The VIA Classification of Strengths and Virtues is the most commonly used model of positive personality. In this study, we used two methods of model modification to develop models for two measures of the character strengths, the VIA Inventory of Strengths-Revised and the Global Assessment of Character Strengths. The first method consisted of freeing residual covariances based on modification indices until good fit was achieved. The second was residual network modeling (RNM), which frees residual partial (...) correlations while minimizing a function that penalizes more complex models. Models based on both strategies were developed for the two questionnaires. The resulting structural models were then applied to four other samples. Though both modification procedures achieved good fit in the sample used to develop the models, only RNM resulted in adequate model fit for both measures in all cross-validation samples. This finding suggests RNM is more robust against overfitting than traditional practices. Moreover, the result supports the validity of the three-factor model of character strengths with replicability. (shrink)

There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition (...) and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal. (shrink)

Embryonic development and adult tissue homeostasis are controlled through activation of intracellular signal transduction pathways by extracellular growth factors. In the past, signal transduction has largely been regarded as a linear process. However, more recent data from large‐scale and high‐throughput experiments indicate that there is extensive cross‐talk between individual signaling cascades leading to the notion of a signaling network. The behavior of such complex networks cannot be predicted by simple intuitive approaches but requires sophisticated models and computational simulations. The (...) purpose of such models is to generate experimentally testable hypotheses and to find explanations for unexpected experimental results. Here, we discuss the need for, and the future impact of, mathematical models for exploring signal transduction in different biological contexts such as for example development. BioEssays 30:1110–1125, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary, while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are (...) ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences and car competition together using weighted directed networks. (shrink)

Based on the Valence Bond theory, an attempt is proposed to the complex network. The principle of chemical bonding of the basic particles that make up the substance creates a metaphor between the formation of social networks. By analyzing the integration of atoms by relying on the chemical bonds between particles, then the social basis for the connection between social network nodes should depend on the tangible or intangible attribute resources that characterize social capital around the main node. (...) Based on the above analysis, the social node is divided into active nodes and passive nodes, and a dynamic model of social network formation is proposed, the Valence Bond model of social network. Through this model, the actual athlete group nodes are depicted, and the representation of the model and the evolution of network structure are given with the actual data. (shrink)

Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess the (...) possibility of predicting the leading texts semantics based on the discourses ranks and their place in the respective linear sequence. Same baseline parameters were used to predict respondents' self-assessments of changes in their psychological well-being and in daily life routine during the quarantine, as well as to predict their preferences of the quarantine strategies. The study relied on basic ideas about discourse as a meaning constituted by the dispersion of other meanings (Foucault). The same dispersion mechanism realizes itself in interdiscourse interaction, forming a discursive formation at a higher level. The method of T-units (Hunt) was used to identify and count discourses in the texts. The ranking of discourses was provided based on the criterion of their semantic-syntactic autonomy. -/- Results. The conducted neural network modeling revealed a high accuracy in predicting the work of the linguistic consciousness functions associated with retrospective self-assessment and anticipatory imagination of the respondents. Another result of this modeling is a partial confirmation of the assumption concerning existence a relationship between the structural parameters of the discursive field (the rank of the discourses and their place in the respective linear sequence) and the leading semantics of the text. -/- Conclusions. A discourse approach to the study of linguistic consciousness, understanding of its structure and functioning features seems to be reasonably appropriate. The implementation of the approach presupposes the need to form a base of linguistic corpora with the inclusion in each text markup of such parameters as: the presence of specific discourses, their ranks, positions in the linear sequence of discourses. (shrink)

Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.

Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this new approach for (...) determining the number of classes in growth mixture models. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered. (shrink)

Autocatalytic networks have been used to model the emergence of self‐organizing structure capable of sustaining life and undergoing biological evolution. Here, we model the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations (MRs) of knowledge and experiences play the role of catalytic molecules, and interactions among them (e.g., the forging of new associations) play the role of reactions and result in representational redescription. The approach tags MRs with their source, that is, whether they were acquired through social (...) learning, individual learning (of pre‐existing information), or creative thought (resulting in the generation of new information). This makes it possible to model how cognitive structure emerges and to trace lineages of cumulative culture step by step. We develop a formal representation of the cultural transition from Oldowan to Acheulean tool technology using Reflexively Autocatalytic and Food set generated (RAF) networks. Unlike more primitive Oldowan stone tools, the Acheulean hand axe required not only the capacity to envision and bring into being something that did not yet exist, but hierarchically structured thought and action, and the generation of new MRs: the concepts EDGING, THINNING, SHAPING, and a meta‐concept, HAND AXE. We show how this constituted a key transition toward the emergence of semantic networks that were self‐organizing, self‐sustaining, and autocatalytic, and we discuss how such networks replicated through social interaction. The model provides a promising approach to unraveling one of the greatest anthropological mysteries: that of why development of the Acheulean hand axe was followed by over a million years of cultural stasis. (shrink)

