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To test the model in a physical environment, we implemented it in a vision-equipped robot, and the model solved the same insight problem from camera percepts.
We address these aims by (a) modeling an example of insight problem solving and comparing it to empirical data, and (b) testing the model on a robot platform.
To address this issue, we tested healthy young and old volunteers on an insight problem solving task, involving both explicit and implicit features, before and after a night of sleep or a comparable wakefulness period.
In this article, we discuss in detail the methodological challenges that brain research on insight poses and describe how we dealt with these challenges in our recent studies on insight problem solving.
Importantly, the kind of insight problem solving paradigm we used (and, arguably, any insight problem solving) involves several neural networks.
Specifically, "event" is defined as solving the insight problem (Type 2) during the testing phase.
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For example, some of these insight problems could be solved in non-insightful ways [45] yet still with the "Aha!" [54].
Yet in an authoritative review of the research, the psychologists Jonathan W. Schooler and Joseph Melcher concluded that the abilities most strongly correlated with insight problem-solving "were not significantly correlated" with solving analytical problems.
At first, the meditator couldn't solve any of the insight problems.
And there's another problem, too: the answers to insight problems, which are one of the main things we can study, sometimes come from forces that are entirely mundane.
Meanwhile, in a study published last year, German researchers found that people with schizotypy a mental condition that resembles schizophrenia, albeit with far less severe symptoms were significantly better at solving insight problems than a control group.
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