infant sustained attention was defined by measuring infant heart rate changes during infants’ looking. Functional connectivity was estimated from the electrodes on the scalp and with reconstructed cortical source activities in brain regions. It was found that infant sustained attention was accompanied by attenuated functional connectivity in the dorsal attention and default mode networks in the alpha band. Graph theory analyses showed that there was an increase in path length and a decrease in clustering coefficient during infant sustained attention. The functional connectivity within the visual, somatosensory, dorsal attention, and ventral attention networks and graph theory measures of path length and clustering coefficient were found to increase with age. These findings suggest that infant sustained attention is accompanied by distinct patterns of brain functional connectivity. The current findings also suggest the rapid development of functional connectivity in brain networks during infancy.     

Affective information processing and the assessment of anxiety

A conceptualization of anxiety as comprising three loosely coupled response systems of overt behavior, verbal report, and physiological activation has proven useful in clinical and theoretical work. With this framework as a starting point, an information-processing approach to the study of emotion is described. Emotions are conceived as affective programs within the brain, with information coded as propositions organized into associative networks. Affective expression results when such a network is accessed and processed, which can occur when a sufficient number of propositions are activated by environmental stimuli and/or internal associations. It is hypothesized that information about the expressive physiology is an integral component of the associative structure, and that processing of the network accordingly results in measurable psychophysiological response. Data from studies of emotional imagery, as well as other areas of research, are reviewed in support of these theories. The utility of this approach for the assessment of anxiety disorders is discussed, and results of clinical studies are presented to suggest that individual differences in accessing and processing emotional information may bear significant implications for prognosis and treatment selection. It is speculated that differences among the anxiety disorders could be interpreted in terms of the degree of cognitive organization of the network, leading to potential refinement of current diagnostic categories. In conclusion, cognitive psychology paradigms are discussed in terms of their application to the assessment and treatment of anxiety disorders.The authors' research is supported in part by NIMH Grant MH 37757. Portions of this paper were presented at the NIMH Conference on Anxiety, Tuxedo, NY, September 1983. Several of its themes were developed there at more length, and this material may be found in the publication of the conference proceedings (Tuma & Maser, 1985).     

Altered topological patterns of large-scale brain functional networks during passive hyperthermia

In this study, we simulated environmental heat exposure to 18 participants, and obtained functional magnetic resonance image (fMRI) data during resting state. Brain functional networks were constructed over a wide range of sparsity threshold according to a prior atlas dividing the whole cerebrum into 90 regions. Results of graph theoretical approaches showed that although brain networks in both normal and hyperthermia conditions exhibited economical small-world property, significant alterations in both global and nodal network metrics were demonstrated during hyperthermia. Specifically, a lower clustering coefficient, maintained shortest path length, a lower small-worldness, a lower mean local efficiency were found, indicating a tendency shift to a randomized network. Additionally, significant alterations in nodal efficiency were found in bilateral gyrus rectus, bilateral parahippocampal gyrus, bilateral insula, right caudate nucleus, bilateral putamen, left temporal pole of middle temporal gyrus, right inferior temporal gyrus. In consideration of physiological system changes, we found that the alterations of normalized clustering coefficient, small-worldness, mean normalized local efficiency were significantly correlated with the rectal temperature alteration, but failed to obtain significant correlations with the weight loss. More importantly, behavioral attention network test (ANT) after MRI scanning showed that the ANT effects were altered and correlated with the alterations of some global metrics (normalized shortest path length and normalized global efficiency) and prefrontal nodal efficiency (right dorsolateral superior frontal gyrus, right middle frontal gyrus and left orbital inferior frontal gyrus), implying behavioral deficits in executive control effects and maintained alerting and orienting effects during passive hyperthermia. The present study provided the first evidence for human brain functional disorder during passive hyperthermia according to graph theoretical analysis using resting-state fMRI.     

Graph‐Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over‐predicted clustering in the norms, whereas the associative model under‐predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts.     

Short-term meditation induces changes in brain resting EEG theta networks

Many studies have reported meditation training has beneficial effects on brain structure and function. However, very little is known about meditation-induced changes in brain complex networks. We used network analysis of electroencephalography theta activity data at rest before and after 1-week of integrative body–mind training (IBMT) and relaxation training. The results demonstrated the IBMT group (but not the relaxation group) exhibited significantly smaller average path length and larger clustering coefficient of the entire network and two midline electrode nodes (Fz and Pz) after training, indicating enhanced capacity of local specialization and global information integration in the brain. The findings provide the evidence for meditation-induced network plasticity and suggest that IBMT might be helpful for alterations in brain networks.     

