Explanation of emotion regulation mechanism of mindfulness using a brain function model.

The emotion regulation mechanism of mindfulness plays an important role in the stress reduction effect. Many researchers in the fields of cognitive psychology and cognitive neuroscience have attempted to elucidate this mechanism by documenting the cognitive processes that occur and the neural activities that characterize each process. However, previous findings have not revealed the mechanism of information propagation in the brain that achieves emotion regulation during mindfulness. In this study, we constructed a functional brain model based on its anatomical network structure and a computational model representing the propagation of information between brain regions. We then examined the effects of mindfulness meditation on information propagation in the brain using simulations of changes in the activity of each region. These simulations of changes represent the degree of processing resource allocation to the neural activity via changes in the weights of each region's output. As a result of the simulations, we reveal how the neural activity characteristic of emotion regulation in mindfulness, which has been reported in previous studies, is realized in the brain. Mindfulness meditation increases the weight of the output from each region of the thalamus and sensory cortex, which processes sensory stimuli from the external world. This sensory information activates the insula and anterior cingulate cortex (ACC). The orbitofrontal cortex and dorsolateral prefrontal cortex inhibit amygdala activity (i.e., top-down emotion regulation). However, when mindfulness meditation dominates bottom-up processing via sensory stimuli from the external world, amygdala activity increases through the insula and ACC activation.

Framework to describe constructs of academic emotions using ontological descriptions of statistical models.

Many studies have been conducted during the last two decades examining learner reactions within e-learning environments. In an effort to assist learners in their scholastic activities, these studies have attempted to understand a learner's mental states by analyzing participants' facial images, eye movements, and other physiological indices and data. To add to this growing body of research, we have been developing the intelligent mentoring system (IMS), which performs automatic mentoring by using an intelligent tutoring system (ITS) to scaffold learning activities and an ontology to provide a specification of learner's models. To identify learner's mental states, the ontology operates on the basis of the theoretical and data-driven knowledge of emotions. In this study, we use statistical models to examine constructs of emotions evaluated in previous psychological studies and then produce a construct of academic boredom. In concrete terms, we develop ontological descriptions of academic boredom that are represented with statistical models. To evaluate the validity and utility of the descriptions, we conduct an experiment to obtain subjective responses regarding learners' academic emotions in their university course and describe them as instances on the basis of the ontological descriptions.

Experimental study of learning support through examples in mathematical problem posing.

When using mathematics to solve problems in everyday life, problem solvers must recognize and formulate problems by themselves because structured problems are not provided. Therefore, in general education, fostering learner problem posing is an important task. Because novice learners have difficulty in composing mathematical structures (solutions) in problem posing, learning support to improve the composition of solutions is required. Although learning by solving examples is adopted in general education, it may not be sufficiently effective in fostering learner problem posing because cognitive skills differ between problem solving and problem posing. This study discusses and experimentally investigates the effects of learning from examples on composing solutions when problem posing. We studied three learning activities: learning by solving an example, learning by reproducing an example, and learning by evaluating an example. In our experiment, undergraduates were asked to pose their own new, unique problems from a base problem initially presented after the students learned an example by solving, reproducing, or evaluating it. The example allowed the undergraduates to gain ideas for composing a novel solution. The results indicated that learning by reproducing the example was the most effective in fostering the composition of solutions.