Project description: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 require extensive coding. Thus, we developed and validated new tablet-based versions of existing analog tasks assessing technical problem-solving with gear construction tasks. To validate these tasks, 215 children (6-8 years) performed the problem-solving tasks in both modalities (analog, digital). To investigate whether performances in both modalities were correlated with other cognitive abilities, participants performed three additional tasks assessing language, reasoning and problem-solving. Structural equation modelling showed that performance was substantially correlated across modalities and also correlated with language, reasoning and another problem-solving task, showing the convergent validity of the digital tasks. We also found scalar measurement invariance across task modalities indicating that both task versions can be used interchangeably. We conclude that both versions (analog and digital) draw on similar cognitive resources and abilities. The analog tasks were thus successfully transferred to a digital platform. The new tasks offer the immense benefits of digital data collection, provide a valid measuring tool advancing problem-solving research in childhood and facilitate the application in the field, e.g., in the classroom.
Project description:Treating social problem-solving as a construct comprised of a number of components enables us to examine patterns formed by the components. However, variable-centered research has paid little attention to exploring these patterns to date. A person-centered approach may enable us to identify distinct profiles for groups. Our study aimed to investigate whether it is possible to establish homogeneous profiles for groups based on social problem-solving factors (positive and negative orientation, rationality, impulsivity, and avoidance). Furthermore, the study sought to explore whether there is any difference among these groups regarding self-efficacy, a fundamental component of social problem-solving. We used cluster analysis to examine social problem-solving and self-efficacy among 543 Hungarian secondary school students and 277 Hungarian university students. We identified three homogeneous groups that had shared characteristics in the two age samples (optimistic-hasty; optimistic-reflective; resigned-procrastinator). Four further groups were identified among adolescents (resigned-distancer; insecure-reflective; insecure-hasty; resigned-brooder); and an additional three among young adults (optimistic-modest; tense-hasty; tense-reflective). The relationships among the social problem-solving factors and self-efficacy differed among the profiles. Taking into account the profiles explored in this study may help identify groups that need improvement, and contribute to interventions being better suited to the needs of a particular group.
Project description:In this paper we argue that a synthesis of findings across the various sub-areas of research in complex problem solving and consequently progress in theory building is hampered by an insufficient differentiation of complexity and difficulty. In the proposed framework of person, task, and situation (PTS), complexity is conceptualized as a quality that is determined by the cognitive demands that the characteristics of the task and the situation impose. Difficulty represents the quantifiable level of a person's success in dealing with such demands. We use the well-documented "semantic effect" as an exemplar for testing some of the conceptual assumptions derived from the PTS framework. We demonstrate how a differentiation between complexity and difficulty can help take beyond a potentially too narrowly defined psychometric perspective and subsequently gain a better understanding of the cognitive mechanisms behind this effect. In an empirical study a total of 240 university students were randomly allocated to one of four conditions. The four conditions resulted from contrasting the semanticity level of the variable labels used in the CPS system (high vs. low) and two instruction conditions for how to explore the CPS system's causal structure (starting with the assumption that all relationships between variables existed vs. starting with the assumption that none of the relationships existed). The variation in the instruction aimed at inducing knowledge acquisition processes of either (1) systematic elimination of presumptions, or (2) systematic compilation of a mental representation of the causal structure underpinning the system. Results indicate that (a) it is more complex to adopt a "blank slate" perspective under high semanticity as it requires processes of inhibiting prior assumptions, and (b) it seems more difficult to employ a systematic heuristic when testing against presumptions. In combination, situational characteristics, such as the semanticity of variable labels, have the potential to trigger qualitatively different tasks. Failing to differentiate between 'task' and 'situation' as independent sources of complexity and treating complexity and difficulty synonymously threaten the validity of performance scores obtained in CPS research.
Project description:Reinforcement learning is generally accepted to be an appropriate and successful method to learn robot control. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a sequence of simpler high-level actions. A problem with the integration of both approaches is that action planning is based on discrete high-level action- and state spaces, whereas reinforcement learning is usually driven by a continuous reward function. Recent advances in model-free reinforcement learning, specifically, universal value function approximators and hindsight experience replay, have focused on goal-independent methods based on sparse rewards that are only given at the end of a rollout, and only if the goal has been fully achieved. In this article, we build on these novel methods to facilitate the integration of action planning with model-free reinforcement learning. Specifically, the paper demonstrates how the reward-sparsity can serve as a bridge between the high-level and low-level state- and action spaces. As a result, we demonstrate that the integrated method is able to solve robotic tasks that involve non-trivial causal dependencies under noisy conditions, exploiting both data and knowledge.
