Dynamical Analyses for Developmental Science: A Primer for Intrigued Scientists

DiDonato, Matthew D.; England, Dawn E.; Martin, Carol Lynn; Amazeen, Polemnia G.

doi:10.1159/000342936

Cited by 17 publications

(7 citation statements)

References 101 publications

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“…The second reason why the traditional model of self-esteem is limiting to the study of self-esteem development is that the traditional model cannot explain the development of trait self-esteem based on internal processes (i.e., dynamics within the self-esteem system; DiDonato, England, Martin, & Amazeen, 2013). As was shortly described earlier, trait self-esteem development (or rather, a specific trajectory of development) is commonly explained by other psychological or environmental factors (see earlier examples).…”

Section: Formalizing the Traditional Approach To Trait And State Selfmentioning

confidence: 99%

Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

Ruiter

Geert

Kunnen

2017

Review of General Psychology

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The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The macro level, which is the level of trait self-esteem, the meso level, on which we find state self-esteem, and the micro level, which is the level of discrete self experiences. The model applies principles from the complex dynamics systems perspective to self-esteem, and can thus uniquely describe the underlying mechanism of self-esteem development based on self-organizational processes and interacting time scales. We compare the proposed SOSE model with a formalized account of the traditional approach to self-esteem, showing that the SOSE model is especially conducive to the understanding of self-esteem development in a way that the traditional approach is not—namely, in its ability to explain and predict the underlying dynamics of trait and state self-esteem, the meaning of variability, and the role of the context.

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Section: Formalizing the Traditional Approach To Trait And State Selfmentioning

confidence: 99%

Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

Ruiter

Geert

Kunnen

2017

Review of General Psychology

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“…Dynamic systems approaches have offered a promising way to investigate coregulatory processes as this method captures the moment-to-moment processes inherent in dyadic interaction (DiDonato et al 2013;Lunkenheimer et al 2011). Historically, operationalizing emotional processes within dyadic contexts has been difficult but in the past decade, the systems perspective has provided an exciting means for studying dyads (Hollenstein 2011).…”

Section: Dynamic Systems Analysis Approach To Studying Emotion Coregumentioning

confidence: 99%

Emotion Coregulation in Mother-Child Dyads: A Dynamic Systems Analysis of Children with and without Autism Spectrum Disorder

Guo

Garfin

et al. 2017

J Abnorm Child Psychol

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Few studies have investigated patterns of emotion coregulation in families of children with Autism Spectrum Disorder (ASD) or contrasted the ways in which their emotion coregulation patterns differ from families of typically developing (TD) children. To address this gap, we used a dynamic systems approach to compare flexible structure and emotional content of coregulation between mothers and children (3-7 years) with ASD (n = 47) and TD children (n = 26). Mother-child play interactions in the home were videotaped and emotion-engagement states were coded in micro-level 5-s intervals based on behavioral and affective expressions. Analyses indicated that mother-child dyads in the ASD group spent more time than dyads in the TD group in mismatched emotion-engagement states (e.g., child negative/mother positive), and children with ASD spent more time than TD children engaged exclusively with objects. Mother-child dyads in the TD group stayed longer in mutual positive engagement states. Compared to dyads in the TD group, mother-child dyads in the ASD group exhibited greater flexibility (i.e., a wider range of emotional-engagement states, more frequent changes in states, and less time in each state). These findings suggest that mothers and their children with ASD do not sustain dyadic positive engagement patterns in a low-stress environment. Findings confirmed the preference of children with ASD for objects over social partners, even when they are at home with their mothers, and elucidated a challenging mother-child interactional style. Results have implications for mother-child interventions aimed at regulating negative emotional states and sustaining positive ones in families raising children with ASD.

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“…Complexity science is an umbrella term that encompasses many inter-related disciplines and subsequent methodologies such as dynamical systems theory, nonlinear dynamics, and statistical mechanics. The methods and theoretical frameworks from the complexity sciences have proven useful in studying child development (Smith and Thelen, 2003 ; Vereijken, 2010 ; DiDonato et al, 2013 ; Stergiou et al, 2013 ) as well as in studying adult behavior (Kugler et al, 1980 ). Although dynamical systems theory has been fruitfully applied to developmental psychology for many years now (Thelen, 1995 ) and considerable work in the developmental sciences has made great strides in understanding the appropriate sampling properties of time and data series (Adolph et al, 2008 ), less work has focused on applying the analytic tools from complexity science to study infant limb and vocal behavior and the interplay between them.…”

Section: Introductionmentioning

confidence: 99%

Using nonlinear methods to quantify changes in infant limb movements and vocalizations

et al. 2014

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The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant's vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant's limbs and an audio recorder was worn on the child's chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant's behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior.

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Dynamical Analyses for Developmental Science: A Primer for Intrigued Scientists

Cited by 17 publications

References 101 publications

Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

Emotion Coregulation in Mother-Child Dyads: A Dynamic Systems Analysis of Children with and without Autism Spectrum Disorder

Using nonlinear methods to quantify changes in infant limb movements and vocalizations

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