Problem-solving Mechanisms Research Articles

Fronto-insular-parietal network engagement underlying arithmetic word problem solving.

Mathematical word problems are ubiquitous and standard for teaching and evaluating generalization of mathematical knowledge for real-world contexts. It is therefore concerning that the neural mechanisms of word problem solving are not well understood, as these insights represent strong potential for improving education and remediating deficits in this domain. Here, we investigate neural response to word problems via functional magnetic resonance imaging (fMRI). Healthy adults performed sentence judgment tasks on word problems that either contained one-step mathematical operations, or nonarithmetic judgments on parallel narratives without any numerical information. Behavioral results suggested that the composite efficiency measurement of combining accuracy and RT did not differ between the two problem types. Arithmetic sentence judgments elicited greater activation in the fronto-insular-parietal network including intraparietal sulcus (IPS), dorsolateral prefrontal cortex (PFC), and anterior insula (AI) than narrative sentence judgment. Narrative sentence judgments, conversely, resulted in greater activation predominantly in the left ventral PFC, angular gyrus and perisylvian cortex compared with reading arithmetic sentences. Moreover, task-dependent functional connectivity analyses showed the AI circuits were more strongly coupled with IPS during arithmetic sentence judgments than nonarithmetic sentences. Finally, activations in the IPS during arithmetic were highly correlated with out-of-scanner performance on a distinct set of problems with the same characteristics. These results show arithmetic word problem performance differences may rely more heavily on fronto-insular-parietal circuits for mathematical model building than narrative text comprehension of similar difficulty. More broadly, our study suggests that quantitative measurements of brain mechanisms can provide pivotal role for uncovering crucial arithmetic skills.

Polycentricity and the Hollow State: Exploring Shared Personnel as a Source of Connectivity in Fragmented Urban Systems

The Ecology of Games (EoG) theory couples institutional rational choice with social network theory, articulating how transaction costs, social capital, and collective action dilemmas shape networks and network outcomes in polycentric governance systems. EoG literature has often focused on social–relational ties across organizational boundaries. However, jurisdictional fragmentation and increased reliance on private contractors in local public service delivery foster another source of network connectivity—shared personnel who work for multiple service providers. Drawing upon novel data of organizational personnel from more than 500 special purpose entities responsible for delivering drinking water to local neighborhoods in the Houston metro area in the state of Texas (United States), we examine how managerial, technical, and financial service delivery personnel connect otherwise independent organizations. We find that districts regulated by a common groundwater management agency and districts which contract with one another are both more likely to share technical and managerial personnel. By studying special districts that have overlapping personnel, we broaden the scope of the EoG framework to include additional layers of governance network complexity. As individual bureaucrats and service professionals play a key role in information transfer and innovation diffusion across organizations, shared personnel networks merit consideration as a mechanism for coordination and collective problem solving in fragmented urban systems.

Willing but unable? Extending theory to investigate community capacity to participate in Ghana’s community-based health planning and service implementation

While primary health care programmes based on community participation are widely implemented in low- and middle- income settings, empirical evidence on whether and to what extent local people have the capacity to participate, support and drive such programmes scale up is scant in these countries. This paper assessed the level of community capacity to participate in one such programme – the Community-Based Health Planning and Service (CHPS) in Ghana. The capacity assessments were drawn from Chaskin’s (2001) theorised indicators of community capacity with modifications to include: sense of community; community members commitment; community leadership commitment; problem solving mechanisms; and access to resources. These capacity measures guided the design of an interview guide used to collect data from community informants, frontline health providers (FLP) and district health managers. Key qualitative themes were built into a questionnaire administered to households selected through systematic sampling approach. Findings showed that growing individualism, low trust in neighbours and apathetic behaviours undermined the capacity of mutual support for CHPS. The capacity to support CHPS was high for local leadership and community social mobilisation groups who often dedicated time to working with FLP to promote maternal and reproductive health service use, and in advocating broader support for CHPS. Within the wider community, commitment to voluntarism was low as members perceived CHPS to be owned by, and run on government funds and resources. Poor voluntarism was compounded by poverty that crippled the capacity to provide needed resource support for CHPS. Findings have great implications for building strong capable communities for participation in community oriented health programmes.

Both attentional control and the ability to make remote associations aid spontaneous analogical transfer.