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...) that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

Articulated arm coordinate measuring machine is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the (...) coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM. (shrink)

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375–381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727–732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, (...) produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this problem. (shrink)

Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic (...) as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.” . (shrink)

Discussion about project management, in both the academic literature and industry, is predominantly based on theories of control, many of which have been developed since the 1950s. However, issues arise when these ideas are applied unilaterally to all types of projects and in all contexts. In complex environments, management problems arise from assuming that results, predicted at the start of a project, can be sufficiently described and delivered as planned. Thus, once a project reaches a critical size, a calendar, and (...) a certain level of ambiguity and interconnection, the analysis centered on control does not function adequately. Projects that involve complex situations can be described as adaptive complex systems, consistent in multiple interdependent dynamic components, multiple feedback processes, nonlinear relations, and management of hard data and soft data. In this study, through a complex network, the dynamic structure of a project and its trajectories are simulated using inference processes. Finally, some numerical simulations are described, leading to a decision making tool that identifies critical processes, thereby obtaining better performance outcomes of projects. (shrink)

Resumen En el texto se expone un proceso de modelación basado en dos consideraciones (Sec. 2): que los modelos son autónomos y que sus metas directas son al menos tres: estar bien construidos, adecuarse al mundo empírico y ser capaces de realizar tareas subrogatorias. Para ello, se esbozan varios ingredientes fundamentales de la tarea modeladora en la lingüística basada en evidencias, así como los de un marco formal elegido para representar aquellos. La tercera sección está dedicada a aplicar el presente (...) análisis al caso de las redes complejas con el fin de que conceptos clave como la validez, la fiabilidad y la replicabilidad de la modelación, así como el concepto de diseño experimental en RC, se puedan observar desde un prisma representacional. La sección cuarta refleja las capacidades aplicativas de (RC), especialmente en el caso del aprendizaje del lenguaje en casos atípicos y a menudo socialmente marginados. A modo de cierre, la conclusión resume la puesta en práctica de la herramienta RC. Abstract A modeling process is initially outlined based on two considerations (Sec. 2), namely that models themselves are autonomous and that their direct goals are at least threefold: to be well constructed, to fit the empirical world, and to be able to perform subrogative tasks. This section outlines the fundamental elements of the modeling task in evidence-based linguistics, as well as those of a formal framework chosen to represent the former. The third section is devoted to applying the present theoretical analysis to the case of complex networks (CN), so that key concepts such as validity, reliability and replicability of modeling, as well as the concept of experimental design in complex networks, can be viewed from a representational perspective. The fourth section reflects the application capabilities of CN, especially in the case of language learning in atypical and often socially marginalized cases. Finally, the conclusion summarizes the implementation of the CN-tool. (shrink)

The competition in higher education has increased, while lecturers are involved in multiple assignments that include teaching, research and publication, consultancy, and community services. The demanding nature of academia leads to excessive work load and stress among academicians in higher education. Notably, offering the right motivational mix could lead to job satisfaction and performance. The current study aims to demonstrate the effects of extrinsic and intrinsic motivational factors influencing job satisfaction and job performance among academicians working in Malaysian private higher (...) educational institutions. Cross-sectional data were collected from the Malaysian PHEIs and the randomly selected 343 samples. The data analysis was performed with the dual analysis of partial least square structural equation modeling and artificial neural network analysis. As a result, it was found that financial rewards, promotion, performance appraisal, classroom environment, and code of conduct significantly predicted job satisfaction. The code of conduct, autonomy, and self-efficacy strongly influenced job performance. The relationship between job satisfaction and job performance was highly moderated by self-efficacy. It was suggested from the ANN analysis that the three prominent factors influencing job satisfaction are financial rewards, performance appraisal, and code of conduct. The analysis supported three significant factors influencing job performance: self-efficacy, performance appraisal, and code of conduct. The management of PHEIs should build the correct policies to transform job satisfaction into job performance. Self-efficacy plays an essential role in activating job performance. Other significant motivating factors that promote job satisfaction and performance, such as emotional intelligence, mindfulness, and other personal traits, should be included in future studies. In addition, future research could use a mixed-method or multi-respondent approach to investigate the important variables and their impact on lecturers’ job satisfaction and performance. (shrink)

Developmental processes in complex animals are directed by a hardwired genomic regulatory code, the ultimate function of which is to set up a progression of transcriptional regulatory states in space and time. The code specifies the gene regulatory networks (GRNs) that underlie all major developmental events. Models of GRNs are required for analysis, for experimental manipulation and, most fundamentally, for comprehension of how GRNs work. To model GRNs requires knowledge of both their overall structure, which depends upon linkage amongst regulatory (...) genes, and the modular building blocks of which GRNs are heirarchically constructed. The building blocks consist of basic transcriptional control processes executed by one or a few functionally linked genes. We show how the functions of several such building blocks can be considered in mathematical terms, and discuss resolution of GRNs by both “top down” and “bottom up” approaches. BioEssays 24:1118–1129, 2002. © 2002 Wiley‐Periodicals, Inc. (shrink)

