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

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 ß 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 ß 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.

Parliamentary roll-call voting as a complex dynamical system: The case of Chile.

A method is proposed to study the temporal variability of legislative roll-call votes in a parliament from the perspective of complex dynamical systems. We studied the Chilean Chamber of Deputies' by analyzing the agreement ratio and the voting outcome of each vote over the last 19 years with a Recurrence Quantification Analysis and an entropy analysis (Sample Entropy). Two significant changes in the temporal variability were found: one in 2014, where the voting outcome became more recurrent and with less entropy, and another in 2018, where the agreement ratio became less recurrent and with higher entropy. These changes may be directly related to major changes in the Chilean electoral system and the composition of the Chamber of Deputies, given that these changes occurred just after the first parliamentary elections with non-compulsory voting (2013 elections) and the first elections with a proportional system in conjunction with an increase in the number of deputies (2017 elections) were held.

How Do Object Shape, Semantic Cues, and Apparent Velocity Affect the Attribution of Intentionality to Figures With Different Types of Movements?

A series of experiments show that attribution of intentionality to figures depends on the interaction between the type of movement -Theory of Mind (ToM), Goal-Directed (GD), Random (R)- with the presence of human attributes, the way these figures are labeled, and their apparent velocity. In addition, the effect of these conditions or their interaction varies when the use of human nouns -present in the participant's responses- is statistically controlled. In Experiment 1, one group of participants observed triangular figures (n = 46) and another observed humanized figures, called Stickman figures (n = 38). In ToM movements, participants attributed more intentionality to triangular figures than to Stickman figures. However, in R movements, the opposite trend was observed. In Experiment 2 (n = 42), triangular figures were presented as if they were people and compared to triangular figures presented in Experiment 1. Here, when the figures were labeled as people the attribution of intentionality only increased in R and GD movements, but not in ToM movements. Finally, in Experiment 3, Stickman figures (n = 45) move at a higher (unnatural) speed with higher frames per second (fps) than the Stickman figures of Experiment 1. This manipulation decreased the attribution of intentionality in R and GD movements but not in ToM movements. In general terms, it was found that the human attributes and labels promote the use of human nouns in participants' responses, while a high apparent speed reduces their use. The use of human nouns was associated to intentionality scores significantly in R movements, but at a lesser extent in GD and ToM movements. We conclude that, although the type of movement is the most important cue in this sort of task, the tendency to attribute intentionality to figures is affected by the interaction between perceptual and semantic cues (figure shape, label, and apparent speed).