Given the widespread belief that analogical processing is an important mechanism for creative problem solving, despite the rarity of spontaneous transfer in laboratory studies, a critical direction for future research is to address which abilities may allow for the spontaneous analogizing between distant (superficially dissimilar) sources and targets. This study explores the role of individual differences in attentional control and the ability to make remote associations and their possible combined effects on spontaneous analogical transfer. Participants attempted to solve Duncker's radiation problem after having been exposed to a distant source as part of an earlier task. Results indicated that both measures of attentional control and the ability to make remote associations uniquely predicted spontaneous transfer between a superficially dissimilar source and target. Further, a critical role was seen for the quality of the representation of the source analog on the likelihood of transfer. The present results affirm that the likelihood of spontaneous transfer depends critically on the quality of the representation for the source, but also suggest that individual differences in the ability to make remote associations may be more conducive to constructing a broader representation of that source than individual differences in attentional control.

Problem-Solving Education to Prevent Depression Among Low-Income Mothers

Although problem solving has been an important component of successful depression prevention and treatment interventions, evidence to support problem solving's mechanism of action is sparse. To understand the mechanism of an efficacious depression prevention intervention, problem-solving education (PSE). A multivariate path analysis was embedded within a randomized efficacy trial (February 15, 2011, to May 9, 2016). Participants were mothers with depressed mood, anhedonia, or depression history (but not in current major depressive episode) at 1 of 6 Head Start agencies. Participants were followed up for 12 months with serial assessments of potential intervention mediators and depressive symptoms. Problem-solving education (n = 111) and usual Head Start services (n = 119). Primary outcomes were depressive symptom elevations, which were measured bimonthly. Eight plausible intervention mediators were assessed: problem-solving ability; mastery; self-esteem; perceived stress; behavioral activation; and avoidant, problem-focused, and social coping. Among 230 participants, 152 (66.1%) were Hispanic; mean (SD) age was 31.4 (7.3) years. Based on associations with either PSE participation or depressive symptom outcomes, problem-solving ability, perceived stress, behavioral activation, and problem-focused coping were included in a parsimonious, multivariate path model. In this model, only perceived stress was associated with both PSE participation and depressive symptoms. Participants in the PSE group had adjusted standardized perceived stress change scores that were 11% lower than controls (95% CI, -0.19 to -0.03), and improvement in perceived stress generated an adjusted rate ratio (aRR) of 0.42 (95% CI, 0.33-0.53) for depressive symptom elevations. Participants in the intervention group also had standardized behavioral activation change scores 15% greater than controls (95% CI, 0.01-0.30) and problem-focused coping change scores 17% greater than controls (95% CI, 0.03-0.31); however, changes in these constructs were not associated with a differential rate of depressive symptom elevations. The direct effect of PSE on depressive symptom elevations (aRR, 0.72; 95% CI, 0.52-0.97) was greater than the mediated effect explained by improvement in perceived stress (aRR, 0.91; 95% CI, 0.85-0.98). Problem-solving education is efficacious in preventing depressive symptoms and appears to work by decreasing perceived stress; however, the mechanism for much of PSE's impact on depression remains unexplained. These results can be used to simplify the intervention model in preparation for effectiveness testing. ClinicalTrials.gov Identifier: NCT01298804.

Escapism as Reflected in Tennessee William’s The Glass Menagerie

Escapism is a part of psychology which in this research connected into literature as an interdisciplinary study which shows inclination of human personality to seeking for a way out or escape from unpleasant realities. The aim of the research was to describe the characters got acquainted with escapism and the way the characters’ cope with escapism in their daily life. The research employed a descriptive qualitative method with psychoanalytical approach of Sigmund Freud’s personality structure from anxiety theory, defense and problem solving mechanism. Data sources were primary and supporting data. The primary data were taken from Tennessee William’s The Glass Menagerie, and supporting data were taken from the books, journals, articles, and internet sources. The research indicated that each character experienced anxiety that made them got acquainted with escapism. They had their own way in dealing with escapism through defense and problem solving mechanism. Some characters solved emotional problems with negative mechanism consciously or unconsciously, and others handled emotion with positive mechanism, therefore the result were also positive.

Mapping the knowledge domain and the theme evolution of appropriability research between 1986 and 2016: a scientometric review

The scholars in the research domains of innovation and strategic management concerned about the appropriability for about 30 years or more. They focused on appropriability research and constantly evolving. In this paper, we analyze 30 years (1986–2016) literature on appropriability studies from Web of Science Core Collection database. A cited reference clustering map of different periods and terms co-occurrence map have been generated using bibliometric analysis and content analysis. Based on this, we study the evolutionary trajectory, mechanisms and theoretical architecture of appropriability research and explore further research directions. The results indicate that the essence of the appropriability research evolution is the perception changes in opening and sharing, value creation and value growth, and future research is focusing on role of appropriability in the platform governance, generative appropriability and the evolution of the problem-solving mechanisms.