Project description:Combinatorial optimization to search for the best solution across a vast number of legal candidates requires the development of a domain-specific computing architecture that can exploit the computational power of physical processes, as conventional general-purpose computers are not powerful enough. Recently, Ising machines that execute quantum annealing or related mechanisms for rapid search have attracted attention. These machines, however, are hard to map application problems into their architecture, and often converge even at an illegal candidate. Here, we demonstrate an analogue electronic computing system for solving the travelling salesman problem, which mimics efficient foraging behaviour of an amoeboid organism by the spontaneous dynamics of an electric current in its core and enables a high problem-mapping flexibility and resilience using a resistance crossbar circuit. The system has high application potential, as it can determine a high-quality legal solution in a time that grows proportionally to the problem size without suffering from the weaknesses of Ising machines.
Project description:Science's broader impacts and the historic social, political, and geographic implications of these impacts are rarely discussed in graduate STEM curricula. A new required "Scientific Responsibility and Citizenship" course for first year chemistry graduate students was developed and taught at UC Berkeley. The course examined a series of case studies in which basic chemistry research led to societal impacts and discussed the diversity and equity of the research process and resulting consequences. The impact of the course was examined through pre- and post-surveys and interviews with participants. The course was found to have raised students' awareness and sense of responsibility for the impacts of their research and the importance of diversity, equity, and inclusion. Students also expressed an increased sense of identity and value alignment with the community as a result of the course. This study shows that even a relatively low-commitment intervention (6 hours in total), can have a large positive impact on students' awareness of the social context of science and their perceptions of department values.
Project description:IntroductionHigh-value cost-conscious care (HVCCC) education has been shown to reduce wasteful health care spending. Incorporating HVCCC into a medical school curriculum can be challenging due to limited curricular time. We explored the feasibility of medical students creating HVCCC peer education within existing platforms at a single urban academic medical school. We reasoned that curricular changes could improve student knowledge, attitudes, and competency with HVCCC within 2 hours and 25 minutes of curricular time.MethodsFirst-year medical student attitudes and understanding regarding HVCCC were evaluated via a survey before and after the delivery of a mixed asynchronous and in-person HVCCC curriculum created by two medical student peers. The curricula comprised three spaced asynchronous online sessions targeting HVCCC skill development followed by a gamified 90-minute clinical skills lab where students competed to determine the correct diagnosis at the lowest cost.ResultsOne hundred and twenty-three medical students (out of 145 first-year medical students) completed the presurvey and indicated willingness to participate in the educational innovation, and 54 completed both surveys. Forty-two percent of students agreed/strongly agreed that the curriculum was effective/strongly effective at promoting cost-effective care. Sixty-five percent of students agreed they would likely use these resources during their clinical rotations. Comfort accessing HVCCC resources improved from 4% precurriculum to 41% postcurriculum. There was no significant difference in HVCCC knowledge pre- and postsurvey.DiscussionThis educational innovation demonstrated the feasibility of a peer-developed HVCCC curriculum in preclinical education that minimally impacted curricular time and improved student comfort in accessing cost-effective resources.
Project description:According to the restructuring hypothesis, insight problem solving typically progresses through consecutive stages of search, impasse, insight, and search again for someone, who solves the task. The order of these stages was determined through self-reports of problem solvers and has never been verified behaviorally. We asked whether individual analysis of problem solving attempts of participants revealed the same order of problem solving stages as defined by the theory and whether their subjective feelings corresponded to the problem solving stages they were in. Our participants tried to solve the Five-Square problem in an online task, while we recorded the time and trajectory of their stick movements. After the task they were asked about their feelings related to insight and some of them also had the possibility of reporting impasse while working on the task. We found that the majority of participants did not follow the classic four-stage model of insight, but had more complex sequences of problem solving stages, with search and impasse recurring several times. This means that the classic four-stage model is not sufficient to describe variability on the individual level. We revised the classic model and we provide a new model that can generate all sequences found. Solvers reported insight more often than non-solvers and non-solvers reported impasse more often than solvers, as expected; but participants did not report impasse more often during behaviorally defined impasse stages than during other stages. This shows that impasse reports might be unreliable indicators of impasse. Our study highlights the importance of individual analysis of problem solving behavior to verify insight theory.
Project description:When confronted with novel problems, problem-solvers must decide whether to copy a modeled solution or to explore their own unique solutions. While past work has established that infants can learn to solve problems both through their own exploration and through imitation, little work has explored the factors that influence which of these approaches infants select to solve a given problem. Moreover, past work has treated imitation and exploration as qualitatively distinct, although these two possibilities may exist along a continuum. Here, we apply a program novel to developmental psychology (DeepLabCut) to archival data (Lucca et al., 2020) to investigate the influence of the effort and success of an adult's modeled solution, and infants' firsthand experience with failure, on infants' imitative versus exploratory problem-solving approaches. Our results reveal that tendencies toward exploration are relatively immune to the information from the adult model, but that exploration generally increased in response to firsthand experience with failure. In addition, we found that increases in maximum force and decreases in trying time were associated with greater exploration, and that exploration subsequently predicted problem-solving success on a new iteration of the task. Thus, our results demonstrate that infants increase exploration in response to failure and that exploration may operate in a larger motivational framework with force, trying time, and expectations of task success.