Understanding Interdisciplinary Collaborations as Social Networks

The dynamics of interdisciplinary collaboration invite further investigation if we are to make this endeavour more rewarding and productive. We are using social network analysis to track the development of a new interdisciplinary collaboration on complex interventions to improve population health. It involves nineteen scholars across four countries. We report the Baseline network of formal relationships among the scholars, along with the impact of the collaboration on these relationships in the first 18 months. We observed statistically significant increases in the density of six types of relationship networks: citing publications by other members of the collaboration, email contact, meeting with each other (outside of the formal annual meeting), visiting one another's institution, submitting research grants together and working on research projects together. The initial strategic role in the network of key 'gate keepers' has not altered substantially (betweenness centralization of the networks), but reciprocity has increased, that is, people are more likely to cite those who have cited them and work together. Increased collaboration is also reflected in the rise in number of subgroups over time and the increase in the average number of subgroup memberships. Use of social network analysis to understand the dynamics of interdisciplinary collaborations is a relatively new field. It invites reflection about what the optimal network structures for interdisciplinary collaborations would look like.     

Network Knowledge Organization: Do Knowledge Structures for Sexual and Emotional Information Reflect Gender or Sexual Orientation?

Network theory conceptualizes the organizationof semantic information as nodes within a net-likestructure. Links within the network representassociations among knowledge structures or concepts andhave been viewed as forming the basis of humansemantic memory (Quillian, 1962). Network models haverecently been employed in emotion theory (Bower, 1981;Lang, 1984) and in research in human sexuality (Geer, 1996; Rabalais & Geer, 1996; Smith,Eggleston, Gerrard, & Gibbons, 1996). As a result ofthat research, stable gender differences in theorganization of knowledge for sexual and emotionalinformation have begun to be identified (see Geer &Manguno-Mire, 1996, for a review). These differences inknowledge organization have been shown to map well ontoexisting research demonstrating gender differences in sexual attitudes, fantasies, and behavior(Geer & Manguno-Mire). The present study employs thePathfinder computer algorithm (Schvaneveldt, 1990) tocompare the cognitive associative networks of heterosexual men and women and gay men andlesbians. Ninety homosexuals (49 gay men and 41lesbians) and 95 heterosexuals (48 men and 47women)rated all pair-wise combinations of 16 wordsrelevant to sexuality and emotion. The sample was predominantlyCaucasian (90%). Four percent of participants wereAfrican-American, 3% were Hispanic American, 1% wereAsian American. Analysis of our dependent variable(number of links on relevant concepts) revealed genderand sexual orientation differences in the total numberof links in associative networks, the number of linkswithin relevant sexual and emotional word clusters, between relevant word clusters, and onindividual words. Results are interpreted in light ofrelevant theories of gender and sexualorientation.     

Anxious personality and functional efficiency of the insular-opercular network: A graph-analytic approach to resting-state fMRI

The brain is an intricate network, not only structurally but also functionally. On the functional level, connectivity in the brain is organized in separable yet interacting networks that support information processing by maintaining a ready state, even in the absence of external stimulation. It has been hypothesized that an insular-opercular network underlies the processing of emotionally salient information and that individual differences in functional connectivity within this network correspond to individual differences in trait anxiety. Here, we tested this relationship by applying graph analysis to multiple regions of interests delineating the insular-opercular network to estimate the characteristic path length that quantifies the overall information exchange efficiency within a given network. We found that people scoring high on the anxiety-related temperament-dimension harm avoidance had decreased insular-opercular network efficiency in the resting state, as indicated by a higher characteristic path length. Furthermore, people scoring high on harm avoidance showed generally reduced functional connectivity between brain regions; the relationship between harm avoidance and insular-opercular network efficiency remained significant when controlling for mean connectivity within this network. No such results were found for other resting-state networks. The results provide insights into how personality is organized in the human brain and point toward clinically relevant endophenotypes for affective and mood disorders.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

Novel associative processing and aging: effect on creative production

ABSTRACT

Creative production has been reported to decline with age. This study investigated age-related differences in creative verbal production. Participants were 30 younger and 30 older adults. Study testing included language and cognitive assessments and the experimental task wherein participants created short stories incorporating words that are not commonly related, semantically or associatively. The resulting stories were rated by independent blinded judges for originality, cohesion, appropriateness, and organization. Younger adults' stories were rated as being significantly more original and more appropriate. Integrating unrelated words to create original stories requires activating widely distributed lexical-semantic networks to develop novel associations. Potential decreased neural network connectivity due to white matter degradation, commonly seen in normal aging, as well as decrements in front-executive disengagement may have affected older adults' performance on this story creation task. Future research using neuroimaging may elucidate possible neuroanatomic correlates of age-related changes in associative creative production..     