Understanding the dynamics of leadership: A case study on Jeff Bezos and Jack Ma

This paper analyzes the leadership approach of two leaders, the founder of Amazon.com, Jeff Bezos, and the founder and executive chairman of Alibaba Group, Jack Ma. It incorporates their decision-making style, problem-solving mechanisms, and their concerns for the overall globe making them "global leaders." This paper critically compares the implications of their approaches to comprehensive employee motivation and company performance. This paper deviates away from "The Great Man Theory" and emphasizes on "Bandura Social Learning Theory" which emphasizes leadership as a process which can be practiced and learned. This paper summarizes their legacy in the business world and the paradigm shift that they brought in the way of doing business.

DGCC: data-driven granular cognitive computing

2016 is the 60 anniversary of the birth of artificial intelligence (AI). In the past 60 years, many AI theoretical models have been proposed and great achievements have been accomplished. Most intelligent computing models are inspired by various human/ natural/social intelligence mechanisms. Three main schools of artificial intelligence are formed, that is, symbolism, connectionism and behaviorism. Artificial intelligence is intelligence exhibited by machines. It is applied when a machine mimics cognitive functions that humans associate with other human minds. Achievements of cognitive science could give much inspiration to AI. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind’s capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. Cognitive computing is the third and most transformational phase in computing’s evolution, after the two distinct eras of computing—the tabulating era and the programming era. Inspired by human’s granularity thinking, problem solving mechanism and the cognition law of “global precedence”, a new powerful cognitive computing model, data-driven granular cognitive computing (DGCC), is proposed in this paper. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”, in a multiple granularity space. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper. The HD $$^3$$ characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data.

Search and Coherence-Building in Intuition and Insight Problem Solving.

Coherence-building is a key concept for a better understanding of the underlying mechanisms of intuition and insight problem solving. There are several accounts that address certain aspects of coherence-building. However, there is still no proper framework defining the general principles of coherence-building. We propose a four-stage model of coherence-building. The first stage starts with spreading activation restricted by constraints. This dynamic is a well-defined rule based process. The second stage is characterized by detecting a coherent state. We adopted a fluency account assuming that the ease of information processing indicates the realization of a coherent state. The third stage is designated to evaluate the result of the coherence-building process and assess whether the given problem is solved or not. If the coherent state does not fit the requirements of the task, the process re-enters at stage 1. These three stages characterize intuition. For insight problem solving a fourth stage is necessary, which restructures the given representation after repeated failure, so that a new search space results. The new search space enables new coherent states. We provide a review of the most important findings, outline our model, present a large number of examples, deduce potential new paradigms and measures that might help to decipher the underlying cognitive processes.

The Hydra revisited: expectations and perceptions of the impact of the EU-Korea free trade agreement

On 1 July 2011, a free trade agreement was provisionally implemented between the EU and South Korea. At the time, there were clear expectations within the EU business community in Seoul about the likely impact of the deal, including an increase in EU exports, the dismantling of tariff and existing non-tariff barriers (NTBs), the possible emergence of new NTBs, the further development of mechanisms for discussion and problem-solving mechanisms, and a shift in Koreans’ perceptions of all imported EU goods as luxury items. Interviews conducted with expatriate executives, diplomats and trade officials in Seoul in 2015 revealed the extent to which these expectations have been fulfilled and also highlighted the importance of broad economic and social trends in the strong performance of EU exports since 2011. These included the maturing of the Korean economy and the growing need for high-end industrial goods, and the Koreans’ increasing purchasing power and the concomitant desire to engage in displays of wealth. In addition to contributing to the general academic debate on the impact of free trade agreements (FTAs) and adding specific colour to the case of the EU-Korea FTA, this research will play an important role in shaping responses to the shifting economic, social and cultural trends that will impact the future outcomes of this key trade deal between the EU and Korea. It will also inform the EC’s discussions with current and future free trade agreement partners and its analysis of the various factors influencing the actual and perceived success or failure of those deals.

Mechanisms of automaticity and anticipatory control in fluid intelligence

ABSTRACTThe constructs of fluid (Gf) and crystalized (Gc) intelligence represent an early attempt to describe the mechanisms of problem solving in the vertebrate brain. Modern neuroscience demonstrates that problem solving involves interplay between the mechanisms of automaticity and anticipatory control, enabling nature’s elegant solution to the challenges animals face in their environment. Studies of neural functioning are making clear the primary role of cortical-subcortical interactions in the manifestation of intelligent behavior in humans and other vertebrates. A tridimensional model of intelligent problem solving is explored, wherein the basal ganglia system (BGS) and cerebrocerebellar system (CCS) interact within large scale brain networks. The BGS and CCS work together to enable automaticity to occur. The BGS enables the organism to learn what to do through a powerful instrumental learning system. The BGS also regulates when behavior is released through an inhibitory system which is incredibly sensitive to context. The CCS enables the organism to learn how to perform adaptive behaviors. Internal cerebellar models enable gradual improvements in the quality of behavioral output. The BGS and CCS interact within large scale brain networks, including the dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN) and frontoparietal network (FPN). The interactions of these systems enable vertebrate organisms to develop a vast array of complex adaptive behaviors. The benefits and importance of developing clinical tests to measure the integrity of these systems is considered.

Research on Steel Logistics Park under the Integration of the Beijing-Tianjin-Hebei Region

In order to adapt to the Chinese government coordinated development of national strategic requirements of the Beijing-Tianjin-Hebei region, the existing steel logistics park need to be upgraded. The steel logistics park presents three development stages in the same time. There is also a lack of overall planning, policy support, market mechanism and basic researches. For this reason, better government policy guidance and support is needed in three aspects: the unified and standard steel logistics park, the information platform/ system, problem-solving mechanism for such as logistics talent cultivation. Eventually, the unified industry service standards and product standards are built as a "steel inventory" and "Internet + logistics" which are based on information network.

The impact of problem solving mechanism in increasing motivation to learn the subject of faith and belief in a sample of students of the University of Bahrain: أثر آلية حلّ المشكلات في زيادة الدافعية لتعلّم مبحث الإيمان والعقيدة لدى عينة من طلبة جامعة البحرين

The aim of this study was to find out the effect of education by solving problems compared to the usual way of increasing the motivation of learning among a sample of students of the University of Bahrain studying the course of Islamic culture in the field of faith. The study sample consisted of (32) students divided into two groups (experimental and control). The first study was conducted according to the method of teaching problem solving while the second was studied according to the usual lecture method. The results of the study showed that there were statistically significant differences between the two groups in increasing learning motivation in favor of the pattern of education by solving problems compared to the usual method, and the study recommended the importance of increasing studies on the relationship of employing problem solving mechanism and learning motivation to other researchers in Islamic culture topics.

Cognitive Architecture with Evolutionary Dynamics Solves Insight Problem.

In this paper, we show that a neurally implemented a cognitive architecture with evolutionary dynamics can solve the four-tree problem. Our model, called Darwinian Neurodynamics, assumes that the unconscious mechanism of problem solving during insight tasks is a Darwinian process. It is based on the evolution of patterns that represent candidate solutions to a problem, and are stored and reproduced by a population of attractor networks. In our first experiment, we used human data as a benchmark and showed that the model behaves comparably to humans: it shows an improvement in performance if it is pretrained and primed appropriately, just like human participants in Kershaw et al. (2013)'s experiment. In the second experiment, we further investigated the effects of pretraining and priming in a two-by-two design and found a beginner's luck type of effect: solution rate was highest in the condition that was primed, but not pretrained with patterns relevant for the task. In the third experiment, we showed that deficits in computational capacity and learning abilities decreased the performance of the model, as expected. We conclude that Darwinian Neurodynamics is a promising model of human problem solving that deserves further investigation.

Organisational learning through knowledge management systems: a case study on improvement of customer support processes

Customer support processes and customer relationship management act as enablers of organisational learning by providing valuable inputs to quality improvement and innovative processes in the existence of appropriate knowledge management systems and problem-solving mechanisms. This single case study explores the impact of effective utilisation of knowledge management systems and six sigma-based problem-solving practices in customer support processes on organisational learning by using the findings from practices of a telecommunication company which is a subsidiary of a MNC in Turkey. Based on the interviews, observations and data provided from the company, customer support processes and organisational structure of customer support teams of the company are analysed, and performance metrics/indicators of the processes are identified. The impacts of three new systems, knowledge management system, a problem-solving system, that is based on six sigma methodology, and a 'skill-based resource allocation system' on performance of customer support processes, are explored by analysing the changes in the predefined performance metrics. Findings reveal that a knowledge management system that is integrated with six sigma methodology improved the effectiveness of customer support processes by providing shorter problem resolution time, reuse of the previous solutions and reduced engineer efforts. Appropriate organisational structuring, effective integration between the systems and acceptance of the systems by the users played major roles in the success of the new systems.

