Data_Sheet_4_The nonlinearity of pupil diameter fluctuations in an insight task as criteria for detecting children who solve the problem from those who do not.CSV

Insights, characterized by sudden discoveries following unsuccessful problem-solving attempts, are fascinating phenomena. Dynamic systems perspectives argue that insight arises from self-organizing perceptual and motor processes. Entropy and fractal scaling are potential markers for emerging new and effective solutions. This study investigated whether specific features associated with self-organization in dynamical systems can distinguish between individuals who succeed and those who fail in solving insight tasks. To achieve this, we analyzed pupillary diameter fluctuations of children aged 6 to 12 during the 8-coin task, a well-established insight task. The participants were divided into two groups: successful (n = 24) and unsuccessful (n = 43) task completion. Entropy, determinism, recurrence ratio, and the β scaling exponent were estimated using Recurrence Quantification and Power Spectrum Density analyses. The results indicated that the solver group exhibited more significant uncertainty and lower predictability in pupillary diameter fluctuations before finding the solution. Recurrence Quantification Analysis revealed changes that went unnoticed by mean and standard deviation measures. However, the β scaling exponent did not differentiate between the two groups. These findings suggest that entropy and determinism in pupillary diameter fluctuations can identify early differences in problem-solving success. Further research is needed to determine the exclusive role of perceptual and motor activity in generating insights and investigate these results’ generalizability to other tasks and populations.

顿悟（Insight）是一类引人入胜的现象，其核心特征为历经多次失败的问题解决尝试后，突然获得突破性发现。动态系统理论视角认为，顿悟源于自组织的知觉与运动过程。熵（Entropy）与分形缩放（fractal scaling）可作为催生全新有效解决方案的潜在标志物。本研究旨在探究：动态系统中与自组织相关的特定特征，能否有效区分成功完成顿悟任务与未完成顿悟任务的个体。为达成这一目标，我们分析了6至12岁儿童在8硬币任务（8-coin task）——一项经过充分验证的经典顿悟任务——中的瞳孔直径波动情况。被试被分为两组：成功完成任务组（n=24）与未完成任务组（n=43）。本研究采用递归量化分析（Recurrence Quantification）与功率谱密度分析（Power Spectrum Density），对熵、决定论（determinism）、复发率（recurrence ratio）以及β缩放指数（β scaling exponent）进行了估算。结果显示：在找到解决方案前，成功组被试的瞳孔直径波动呈现出更显著的不确定性与更低的可预测性。递归量化分析揭示了均值与标准差指标未能捕捉到的细微变化。但β缩放指数未能有效区分两组被试。上述研究结果表明，瞳孔直径波动中的熵与决定论水平，能够识别出问题解决成功率的早期差异。未来仍需开展进一步研究，以明确知觉与运动活动在顿悟产生过程中的专属作用，并探究本研究结果在其他任务与人群中的可推广性。