scholarly journals Equilibria in Personality States: A Conceptual Primer for Dynamics in Personality States

2020 ◽  
Vol 34 (6) ◽  
pp. 999-1016 ◽  
Author(s):  
Alexander F. Danvers ◽  
Richard Wundrack ◽  
Matthias Mehl
Keyword(s):  
Dynamic Systems ◽  
Time Series Data ◽  
Systems Approach ◽  
Equilibrium Points ◽  
Personality Development ◽  
Series Data ◽  
Basic Step ◽  
Personality Psychology ◽  
Systems Model ◽  
Highly Correlated

We provide a basic, step–by–step introduction to the core concepts and mathematical fundamentals of dynamic systems modelling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as Outcome modelling also allows for estimating nuanced effects of personality development after significant life changes, separating effects on characteristic states after the significant change and how strongly she or he is drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of ‘repulsive’ personality states and ‘ambivert’ personality structures. © 2020 European Association of Personality Psychology

scholarly journals Equilibria in Personality States: A Conceptual Primer for Dynamical Analyses

2019 ◽  
Author(s):  
Alexander Francois Danvers ◽  
Richard Wundrack ◽  
Matthias R. Mehl
Keyword(s):  
Dynamic Systems ◽  
Time Series Data ◽  
Systems Approach ◽  
Equilibrium Points ◽  
Personality Development ◽  
Series Data ◽  
Basic Step ◽  
Systems Model ◽  
Highly Correlated ◽  
Using Data

We provide a basic, step-by-step introduction to the core concepts and mathematical fundamentals of dynamic systems modeling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles (PAIRS) study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as outcome modeling also allows for the effects of personality development after significant life changes to be conceptualized in more nuanced ways, separating effects on characteristic states after the significant change and how people are drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of “repulsive” personality states and “ambivert” personality structures. Supplementary materials: https://osf.io/dps4w.

A Dynamic Dyadic Systems Approach to Interpersonal Communication

2021 ◽  
Author(s):  
Denise Haunani Solomon ◽  
Miriam Brinberg ◽  
Graham D Bodie ◽  
Susanne Jones ◽  
Nilam Ram
Keyword(s):  
Interpersonal Communication ◽  
Systems Analysis ◽  
Time Series Data ◽  
Systems Approach ◽  
Dynamic Structure ◽  
Relevant Information ◽  
Dyadic Interaction ◽  
Series Data ◽  
Conversational Units ◽  
Dyadic Systems

Abstract This article articulates conceptual and methodological strategies for studying the dynamic structure of dyadic interaction revealed by the turn-to-turn exchange of messages between partners. Using dyadic time series data that capture partners’ back-and-forth contributions to conversations, dynamic dyadic systems analysis illuminates how individuals act and react to each other as they jointly construct conversations. Five layers of inquiry are offered, each of which yields theoretically relevant information: (a) identifying the individual moves and dyadic spaces that set the stage for dyadic interaction; (b) summarizing conversational units and sequences; (c) examining between-dyad differences in overall conversational structure; (d) describing the temporal evolution of conversational units and sequences; and (e) mapping within-dyad dynamics of conversations and between-dyad differences in those dynamics. Each layer of analysis is illustrated using examples from research on supportive conversations, and the application of dynamic dyadic systems analysis to a range of interpersonal communication phenomena is discussed.

Model Validation of Dynamic Engineering Models Under Uncertainty

2016 ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Eigen Analysis ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments, which may stem from computer model instability, imperfection in material fabrication and manufacturing process, and variations in experimental conditions. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, Eigen analysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen-Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data fusion strategy, probability integral transform is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant restraint system.

