26 papers · ranked by Valyu relevance
Juliana Gottschling, Florian Krieger, Samuel Greiff
The development of a vaccine marks a breakthrough in the fight against infectious diseases. However, to eradicate highly infectious diseases globally, the immunization of large parts of the population is needed. Otherwise, diseases, such as polio, measles, or more recently COVID-19, will repeatedly flare-up, with…
Oka Kurniawan, Cyrille Jégourel, Norman Tiong Seng Lee, Matthieu De Mari + 1 more
'Matthieu De Mari' 'Christopher M. Poskitt'] Novice programmers often struggle with problem solving due to the high cognitive loads they face. Furthermore, many introductory programming courses do not explicitly teach it, assuming that problem solving skills are acquired along the way. In this paper, we present…
Qianli Yang, Zhihua Zhu, Ruoguang Si, Yunwei Li + 2 more
Human intelligence is characterized by our remarkable ability to solve complex problems. This involves planning a sequence of actions that leads us from an initial state to a desired goal state. Quantifying and comparing problem-solving capabilities across species and finding its evolutional roots is a fundamental…
Alex Doboli
—This paper proposes a novel representation to support computing metrics that help understanding and improving in real-time a team's behavior during problem solving in real-life. Even though teams are important in modern activities, there is little computing aid to improve their activity. The representation captures…
Çağla Girgin Büyükbayraktar, Süleyman Barbaros Yalçın, İsmail Yavuz Öztürk, Serkan Say + 1 more
'İsmail Yavuz Öztürk' 'Serkan Say' 'Xiaochun Xie'] This study explores the relationship between forgiveness and interpersonal problem-solving skills among university students using a correlational design. The sample includes 443 students aged 18-26 from Mersin and Selçuk Universities, selected through convenience…
Chandralekha Singh, Alexandru Maries, Kenneth Heller, Patricia Heller
'Patricia Heller'] Helping students become proficient problem solvers is a major goal of many physics courses from introductory to advanced levels. In fact, physics has often been used by cognitive scientists to investigate the differences between the problem-solving strategies of expert and novice problem solvers…
Jonas Schäfer, Timo Reuter, Miriam Leuchter, Julia Karbach + 1 more
'Adel Tekari'] Problem-solving is an important skill that is associated with reasoning abilities, action control and academic success. Nevertheless, empirical evidence on cognitive correlates of problem-solving performance in childhood is limited. Appropriate assessment tools are scarce and existing analog tasks…
Enrico Benedetti, Isaac Alpizar-Chacon, Johan Jeuring
Computational thinking (CT) is regarded as a fundamental skill set everyone should learn. Identifying when and how CT skills are used is challenging but important to inform interventions supporting their development. Previous research has examined how students and experts apply CT skills when solving introductory…
Reiji Ohkuma, Yuto Kurihara, Toru Takahashi, Rieko Osu
People solve insight problems that they encounter daily with a sudden sense of ‘aha!’ to reach a solution. Chunk decomposition, which decomposes the factors of the problem, and constraint relaxation, which manipulates filters to organize the information necessary to solve the problem, are important in insight…
Mattia Eluchans, Gian Luca Lancia, Antonella Maselli, Marco D’Alessando + 2 more
We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them are not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants…
Kenneth J. Kurtz, Leif Haley, Alexus Longo, Shanti Astra + 3 more
The nature and basis of creative thought has been the subject of wide-ranging inquiry. It is well established that people tend to struggle to solve problems that require an insight-and that this limitation is not readily alleviated. What can help produce more successful creative cognition? We propose a benefit from…
Gülce Kardeş, David Krakauer, Joshua Grochow
Cognitive science and theoretical computer science both seek to classify and explain the difficulty of tasks. Mechanisms of intelligence can be understood as leading to reductions in task difficulty. We map concepts from the computational complexity of a physical puzzle, the Soma Cube, onto cognitive problem-solving…
Aminu Darda’u Rafindadi, Nasir Shafiq, Idris Othman, Miljan Mikić + 2 more
'Bochen Jia' 'Paul B. Tchounwou'] Cognitive failures at the information acquiring (safety training), comprehension, or application stages led to near-miss or accidents on-site. The previous studies rarely considered the cognitive processes of two different kinds of construction safety training. Cognitive processes are…
Mario Graf, Amory H. Danek, Nemanja Vaci, Merim Bilalić
Insight problems are likely to trigger an initial, incorrect mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, leading to the typical “Aha!” experience, the evidence is inconclusive. Among…
Percy K Mistry, Hyesang Chang, Dawlat El-Said, Vinod Menon
Children exhibit remarkable variability in their mathematical problem-solving abilities, yet the cognitive, metacognitive and affective mechanisms underlying these individual differences remain poorly understood. We developed a novel Bayesian model of arithmetic problem-solving (BMAPS) to uncover the latent processes…
Alexander Gutfraind
Uncertainty is a pervasive challenge in decision and risk management and it is usually studied by quantification and modeling. Interestingly, engineers and other decision makers usually manage uncertainty with strategies such as incorporating robustness, or by employing decision heuristics. The focus of this paper is…
Stephanie M. Halmo, Kira A. Yamini, Julie Dangremond Stanton
Stronger metacognitive regulation skills are linked to increased academic achievement. Metacognition has primarily been studied using retrospective methods, but these methods limit access to students’ in-the-moment metacognition. We investigated first-year life science students’ in-the-moment metacognition while they…
Mary Vitello, Carola Salvi
The Gestalt psychologists’ theory of insight problem-solving was based on a direct parallelism between perceptual experience and higher-order forms of cognition (e.g., problem-solving). Similarly, albeit not exclusively, to the sudden recognition of bistable figures, these psychologists contended that problem-solving…
Juan Pablo Franco, Peter Bossaerts, Carsten Murawski
Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which…
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In philosophy and science, a first principle is a basic proposition or assumption that cannot be deduced from any other proposition or assumption. Ancient Greek philosophy Aristotle defined the first principle as “the first basis from which a thing is known.” First principles thinking (or reasoning from first…
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Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
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The Socratic method, grounded in iterative questioning and critical dialogue, offers a compelling framework for leveraging large language models (LLMs) to advance scientific reasoning and discovery in chemistry and materials science. In this paper, we explore how Socratic principles can be integrated into prompt…
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Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
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Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
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Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture…