Decomposing intuitive components in a conceptual problem solving task

Research into intuitive problem solving has shown that objective closeness of participants' hypotheses were closer to the accurate solution than their subjective ratings of closeness. After separating conceptually intuitive problem solving from the solutions of rational incremental tasks and of sudden insight tasks, we replicated this finding by using more precise measures in a conceptual problem-solving task. In a second study, we distinguished performance level, processing style, implicit knowledge and subjective feeling of closeness to the solution within the problem-solving task and examined the relationships of these different components with measures of intelligence and personality. Verbal intelligence correlated with performance level in problem solving, but not with processing style and implicit knowledge. Faith in intuition, openness to experience, and conscientiousness correlated with processing style, but not with implicit knowledge. These findings suggest that one needs to decompose processing style and intuitive components in problem solving to make predictions on effects of intelligence and personality measures.

Beyond questionnaires – Exploring adult education teachers' mathematical beliefs with pictures and interviews

Because of the impact that mathematical beliefs have on an individual’s behaviour, they are generally well researched. However, little mathematical belief research has taken place in the field of adult education. This paper presents preliminary results from a study conducted in this field in Switzerland. It is based on Ernest’s (1989) description of mathematics as an instrumental, Platonist or problem solving construct. The analysis uses pictures drawn by the participants and interviews conducted with them as data. Using a categorising scheme developed by Rolka and Halverscheid (2011), the author argues that adults’ mathematical beliefs are complex and especially personal aspects are difficult to capture with said scheme. Particularly the analysis of visual data requires a more refined method of analysis.

Cognitive Interventions with Children with Special Needs - Transfer effects and multidisciplinary perspectives

Our last study with regularly developed children demonstrated a positive effect of working memory training on cognitive abilities. Building upon these findings, the aim of this multidisciplinary study is to investigate the effects of training of core functions with children who are suffering from different learning disabilities, like AD/HD, developmental dyslexia or specific language impairment. In addition to working memory training (BrainTwister), we apply a perceptual training, which concentrates on auditory-visual matching (Audilex), as well as an implicit concept learning task. We expect differential improvements of mental capacities, specifically of executive functions (working memory, attention, auditory and visual processing), scholastic abilities (language and mathematical skills), as well as of problem solving. With that, we hope to find further directions regarding helpful and individually adapted interventions in educational settings. Interested parties are invited to discuss and comment the design, the research question, and the possibilities in recruiting the subjects.

Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps

The new computing paradigm known as cognitive computing attempts to imitate the human capabilities of learning, problem solving, and considering things in context. To do so, an application (a cognitive system) must learn from its environment (e.g., by interacting with various interfaces). These interfaces can run the gamut from sensors to humans to databases. Accessing data through such interfaces allows the system to conduct cognitive tasks that can support humans in decision-making or problem-solving processes. Cognitive systems can be integrated into various domains (e.g., medicine or insurance). For example, a cognitive system in cities can collect data, can learn from various data sources and can then attempt to connect these sources to provide real time optimizations of subsystems within the city (e.g., the transportation system). In this study, we provide a methodology for integrating a cognitive system that allows data to be verbalized, making the causalities and hypotheses generated from the cognitive system more understandable to humans. We abstract a city subsystem—passenger flow for a taxi company—by applying fuzzy cognitive maps (FCMs). FCMs can be used as a mathematical tool for modeling complex systems built by directed graphs with concepts (e.g., policies, events, and/or domains) as nodes and causalities as edges. As a verbalization technique we introduce the restriction-centered theory of reasoning (RCT). RCT addresses the imprecision inherent in language by introducing restrictions. Using this underlying combinatorial design, our approach can handle large data sets from complex systems and make the output understandable to humans.