The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach
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University of Cincinnati / OhioLINK
2014
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin13965318552021-08-03T06:23:19Z The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach Castillo Guevara, Ramon Daniel Cognitive Therapy Cognition Flow-network reasoning Pattern of performance predictive learning ecodynamic A theory, originally proposed to describe and explain the emergent organization of ecosystems, was applied to the emergence of new patterns of performance in human learning. This theory states that the emergence of new patterns in large-scale systems can be explained by the forms that adopt their flow networks in probabilistic terms. It was hypothesized that the emergence of new patterns during a learning process can be considered like the flow network of changing ecosystems. Like ecosystems, these patterns can evolve, create new configurations, keep their stability regardless the external fluctuation, and experience transformations as a function of small variations in their environment. Eco-dynamic measures of flow-network stability were applied to describe learning patterns. Five experiments were carried out, the final goals being: To determinate the extent to which Ulanowicz’s model can be adapted to characterize the emergence of cognitive patterns; to obtain a measure of grow and development in learning that is based on measures of uncertainty (H) and average mutual information (AMI); and to characterize the emergence of new patterns in terms of degree of order (alpha) and robustness (R). Across the experiments, participants had to make prediction about which of two objects would sink faster (or slower). The objects were transparent containers of different sizes that could hold a certain number of aluminum discs. The experimental session had a pre-test, a feedback-training, and a post-test phase. The contextual constraints that give rise to stable patterns of performance and their dissolution were manipulated to emulate changes in ecosystems. In Experiment 1, two presentation modes were contrasted: stimuli and feedback was conveyed either in short video clips or in pictures. In Experiment 2, using pictures, the task was to determine either which object sinks faster or sinks slower. In Experiment 3 rather than presenting each type of object pairs equally often, the frequency of pairs that most likely conflicted with participants’ prior beliefs was increased. In Experiment 4, 6- to 12-year-old children were submitted to a 2-by-2 design, in which the instruction (sink-faster vs. sink-slower) and trial types frequency were manipulated. Finally, in Experiment 5, an embodied experience of sinking objects was provided to participants during the pre-test and others during the post-test.Based on accuracy analysis, Hierarchical Cluster Analysis (HCA) and Categorical Principal Component Analysis (CATPCA), a pattern of performance based on a naive heaviness criterion was identified during the pre-test. Once the feedback was administrated this pattern was slightly modified. However few participants reached the density criterion to make the correct predictions. Eco-dynamic measures traced the fluctuation observed in the first dimension extracted by CATPCA. In all experiments, alpha and R had a significant correlation with the eigenvalue of the first dimension. This means that every time a new structure emerged, levels of order and system robustness were high too. This linear relation also was found across segments, from the pre-test to the feedback training, in which between 45% and 80% of the variability of Eigenvalue was explained by the fluctuations in alpha. 2014-10-17 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Cognitive Therapy Cognition Flow-network reasoning Pattern of performance predictive learning ecodynamic |
| spellingShingle |
Cognitive Therapy Cognition Flow-network reasoning Pattern of performance predictive learning ecodynamic Castillo Guevara, Ramon Daniel The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach |
| author |
Castillo Guevara, Ramon Daniel |
| author_facet |
Castillo Guevara, Ramon Daniel |
| author_sort |
Castillo Guevara, Ramon Daniel |
| title |
The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach |
| title_short |
The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach |
| title_full |
The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach |
| title_fullStr |
The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach |
| title_full_unstemmed |
The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach |
| title_sort |
emergence of cognitive patterns in learning: implementation of an ecodynamic approach |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2014 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855 |
| work_keys_str_mv |
AT castilloguevararamondaniel theemergenceofcognitivepatternsinlearningimplementationofanecodynamicapproach AT castilloguevararamondaniel emergenceofcognitivepatternsinlearningimplementationofanecodynamicapproach |
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