Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring
Abstract
:1. Introduction
1.1. Temporal Dynamics of the Restructuring Process
1.2. Eye Movements and Matchstick Arithmetic Problems
1.3. Metacognitive Processes and Insight Problems
2. Methods
3. Results
3.1. Is Insight Sudden or Incremental? (Solvers vs. Non-Solvers: First 5 Min Analysis)
3.2. Do Explicit Cues Rearrange Attentional Distribution? (An Immediate Change after the Hint)
3.3. Does Metacognition Influence Insight Problem Solving? (Solvers vs. Non-Solvers after the Hint)
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
1 | The length of time taken to solve (or not solve) a problem is different from person to person, meaning that one cannot compare the eye tracking data directly between people. For example, some may need only 45 s to solve the problem, whereas others need four minutes to find a solution. In consequence, the data must be transformed in order to be able to compare the data between people properly. While the problem-solving period can be extended by adding more time phases, it is important to note that the duration should not be prolonged beyond a certain point. Utilizing too many time frames may leave too little data (e.g., a 10-second trial should not be divided into 100 bins, as each bin will have the duration of only 100 ms). This can lead to distorted eye movement patterns, masking the underlying effects present before the data were binned. On the other hand, choosing too few bins may not capture the full temporal dynamics of the problem-solving process. In either case, ANOVA is not suitable for analyzing a large number of problem-solving periods, unlike GAM and multiple change point analysis, which can easily accommodate a large number of time frames. MCP analysis is another adequate tool for this type of analysis as it can capture the shift of attention. However, in contrast to GAMs, one needs a priori knowledge about the number of change points and the form of the segments in between (Lindeløv 2020). |
2 | Please note that the data presented here are simulated to represent a sudden shift, which is difficult to capture by classical analyses. The original data in Bilalić et al. (2019) indicate a gradual shift. |
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Graf, M.; Danek, A.H.; Vaci, N.; Bilalić, M. Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring. J. Intell. 2023, 11, 86. https://doi.org/10.3390/jintelligence11050086
Graf M, Danek AH, Vaci N, Bilalić M. Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring. Journal of Intelligence. 2023; 11(5):86. https://doi.org/10.3390/jintelligence11050086
Chicago/Turabian StyleGraf, Mario, Amory H. Danek, Nemanja Vaci, and Merim Bilalić. 2023. "Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring" Journal of Intelligence 11, no. 5: 86. https://doi.org/10.3390/jintelligence11050086
APA StyleGraf, M., Danek, A. H., Vaci, N., & Bilalić, M. (2023). Tracing Cognitive Processes in Insight Problem Solving: Using GAMs and Change Point Analysis to Uncover Restructuring. Journal of Intelligence, 11(5), 86. https://doi.org/10.3390/jintelligence11050086