Updating the dual systems model of temporal cognition: Reasoning with dynamic systems theory

2019 ◽  
Vol 42 ◽  
Author(s):  
Annette Hohenberger
Keyword(s):  
Systems Theory ◽  
Time Scales ◽  
Dynamic Systems ◽  
Systems Approach ◽  
Temporal Reasoning ◽  
Dynamic Systems Theory ◽  
Dual Systems ◽  
Temporal Cognition ◽  
Systems Model ◽  
Two Systems

Abstract This commentary construes the relation between the two systems of temporal updating and temporal reasoning as a bifurcation and tracks it across three time scales: phylogeny, ontogeny, and microgeny. In taking a dynamic systems approach, flexibility, as mentioned by Hoerl & McCormack, is revealed as the key characteristic of human temporal cognition.

scholarly journals How real are observed trends in small correlated datasets?

2019 ◽  
Vol 6 (3) ◽  
pp. 181089 ◽  
Author(s):  
S. J. Salamon ◽  
H. J. Hansen ◽  
D. Abbott
Keyword(s):  
Carbon Dioxide ◽  
Climate Model ◽  
Time Series Data ◽  
Carbon Dioxide Concentration ◽  
Significant Trend ◽  
Series Data ◽  
Model Errors ◽  
Data Points ◽  
Highly Correlated ◽  
Watson Statistic

The eye may perceive a significant trend in plotted time-series data, but if the model errors of nearby data points are correlated, the trend may be an illusion. We examine generalized least-squares (GLS) estimation, finding that error correlation may be underestimated in highly correlated small datasets by conventional techniques. This risks indicating a significant trend when there is none. A new correlation estimate based on the Durbin–Watson statistic is developed, leading to an improved estimate of autoregression with highly correlated data, thus reducing this risk. These techniques are generalized to randomly located data points in space, through the new concept of the nearest new neighbour path. We describe tests on the validity of the GLS schemes, allowing verification of the models employed. Examples illustrating our method include a 40-year record of atmospheric carbon dioxide, and Antarctic ice core data. While more conservative than existing techniques, our new GLS estimate finds a statistically significant increase in background carbon dioxide concentration, with an accelerating trend. We conclude with an example of a worldwide empirical climate model for radio propagation studies, to illustrate dealing with spatial correlation in unevenly distributed data points over the surface of the Earth. The method is generally applicable, not only to climate-related data, but to many other kinds of problems (e.g. biological, medical and geological data), where there are unequally (or randomly) spaced observations in temporally or spatially distributed datasets.

scholarly journals Corticosteroid-regulated genes in rat kidney: mining time series array data

2005 ◽  
Vol 289 (5) ◽  
pp. E870-E882 ◽  
Author(s):  
Richard R. Almon ◽  
William Lai ◽  
Debra C. DuBois ◽  
William J. Jusko
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Binding Sites ◽  
Time Series Data ◽  
Rat Kidney ◽  
Microarray Dataset ◽  
Series Data ◽  
Dna Binding Sites ◽  
Highly Correlated ◽  
Major Target

Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expressed in the tissue, not regulated by drug, or did not meet defined quality control standards. These filters eliminated 14,890 probe sets (94%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series datasets. The remaining data can then be further analyzed by clustering and mathematical modeling. Initial analysis of this filtered dataset identified a group of genes whose pattern of regulation was highly correlated with prototype corticosteroid enhanced genes. Twenty genes in this group, as well as selected genes exhibiting either downregulation or no regulation, were analyzed for 5′ GRE half-sites conserved across species. In general, the results support the hypothesis that the existence of conserved DNA binding sites can serve as an important adjunct to purely analytic approaches to clustering genes into groups with common mechanisms of regulation. This dataset, as well as similar datasets on liver and muscle, are available online in a format amenable to further analysis by others.

