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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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The Development of Spatial-Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes
Selma Dündar-Coecke1, Andrew Tolmie1, Anne Schlottmann2
1Centre for Educational Neuroscience and Department of Psychology and Human Development, UCL Institute of Education, University College London, London, United Kingdom.
Frontiers in Psychology
|March 22, 2021
Summary
Children
Area of Science:
- Cognitive Development
- Causal Reasoning
- Statistical Thinking
Background:
- Causal reasoning in children often focuses on distinct events.
- Extended, dynamic natural processes lack perceptually distinct causes and effects.
- Spatial-temporal analysis is key for understanding causality in dynamic systems.
Purpose of the Study:
- Investigate the link between children's statistical thinking and causal reasoning about dynamic processes.
- Assess the role of statistical thinking in understanding causality without distinct components.
- Examine the predictive power of spatial-temporal analysis and statistical thinking.
Main Methods:
- Two studies with 5- to 11-year-olds (N=107, N=124).
- Administered measures of covariation, probability, spatial-temporal analysis, and causal reasoning.
- Controlled for verbal and non-verbal abilities.
Main Results:
- Spatial-temporal analysis was the strongest predictor of causal thinking.
- Statistical thinking (covariation, probability) supported spatial-temporal analysis.
- Statistical thinking aids in identifying variables and understanding unseen mechanisms.
Conclusions:
- Pattern detection in data is crucial for causal analysis from childhood.
- Statistical thinking is vital for understanding causality in dynamic processes, not just distinct events.
- Spatial-temporal analysis and statistical thinking are interconnected in causal reasoning.
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