In the current paper, we propose a latent interdependence approach to modeling psychometric data in social networks. The idea of latent interdependence is adopted from social relations models, which formulate a mutual-rating process by both dyad members’ characteristics. Under the framework of the latent interdependence approach, we introduce two psychometric models: The first model includes the main effects of both rating-sender and rating-receiver, and the second model includes a latent distance effect to assess the influence from the dissimilarity between (...) the latent characteristics of both sides. The latent distance effect is quantified by the Euclidean distance between both sides’ trait scores. Both models use Bayesian estimation via Markov chain Monte Carlo. How accurately model parameters were estimated was evaluated in a simulation study. Parameter recovery results showed that all parameters were accurately recovered under most of the conditions investigated. As expected, the accuracy of model estimation was significantly improved as network size grew. Also, through analyzing empirical data, we showed how to use the estimates of model parameters to predict the latent weight of connections among group members and rebuild either a univariate or multivariate network at a latent trait level. Finally, we discuss issues regarding model comparison and offer suggestions for future studies. (shrink)

Biochemical networks, including those containing signaling pathways, display a wide range of regulatory properties. These include the ability to propagate information across different time scales and to function as switches and oscillators. The mechanisms underlying these complex behaviors involve many interacting components and cannot be understood by experiments alone. The development of computational models and the integration of these models with experiments provide valuable insight into these complex systems‐level behaviors. Here we review current approaches to the development of computational models (...) of biochemical networks and describe the insights gained from models that integrate experimental data, using three examples that deal with ultrasensitivity, flexible bistability and oscillatory behavior. These types of complex behavior from relatively simple networks highlight the necessity of using theoretical approaches in understanding higher order biological functions. BioEssays 24:1110–1117, 2002. © 2002 Wiley‐Periodicals, Inc. (shrink)

This is an excerpt of a report that highlights and explores five questions which arose from The Unity of Consciousness and Sensory Integration conference at Brown University in November of 2011. This portion of the report explores the question: How should we model the unity of consciousness?

This paper proposes a new financial market model based on the analysis of the minority game model. The agent in this model forms a network through information sharing, and the agent uses the minority game model to realize the evolution of the system. To better describe the financial market, we also adopt a prior connection strategy for the model. The network formed by the agent has the characteristics of a scale-free network, and as the initial network (...) connection probability increases, the growth rate of the corresponding agent’s average connection degree increases and then decreases after reaching the peak. (shrink)

Large scale experiments at CERN’s Large Hadron Collider rely heavily on computer simulations, a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, (...) hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. In order to perform the epistemic function Winsberg Model-based reasoning in scientific discovery. Kluwer Academic/plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: holistic validation :253–262, 2010; in Carrier M, Nordmann A Science in the context of application. Springer, Berlin, pp 115–130, 2011), and model coherence. As it turns out, the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline. (shrink)

Individual neurons display characteristic firing patterns determined by the number and kind of ion channels in their membranes. We describe experimental and computational studies that suggest that neurons use activity sensors to regulate the number and kind of ion channels and receptors in their membrane to maintain a stable pattern of activity and to compensate for ongoing processes of degradation, synthesis and insertion of ion channels and receptors. We show that similar neuronal and network outputs can be produced by (...) a number of different combinations of ion channels and synapse strengths. This suggests that individual neurons of the same class may each have found an acceptable solution to a genetically determined pattern of activity, and that networks of neurons in different animals may produce similar output patterns by somewhat variable underlying mechanisms. BioEssays 24:1145–1154, 2002. © 2002 Wiley Periodicals, Inc. (shrink)

This work studies the interaction networks that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. The largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. The quoting and retweeting networks have a more hierarchical structure than (...) the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. The cumulative probability distributions of hashtags per community are also examined. (shrink)

Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or Bayes nets, can simulate (...) rational belief updating. When fit to experimental data, Bayes nets can help identify the factors that contribute to polarization. We present a study into belief updating concerning the reality of climate change in response to information about the scientific consensus on anthropogenic global warming. The study used representative samples of Australian and U.S. participants. Among Australians, consensus information partially neutralized the influence of worldview, with free-market supporters showing a greater increase in acceptance of human-caused global warming relative to free-market opponents. In contrast, while consensus information overall had a positive effect on perceived consensus among U.S. participants, there was a reduction in perceived consensus and acceptance of human-caused global warming for strong supporters of unregulated free markets. Fitting a Bayes net model to the data indicated that under a Bayesian framework, free-market support is a significant driver of beliefs about climate change and trust in climate scientists. Further, active distrust of climate scientists among a small number of U.S. conservatives drives contrary updating in response to consensus information among this particular group. (shrink)