Adaptive rewiring in weighted networks

Human connectome studies suggest that the brain has a modular small world network structure with rich-club effect. Such structure emerges spontaneously in simple model neural networks, (e.g. coupled maps), through adaptive rewiring according to the dynamic functional connectivity. The utility of adaptive rewiring has so far exclusively been demonstrated for unweighted networks; it is anything but guaranteed to work as well for weighted networks. We investigate adaptive rewiring in weighted networks, comparing various right-skewed, symmetrical, and left-skewed fixed weight distributions. We examine how network clustering, path length, modularity, and rich club coefficients develop for weakly, intermediate and strongly coupled networks. At low coupling strength, the weight distribution, as well as episodes of functional synchrony, have a significant effect on network evolution. With increased coupling strengths, all weighted networks robustly develop architectures similar to the unweighted ones. Adaptive rewiring appears relatively ineffective in networks with (biologically implausibly) extreme right-skewed weight distributions but performed most economically in biologically plausible log-normal distributions.     

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.     

Advice networks in teams: the role of transformational leadership and members' core self-evaluations

This article examines the team-level factors promoting advice exchange networks in teams. Drawing upon theory and research on transformational leadership, team diversity, and social networks, we hypothesized that transformational leadership positively influences advice network density in teams and that advice network density serves as a mediating mechanism linking transformational leadership to team performance. We further hypothesized a 3-way interaction in which members' mean core self-evaluation (CSE) and diversity in CSE jointly moderate the transformational leadership-advice network density relationship, such that the relationship is positive and stronger for teams with low diversity in CSE and high mean CSE. In addition, we expected that advice network centralization attenuates the positive influence of network density on team performance. Results based on multisource data from 79 business unit management teams showed support for these hypotheses. The results highlight the pivotal role played by transformational leadership and team members' CSEs in enhancing team social networks and, ultimately, team effectiveness.     

Communication and Structured Correlation

Philosophers and social scientists have recently turned to Lewis sender–receiver games to provide an account of how lexical terms can acquire meaning through an evolutionary process. However, the evolution of meaning is contingent on both the particular sender–receiver game played and the choice of evolutionary dynamic. In this paper I explore some differences between models that presume an infinitely large and randomly mixed population and models in which a finite number of agents communicate with their neighbors in a social network. My results show that communication with neighbors is more conducive to the evolution of meaning than communication with strangers. Additionally, I show that the behavior of the system is highly dependent on the topological structure of the social network. I argue that a specific class of networks—small world graphs—is especially conducive to the evolution of meaning. This is because small world graphs have a short characteristic path length while still maintaining a high degree of correlation between neighbors. Since many actual social networks, such as friendship networks and nervous systems, are conjectured to be small world structures, these results indicate that these networks are quite hospitable to the efficient evolution of meaning.     

Optimization of Social Structure in Business Networks by Grid-Technique and Polyhedral Analysis

This article deals with the definition and analysis of the social, communicative, and cognitive abilities of managers acting in network contexts. We can tackle the complex relationship of role demands and role adoption by applying grid-technique and polyhedral analysis. We can gather data on context-specific personal constructs with the help of the grid technique. Using these data, polyhedral analysis shows how defined persons are integrated into the relation structures within a network. Beyond this, we show if and how relevant criteria such as holes, connectivity, and eccentricity can be employed for analyzing problems stemming from personnel management. Based upon the applied methods, it is possible to provide information about the relevant structures within networks used in personnel selection and competency development. Decision makers have the task to select among the measures of personnel management. Within the relevant context, these measures should be suited to ensure the stability and efficiency of the network.     

Implicitly activated memories are associated to general context cues

Words having more densely entangled associative structures are more likely to be recalled in the presence of related extralist cues. A context-modified PIER2 model predicts that the implicit activation of such structures during study connects them to the context of the learning episode. In two experiments, we evaluated this assumption by varying the associative density of the study words and the accessibility of context information. In Experiment 1, we varied context accessibility by manipulating context awareness and by delaying testing and filling the delay with different types of interfering tasks. In Experiment 2, we varied accessibility by manipulating test delay and type of interference in a factorial design. The effects of associative density were reduced by using implicit testing that never refers to context, by performing interpolated interference tasks, and finally, by extending the length of the delay. Information that is implicitly activated during an episodic experience is associated to the context of that experience.     

Development of memory structures for homographs using pathfinder network representations

Some studies with children have shown that there is no semantic priming at short stimulus onset asynchrony (SOA) in lexical decision and naming tasks for homographs. The predictions of spreading activation theories might explain this missing effect. There may be differences in children's and adults' memory structures. We have explored this hypothesis. The development of memory structure representations for homographs was measured by a Pathfinder algorithm. In Experiment 1, the three dependent variables were: the number of links in the network, closeness measures (C), and distances between nodes. Results revealed developmental differences in network structure representations in adults and children. In Experiment 2, results revealed that these differences were not due to the cohort effect. In Experiment 3, the relationship between associative strength, as measured by associative norms, and distances, as measured by Pathfinder algorithm, was explored. The results of these three experiments and empirical research from semantic priming experiments show that these differences in memory structure representations could be one of the sources of the missing semantic priming effect in children.