Dynamic Predictions from Time Series Data — An Artificial Neural Network Approach

1997 ◽  
Vol 08 (06) ◽  
pp. 1345-1360 ◽  
Author(s):  
D. R. Kulkarni ◽  
J. C. Parikh ◽  
A. S. Pandya
Keyword(s):  
Time Series ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Computer Experiments ◽  
Series Data ◽  
Optimal Size ◽  
Good Prediction ◽  
Ann Model ◽  
Artificial Neural

A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model a time series generated by complex dynamic systems. We introduce well-known features used in the study of dynamic systems — time delay τ and embedding dimension d — for ANN modeling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture, models the time series and gives good prediction. As a consequence, we have an integrated and systematic data-driven scheme for modeling time series data. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two time series, to simulate real world effects. We find that up to a limit introduction of noise leads to a smaller network with good generalizing capability.

scholarly journals A generalised approach to detect selected haplotype blocks in Evolve and Resequence experiments

2019 ◽  
Author(s):  
Kathrin A. Otte ◽  
Christian Schlötterer
Keyword(s):  
Ad Hoc ◽  
Time Series Data ◽  
Window Size ◽  
Series Data ◽  
Separate Analysis ◽  
Nucleotide Polymorphisms ◽  
Haplotype Blocks ◽  
Adaptive Architecture ◽  
Block Based ◽  
Highly Correlated

AbstractShifting from the analysis of single nucleotide polymorphisms to the reconstruction of selected haplotypes greatly facilitates the interpretation of Evolve and Resequence (E&R) experiments. Merging highly correlated hitchhiker SNPs into haplotype blocks reduces thousands of candidates to few selected regions. Current methods of haplotype reconstruction from Pool-Seq data need a variety of data-specific parameters that are typically defined ad hoc and require haplotype sequences for validation. Here, we introduce haplovalidate, a tool which detects selected haplotypes in a broad range of Pool-seq time series data without the need of sequenced haplotypes. Haplovalidate makes data-driven choices of two key parameters for the clustering procedure, the minimum correlation between SNPs constituting a cluster and the window size. Applying haplovalidate to simulated and experimental E&R data reliably detects selected haplotype blocks with low false discovery rates – independent if few or many selection targets are included. Our analyses identified an important restriction of the haplotype block-based approach to describe the genomic architecture of adaptation. We detected a substantial fraction of haplotypes containing multiple selection targets. These blocks were considered as one region of selection and therefore led to under-estimation of the number of selection targets. We demonstrate that the separate analysis of earlier time points can significantly increase the separation of selection targets into individual haplotype blocks. We conclude that the analysis of selected haplotype blocks has a large potential for the characterisation of the adaptive architecture with E&R experiments.

scholarly journals Dynamic Systems Approach in Sensorimotor Synchronization: Adaptation to Tempo Step-Change

2021 ◽  
Vol 12 ◽  
Author(s):  
Nima Darabi ◽  
U. Peter Svensson
Keyword(s):  
Dynamic Systems ◽  
Systems Approach ◽  
Linear Time ◽  
Sensorimotor Synchronization ◽  
Linear Functions ◽  
Model Complexity ◽  
Step Response ◽  
Model Parameters ◽  
Step Size ◽  
Systems Model

This paper presents a dynamic systems model of a sensorimotor synchronization (SMS) task. An SMS task typically gives temporally discrete human responses to some temporally discrete stimuli. Here, a dynamic systems modeling approach is applied after converting the discrete events to regularly sampled time signals. To collect data for model parameter fitting, a previously published pilot study was expanded. Three human participants took part in an experiment: to tap a finger on a keyboard, following a metronome which changed tempo in steps. System identification was used to estimate the transfer function that represented the relationship between the stimulus and the step response signals, assuming a separate linear, time-invariant system for each tempo step. Different versions of model complexity were investigated. As a minimum, a second-order linear system with delay, two poles, and one zero was needed to model the most important features of the tempo step response by humans, while an additional third pole could give a somewhat better fit to the response data. The modeling results revealed the behavior of the system in two distinct regimes: tempo steps below and above the conscious awareness of tempo change, i.e., around 12% of the base tempo. For the tempo steps above this value, model parameters were derived as linear functions of step size for the group of three participants. The results were interpreted in the light of known facts from other fields like SMS, psychoacoustics and behavioral neuroscience.

Validating Dynamic Engineering Models Under Uncertainty

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Dynamic Systems ◽  
Computational Models ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Practical Applications ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, eigenanalysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen–Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data-fusion strategy, probability integral transform (PIT) is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant-restraint system.

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