ArticleFrontiers in psychology2020
Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series.
Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From data to discovery: Technology propels speech-language research and theory-building in developmental science.Neuroscience and biobehavioral reviews · 2025Pooled it
- Decoding Preschool Social Dynamics: Automated Tracking of Spatial and Temporal Patterns to Investigate Social Interactions and Relationships in Peer Groups.Developmental science · 2026Article
- A Methodological Framework to Study Change in Team Cognition Under the Dynamical Hypothesis.Topics in cognitive science · 2026Article
- Computational Modeling of Sequential Dependencies in Mother-Child Social Interaction and Associations to Empathic Responses.Developmental science · 2026Article
- Synchronizing brains and hearts: A practical guide for caregiver-child fNIRS-ECG multimodal hyperscanning.Behavior research methods · 2026Article
- Thinking Critically About Algorithms for Automated Detection of Behavior: 11 Guidelines for Social and Behavioral Scientists.Developmental science · 2026Article
- Probabilistic Cognitive State Modeling (PCSM): Decoding dynamic brain states to derive emergent cognitive processing properties from task fMRI.NeuroImage · 2026Article
- Neural regulatory networks: challenges and opportunities in the treatment of psoriasis.Frontiers in immunology · 2026Review
- Probabilistic Cognitive State Modeling (PCSM): Decoding Latent Spatiotemporal Dynamics to Reveal Serial-Parallel Processing, Cognitive Demand, and Serial Bottleneck in Task-Based fMRI.bioRxiv : the preprint server for biology · 2025Article
- Article
- A multi-level analysis of motor and behavioural dynamics in 9-month-old preterm and term-born infants during changing emotional and interactive contexts.Scientific reports · 2025Article
- Mother-child autonomic nervous system interaction as an indication of parental stress: 24-hour cross recurrence plot analysis.PloS one · 2025Article
- Beyond Words: Speech Coordination Linked to Personality and Appraisals.Journal of nonverbal behavior · 2025Article
- An automated, data-driven approach to children's social dynamics in space and time.Child development perspectives · 2024Article
- Maternal contingent responses to distress facilitate infant soothing but not in mothers with depression or infants high in negative affect.Developmental psychology · 2024Article
- Early social-cognitive development as a dynamic developmental system-a lifeworld approach.Frontiers in psychology · 2024Article
- A scoping review of interaction dynamics in minimally verbal autistic individuals.Frontiers in psychology · 2024Article
- Intentional Understanding Through Action Coordination in Early Triadic Interactions.Integrative psychological & behavioral science · 2023Article
- Parent-infant affect synchrony during social and solo play.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Sampling statistics are like story creation: a network analysis of parent-toddler exploratory play.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
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4 authors.
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Abstract
The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the non-linear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique's application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a "Programming Basics" module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.