The journal of problem solving最新文献

{"title":"Human Problem Solving in 2012","authors":"J. Funke","doi":"10.7771/1932-6246.1156","DOIUrl":"https://doi.org/10.7771/1932-6246.1156","url":null,"abstract":"This paper presents a bibliography of 263 references related to human problem solving, arranged by subject matter. The references were taken from PsycInfo and Academic Premier database. Journal papers, book chapters, and dissertations are included. The topics include human development, education, neuroscience, and research in applied settings. It is argued that researchers are more and more engaged with problem solving research because of its centrality in human actions and because society needs advice from science in understanding and solving complex problems.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90990519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introspection in Problem Solving","authors":"F. Jäkel, Cornell Schreiber","doi":"10.7771/1932-6246.1131","DOIUrl":"https://doi.org/10.7771/1932-6246.1131","url":null,"abstract":"Problem solving research has encountered an impasse. Since the seminal work of Newell und Simon (1972) researchers do not seem to have made much theoretical progress (Batchelder and Alexander, 2012; Ohlsson, 2012). In this paper we argue that one factor that is holding back the field is the widespread rejection of introspection among cognitive scientists. We review evidence that introspection improves problem solving performance, sometimes dramatically. Several studies suggest that self-observation, self-monitoring, and self-reflection play a key role in developing problem solving strategies. We argue that studying these introspective processes will require researchers to systematically ask subjects to introspect. However, we document that cognitive science textbooks dismiss introspection and as a consequence introspective methods are not used in problem solving research, even when it would be appropriate. We conclude that research on problem solving would benefit from embracing introspection rather than dismissing it.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91012794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Evaluating Human Problem Solving of Computationally Hard Problems","authors":"Sarah Carruthers, U. Stege","doi":"10.7771/1932-6246.1152","DOIUrl":"https://doi.org/10.7771/1932-6246.1152","url":null,"abstract":"This article is concerned with how computer science, and more exactly computational complexity theory, can inform cognitive science. In particular, we suggest factors to be taken into account when investigating how people deal with computational hardness. This discussion will address the two upper levels of Marr’s Level Theory: the computational level and the algorithmic level. Our reasons for believing that humans indeed deal with hard cognitive functions are threefold: (1) Several computationally hard functions are suggested in the literature, e.g., in the areas of visual search, visual perception and analogical reasoning, linguistic processing, and decision making. (2) People appear to be attracted to computationally hard recreational puzzles and games. Examples of hard puzzles include Sudoku, Minesweeper, and the 15-Puzzle. (3) A number of research articles in the area of human problem solving suggest that humans are capable of solving hard computational problems, like the Euclidean Traveling Salesperson Problem, quickly and near-optimally. This article gives a brief introduction to some theories and foundations of complexity theory and motivates the use of computationally hard problems in human problem solving with a short survey of known results of human performance, a review of some computationally hard games and puzzles, and the connection between complexity theory and models of cognitive functions. We aim to illuminate the role that computer science, in particular complexity theory, can play in the study of human problem solving. Theoretical computer science can provide a wealth of interesting problems for human study, but it can also help to provide deep insight into these problems. In particular, we discuss the role that computer science can play when choosing computational problems for study and designing experiments to investigate human performance. Finally, we enumerate issues and pitfalls that can arise when choosing computationally hard problems as the subject of study, in turn motivating some interesting potential future lines of study. The pitfalls addressed include: choice of presentation and representation of problem instances, evaluation of problem comprehension, and the role of cognitive support in experiments. Our goal is not to exhaustively list all the ways in which these choices may impact experimental studies, but rather to provide a few simple examples in order to highlight possible pitfalls.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"274 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85917868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives on Problem Solving in Educational Assessment: Analytical, Interactive, and Collaborative Problem Solving","authors":"Samuel Greiff, Daniel V. Holt, J. Funke","doi":"10.7771/1932-6246.1153","DOIUrl":"https://doi.org/10.7771/1932-6246.1153","url":null,"abstract":"Problem solving has received broad public interest as an important competency in modern societies. In educational large-scale assessments paper-pencil based analytical problem solving was included first (e.g., Programme for International Student Assessment, PISA 2003). With growing interest in more complex situations, the focus has shifted to interactive problem solving (e.g., PISA 2012) requiring identification and control of complex systems. In the future, collaborative problem solving represents the next step in assessing problem solving ability (e.g., PISA 2015). This paper describes these different approaches to assessing problem solving ability in large-scale assessments considering theoretical questions as well as assessment issues. For each of the three types of problem solving, the definition and understanding of the construct is explained, items examples are shown together with some empirical results, and limitations of the respective approach are discussed. A final discussion centers on the connection of cognitive and differential psychology within educational research and assessment.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89901757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Cluster Location on Human Performance on the Traveling Salesperson Problem","authors":"J. MacGregor","doi":"10.7771/1932-6246.1151","DOIUrl":"https://doi.org/10.7771/1932-6246.1151","url":null,"abstract":"Most models of human performance on the traveling salesperson problem involve clustering of nodes, but few empirical studies have examined effects of clustering in the stimulus array. A recent exception varied degree of clustering and concluded that the more clustered a stimulus array, the easier a TSP is to solve (Dry, Preiss, & Wagemans, 2012). However, a limitation to this conclusion arises because degree of clustering may have been partially confounded with cluster location. An experiment was conducted to test the effects of cluster location while holding degree of clustering constant. Stimuli with a cluster near a boundary were solved more quickly and accurately than stimuli with the same cluster located more centrally. The results support and extend the previous findings of MacGregor, Ormerod, & Chronicle (1999). They also qualify the results of Dry et al. (2012). To the extent that degree of clustering may have been confounded with the location of clusters in their stimuli, it is unclear to what extent each factor may have affected performance.