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Expertise, giftedness and insight in mathematics

2018, International Journal of Psychophysiology

Symposia / International Journal of Psychophysiology 131S (2018) S9–S68 Even though the similarities of the psychological mechanisms of insight and humour perception are proven right, the cerebral mechanisms of humour generation still await their studies. Methods: The objective of this study was to explore spatial EEG correlates of insight in creating original and humorous phrases by 78 right-handed specialists in art (the average age being 23 years) in tasks based on “droodle” pictures. EEG registration was performed by 64 derivations. The subjects were to find non-standard, original answers in the first test and humorous answers in the second one. They had to decide whether his/her decision was insight-based or not as well. To analyse each functional test, five-second artefact-less EEG segments were chosen; in each of them the coefficient of coherence values of each subject were averaged in theta, alpha, beta and gamma frequency bands. Statistical processing of data was performed by using the post hoc comparative analysis with the help of the PC software bundle “STATISTICA 12.0”. Results: In the theta band, in creating the humorous responses, the coherent connections in the posterior cortex areas of each brain hemisphere were more significant in the non-insight-based way of solving the problem other than in the insight-based one (p b0.05). In the alpha band, in finding the answer using the insight-based way, all the significant coherent connections were more powerful in case of original answer as opposed to the humorous one, excluding the connections in the right occipital cortex (p b0.05) that were most involved in the process of finding the humorous answer. In the beta band, in comparing the insight-based solution, the coherent connections were stronger in the posterior cortex area of the left hemisphere when finding an original answer, and in the posterior cortex area of the right hemisphere when finding a humorous one. In the gamma band the stronger coherent connections in the pre-frontal cortex area of the right hemisphere were reliable in differentiating the non-inside-based way of finding a humorous answer from the insight-based one (p b0.05). Conclusions: In the insight-based answers, in the low-frequency bands the EEG coherent connections in the prefrontal cortex area of the right hemisphere and the interhemispheric connections in the occipital cortex area were important, as are coherence in the posterior cortex area of both hemispheres in all explored EEG frequency bands. The occipital cortex areas of the right hemisphere turned out to be specific to the insight-based humour creation. doi:10.1016/j.ijpsycho.2018.07.113 Expertise, giftedness and insight in mathematics M. Leikin, R. Leikin, I. Waisman University of Haifa, Haifa, Israel In this research report we will discuss links between expertise in school mathematics, general giftedness and mathematical insight. Expertise is acquired by means of deliberate practice that leads to the ability to make fluent and flexible use of strategy-based processes as and when required. Mathematical insight is associated with production of original work, invention and illumination, which is an experience of suddenly realizing “how to solve a problem”. Several distinctions were introduced in the study: First, based on theories of gifted education (e.g., Milgram and Hong 2009), a distinction was made between levels of intelligence (“general giftedness,” G, determined by IQ scores higher than 130) and levels of expertise S37 (“excellence in mathematics,” EM, determined by high scores in secondary school mathematics). This was applied in the sampling procedure, whereby four research groups were designed by a varying combination of EM and G characteristics. Second, based on the theories of mathematics education, a distinction was made between the translations of different representations of mathematical objects required by the task (Kaput 1998) and different areas of mathematics (i.e., algebra and geometry), together with a third distinction between learning-based and insight-based tasks; these distinctions were implemented in the design of the research tools. The task design was determined by Pólya’s (1973) theory of problem-solving strategies. Comparison between behavioral and ERP measures associated with solving learning-based and insight-based mathematical problems will be presented. The study design led to some exciting discoveries: The distinction between general giftedness and expertise in mathematics proved to be powerful in understanding that these two characteristics, even though interrelated, are different in nature. It was also obvious that using behavioral measures only is insufficient and sometimes misleading. We will argue that effects associated with solving insight-based tasks are linked to general giftedness. In contrast, effects associated with solving learning-based tasks are linked to expertise in mathematics. The insight-based component associated with mathematical expertise when solving learning-based tasks will be analyzed. doi:10.1016/j.ijpsycho.2018.07.114 Late Afternoon Session: 5.00 – 06.30 p.m. Symposium A: Using Magnetoencephalography to study dynamics of brain activity and functional connectivity: method developments and applications (Betti V. – Rome, Italy) A MEG source reconstruction workflow A. Pascarella CNR, Roma, Italy Nowadays,performing all the data processing steps that are required for a complete MEG/EEG analysis pipeline often needstouse a multitude of software packages and in-house or custom tools (e.g. MRI segmentation, pre-processing, source reconstruction, graph theoretical analysis, statistics). This is not only cumbersome, but may also increase sources of errors and leads to a not easy reproducibility of the experiment results. Here we describe NeuroPycon, an open-source, multi-modal brain data analysis kit which provides Python-based pipelines for advanced multi-thread processing of fMRI, MEG, and EEG data, with a focus on connectivity and graph analyses [1]. NeuroPycon is based on NiPype framework [2] which facilitates data analyses by wrapping many commonly-used neuroimaging software into a common python framework. The several pipelines provided by NeuroPycon represent the different steps of data analyses (preprocessing, source reconstruction, connectivity analysis, …) and can be usedin a stand-alone mode orcan be combined within building blocks to form a larger workflow. NeuroPycon provides a common and fast framework to develop workflows for advanced neuroimaging data analyses. Several workflows have already been developed to analyze different datasets coming from either MEG and EEG studies, such as MEG sleep