Modeling incubation and restructuring for creative problem solving in robots

Creativity represents the pinnacle of higher-level cognition, but exactly how it is achieved remains poorly understood, especially when simultaneously facing the opposing challenge of intractable complexity. The aims of the current study were (a) to examine how the brain may achieve the dual goals of creativity and complexity reduction, and (b) to begin developing higher-level cognition and creativity in robots. We address these aims by (a) modeling an example of insight problem solving and comparing it to empirical data, and (b) testing the model on a robot platform. Unlike other models, we propose a single mechanism for both creative problem solving and complexity reduction. Focusing on creativity, the computational mechanism leads to insightful problem solving by restructuring an internal belief representation based on evidence collected during an incubation period. Because insightful problem solving has been examined closely in nonhuman primates, providing detailed quantitative datasets lacking in humans, we tested the model by comparing simulations to insightful problem solving by rhesus monkeys. Results show that the proposed model accounts for both the discontinuous three stage problem-solving patterns and the spontaneous generalization to novel cases observed with the monkeys. To test the model in a physical environment, we implemented it in a vision-equipped robot, and the model solved the same insight problem from camera percepts. Our model shows how the creative brain may address the dual challenges of complex environments–finding unprecedented opportunity hidden within potentially intractable complexity–and suggests that both challenges may be met by a single underlying computational mechanism.

China’s security agenda transcends the South China Sea

ABSTRACTIn 2013, China’s president, Xi Jinping, launched a massive reclamation and construction campaign on seven reefs in the Spratly Islands in the South China Sea. Beijing insisted that its actions were responsible and in accord with international law, but foreign critics questioned Xi’s real intentions. Recently available internal documents involving China’s leader reveal his views about war, the importance of oceans in protecting and rejuvenating the nation, and the motives underlying his moves in the South China Sea. Central to those motives is China’s rivalry with the United States and the grand strategy needed to determine its outcome. To this end, Xi created five externally oriented and proactive military theater commands, one of which would protect newly built assets in the South China Sea and the sea lanes – sometimes referred to as the Maritime Silk Road – that pass through this sea to Eurasia and beyond. Simultaneously, China’s actions in the Spratlys complicated and worsened the US-China rivalry, and security communities in both countries recognized that these actions could erupt into armed crises – despite decades of engagement to prevent them. A permanent problem-solving mechanism may allow the two countries to move toward a positive shared future.

New Ideas on the Design of the Web-Based Learning System Oriented to Problem Solving From the Perspective of Question Chain and Learning Community

<p class="2">In recent years, a number of models concerning problem solving systems have been put forward. However, many of them stress on technology and neglect the research of problem solving itself, especially the learning mechanism related to problem solving. In this paper, we analyze the learning mechanism of problem solving, and propose that when designing Web-based problem solving systems, more attention should be paid to the learning mechanism involved in the problem solving process than to the technology itself. On the basis of that, some new ideas on the design of the problem solving systems are put forward in order to promote the rapid development of the Web-based problem solving systems.</p>

Research of Data-Aiming Mining Algorithm in Cloud Environment

Cloud computing contains a huge amount of data, which are featured as being widely distributed, heterogeneous, and dynamic. Thus, aiming at how to mine useful parts in these information, this paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k frequency set and uses the method of generating association rules to improve effectiveness of data mining. Simulation experiments show that mining algorithm in this paper is highly effective and suitable for data mining in the context of cloud computing. With the emergence of the concept cloud computing, more and more information is shared and spread through the Internet. How can carry out data mining in cloud computing became an important means of access to information (1). Literature (2) data mining solutions in the cloud computing environment is proposed, through the cloud computing and cloud computing services, describes the problem solving mechanism for data mining services. Literature (3) proposed a data processing method based on cloud computing, based on mining user browsing preference path. Experiments show that the mining algorithm for large amount of data logs, accuracy and efficiency than ordinary users preferred path mining algorithm based on frequency increased. Literature (4) proposed a new framework for data mining in the field of cloud platforms and cloud computing environments are described mechanism of data mining in the field of service-oriented. Literature (5) is a method of data mining based on cloud computing technology: large data sets and mining tasks on multiple computers in parallel, experiments show that use cloud computing technology to handle large data sets in a cluster, you can significantly improve the efficiency of data mining. Literature (6) for cloud computing resources prediction model based on data mining technology, results showed that this model not only improves the prediction accuracy of cloud computing resources, and reduce the complexity of modeling, improves the efficiency of models, to cloud computing resources provides a new way of building models. Literature (7) proposed data mining services view, cloud services task force has analyzed data mining advantages and challenges, data mining and prediction of cloud point data mining development trend of cloud services, providing data mining services framework for cloud services. This paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k