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"33 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72928847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning","authors":"H. Steingroever, Ruud Wetzels, E. Wagenmakers","doi":"10.7771/1932-6246.1150","DOIUrl":"https://doi.org/10.7771/1932-6246.1150","url":null,"abstract":"The Iowa gambling task (IGT) is one of the most popular tasks used to study decisionmaking deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we present a comparison of three models—the EV and PVL models, and a combination of these models (EV-PU)—based on the method of parameter space partitioning. This method allows us to assess the choice patterns predicted by the models across their entire parameter space. Our results show that the EV model is unable to account for a frequency-of-losses effect, whereas the PVL and EV-PU models are unable to account for a pronounced preference for the bad decks with many switches. All three models underrepresent pronounced choice patterns that are frequently seen in experiments. Overall, our results suggest that the search of an appropriate IGT model has not yet come to an end.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82451662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Performance on Hard Non-Euclidean Graph Problems: Vertex Cover","authors":"Sarah Carruthers, M. Masson, U. Stege","doi":"10.7771/1932-6246.1142","DOIUrl":"https://doi.org/10.7771/1932-6246.1142","url":null,"abstract":"Recent studies on a computationally hard visual optimization problem, the Traveling Salesperson Problem (TSP), indicate that humans are capable of finding close to opti mal solutions in near-linear time. The current study is a preliminary step in investigating human performance on another hard problem, the Minimum Vertex Cover Problem, in which solvers attempt to find a smallest set of vertices that ensures that every edge in an undirected graph is incident with at least one of the selected vertices. We identify appropriate measures of performance, examine features of problem instances that impact performance, and describe strategies typically employed by participants to solve instances of the Vertex Cover problem.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84006376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Problems with Problem Solving: Reflections on the Rise, Current Status, and Possible Future of a Cognitive Research Paradigm","authors":"S. Ohlsson","doi":"10.7771/1932-6246.1144","DOIUrl":"https://doi.org/10.7771/1932-6246.1144","url":null,"abstract":"This is a copy of an article published in the Journal of Problem Solving. © 2012 University of Illinois at Chicago.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78801342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relevancy in Problem Solving: A Computational Framework","authors":"J. Kwisthout","doi":"10.7771/1932-6246.1141","DOIUrl":"https://doi.org/10.7771/1932-6246.1141","url":null,"abstract":"When computer scientists discuss the computational complexity of, for example, finding the shortest path from building A to building B in some town or city, their starting point typically is a formal description of the problem at hand, e.g., a graph with weights on every edge where buildings correspond to vertices, routes between buildings to edges, and route-distances to edge-weights. Given such a formal description, either tractability or intractability of the problem is established, by proving that the problem either enjoys a polynomial time algorithm or is NP-hard. However, this problem description is in fact an abstraction of the actual problem of being in A and desiring to go to B: it focuses on the relevant aspects of the problem (e.g., distances between landmarks and crossings) and leaves out a lot of irrelevant details. This abstraction step is often overlooked, but may well contribute to the overall complexity of solving the problem at hand. For example, it appears that “going from A to B” is rather easy to abstract: it is fairly clear that the distance between A and the next crossing is relevant, and that the color of the roof of B is typically not. However, when the problem to be solved is “make X love me”, where the current state is (assumed to be) “X doesn’t love me”, it is hard to agree on all the relevant aspects of this problem. In this paper a computational framework is presented in order to formally investigate the notion of relevance in finding a suitable problem representation. It is shown that it is in itself intractable in general to find a minimal relevant subset of all problem dimensions that might or might not be relevant to the problem. Starting from a computational complexity stance, this paper aims to contribute a computational framework of ‘relevancy’ in problem solving, in order to be able to separate ‘easy to abstract’ from ‘hard to abstract’ problems. This framework is then used to discuss results in the literature on representation, (insight) problem solving and individual differences in the abstraction task, e.g., when experts in a particular domain are compared with novice problem solvers.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86152036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insight Problem Solving: A Critical Examination of the Possibility of Formal Theory","authors":"W. Batchelder, Gregory E. Alexander","doi":"10.7771/1932-6246.1143","DOIUrl":"https://doi.org/10.7771/1932-6246.1143","url":null,"abstract":"This paper provides a critical examination of the current state and future possibility of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are contrasted with move problems, which have been formally defined and studied extensively by cognitive psychologists since the pioneering work of Alan Newell and Herbert Simon. To facilitate our discussion, a number of classical brainteasers are presented along with their solutions and some conclusions derived from observing the behavior of many students trying to solve them. Some of these problems are interesting in their own right, and many of them have not been discussed before in the psychological literature. The main purpose of presenting the brainteasers is to assist in discussing the status of formal cognitive theory for insight problem solving, which is argued to be considerably weaker than that found in other areas of higher cognition such as human memory, decision-making, categorization, and perception. We discuss theoretical barriers that have plagued the development of successful formal theory for insight problem solving. A few suggestions are made that might serve to advance the field.","PeriodicalId":90070,"journal":{"name":"The journal of problem solving","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85330475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}