Cognitive science as complexity science

Favela, Luis H.

doi:10.1002/wcs.1525

Cited by 34 publications

(26 citation statements)

References 156 publications

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“…SRL includes dynamically and accurately monitoring and regulating cognitive, affective, metacognitive, and motivational processes and adapting them to meet the internal (e.g., evolving understanding) and external demands and constraints of an activity (Azevedo et al, 2019 ). According to Favela ( 2020 ) and the assumptions of Winne's ( 2018 ) COPES model, complexity science offers a lens to understand and analyze cognitive and psychological processes that emerge as a function of complex systems. Complex systems theory describes how systems that demonstrate changing behavior due to interacting components can be explained and predicted (Favela, 2020 ).…”

Section: Defining Srl As a Complex Systemmentioning

confidence: 99%

“…According to Favela ( 2020 ) and the assumptions of Winne's ( 2018 ) COPES model, complexity science offers a lens to understand and analyze cognitive and psychological processes that emerge as a function of complex systems. Complex systems theory describes how systems that demonstrate changing behavior due to interacting components can be explained and predicted (Favela, 2020 ). For the current study, we align this framework with SRL literature in which learners' conditions, operations, and products are components of SRL that change and interact with each other as learning occurs.…”

Section: Defining Srl As a Complex Systemmentioning

confidence: 99%

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Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis

et al. 2022

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Undergraduate students (N = 82) learned about microbiology with Crystal Island, a game-based learning environment (GBLE), which required participants to interact with instructional materials (i.e., books and research articles, non-player character [NPC] dialogue, posters) spread throughout the game. Participants were randomly assigned to one of two conditions: full agency, where they had complete control over their actions, and partial agency, where they were required to complete an ordered play-through of Crystal Island. As participants learned with Crystal Island, log-file and eye-tracking time series data were collected to pinpoint instances when participants interacted with instructional materials. Hierarchical linear growth models indicated relationships between eye gaze dwell time and (1) the type of representation a learner gathered information from (i.e., large sections of text, poster, or dialogue); (2) the ability of the learner to distinguish relevant from irrelevant information; (3) learning gains; and (4) agency. Auto-recurrence quantification analysis (aRQA) revealed the degree to which repetitive sequences of interactions with instructional material were random or predictable. Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. Results from hierarchical clustering found that participants with restricted agency and more recurrent action sequences had greater learning gains. Implications are provided for how learning unfolds over learners' time in game using a non-linear dynamical systems analysis and the extent to which it can be supported within GBLEs to design advanced learning technologies to scaffold self-regulation during game play.

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Section: Defining Srl As a Complex Systemmentioning

confidence: 99%

Section: Defining Srl As a Complex Systemmentioning

confidence: 99%

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis

et al. 2022

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“…One approach to the investigation of synchronization in multi-component systems is investigating the level of temporal regularity of state sequences. Since complex systems have many components (Amazeen, 2018;Favela, 2020), they exhibit a large number of possible states and can cycle through varied combinations of these behavioral states. When the components of the system are synchronized, then the system will exhibit a smaller number of different states and it will do so with increased repetitiveness.…”

Section: Symbolic Entropymentioning

confidence: 99%

multiSyncPy: A Python Package for Assessing Multivariate Coordination Dynamics

Hudson¹,

Wiltshire²,

Atzmueller³

2021

Preprint

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In order to support the burgeoning field of research into interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification, coherence, the cluster-phase ‘Rho’ metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels. Taken together, our collation and presentation of these make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy, and also available at the Python package index.

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“… Note : Based on Breakspear (2017), Cocchi, Gollo, Zalesky, and Breakspear (2017), Favela (2020), Pipes (1959), Riley and Holden (2012), and Socolar (2006). …”

Section: Dynamical Systems Theorymentioning

confidence: 99%

Dynamical systems theory in cognitive science and neuroscience

Favela

2020

Philosophy Compass

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Dynamical systems theory (DST) is a branch of mathematics that assesses abstract or physical systems that change over time. It has a quantitative part (mathematical equations) and a related qualitative part (plotting equations in a state space). Nonlinear dynamical systems theory applies the same tools in research involving phenomena such as chaos and hysteresis. These approaches have provided different ways of investigating and understanding cognitive systems in cognitive science and neuroscience. The ‘dynamical hypothesis’ claims that cognition is and can be understood as dynamical systems. Common consequences for such an approach include rejecting understanding cognition as information processing in nature, including eschewing explanatory roles for computation or representation. Contemporary applications of DST include mouse‐tracking studies in cognitive science and nonrepresentational perspectives on motor control in neuroscience. Such work has philosophical implications concerning the boundaries of cognition, explanation, and representations. DST offers powerful methodology and theories that raise many topics of philosophical significance.

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Cognitive science as complexity science

Cited by 34 publications

References 156 publications

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis

multiSyncPy: A Python Package for Assessing Multivariate Coordination Dynamics

Dynamical systems theory in cognitive science and neuroscience

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