Abstract
Insight problem solving has been conceptualized as a dynamic search through a constrained search space, where a non-obvious solution needs to be found. Multiple sources of task difficulty have been defined that can keep the problem solver from finding the right solution such as an overly large search space or knowledge constraints requiring a change of the problem representation. Up to now, there are very few accounts that focus on different aspects of difficulty within an insight problem-solving context and how they affect task performance as well as the probability of finding a solution that is accompanied by an Aha! experience. In addition, we are not aware of any approaches investigating how knowledge constraints parametrically modulate task performance and the Aha! experience in compound remote associates (CRA) when controlling for other sources of task difficulty. Therefore, we first developed, tested, and externally validated a modified CRA paradigm in combination with lexical priming that is more likely to elicit representational change than the classical CRA tasks. Second, we parametrically estimated the effect of the knowledge constraint together with other sources of difficulty (size of the problem and search space, word length and frequency) using general linear mixed models. The knowledge constraint (and the size of the search space) was operationalized as lexical distance (measured as cosine distances) between different word pairs within this task. Our results indicate that the experimentally induced knowledge constraint still affects task performance and is negatively related to the Aha! experience when controlling for various other types of task difficulties. Finally, we will present the complete stimulus set in German language together with their statistical (i.e., item difficulty and mean solution time) and lexical properties.
Similar content being viewed by others
References
Ash, I. K., & Wiley, J. (2006). The nature of restructuring in insight: An individual-differences approach. Psychonomic Bulletin & Review, 13(1), 66–73.
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59(4), 390–412.
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of memory and language, 68(3), 255–278.
Bates, D,, Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.0–6.
Ben-Zur, H. (1989). Automatic and directed search processes in solving simple semantic-memory problems. Memory & Cognition, 17(5), 617–626.
Biemann, C., Heyer, G., Quasthoff, U., & Richter, M. (2007). The Leipzig Corpora Collection—Monolingual corpora of standard size. In Proceedings of corpus linguistics 2007, Birmingham
Bowden, E. M. (1997). The effect of reportable and unreportable hints on anagram solution and the aha! experience. Consciousness and cognition, 6(4), 545–573.
Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate problems. Behavior Research Methods, Instruments, & Computers, 35(4), 634–639.
Bowden, E. M., & Jung-Beeman, M. (2007). Methods for investigating the neural components of insight. Methods, 42(1), 87–99.
Bowden, E. M., Jung-Beeman, M., Fleck, J., & Kounios, J. (2005). New approaches to demystifying insight. Trends in cognitive sciences, 9(7), 322–328.
Bowers, K. S., Regehr, G., Balthazard, C., & Parker, K. (1990). Intuition in the context of discovery. Cognitive psychology, 22(1), 72–110.
Chein, J. M., & Weisberg, R. W. (2014). Working memory and insight in verbal problems: Analysis of compound remote associates. Memory & cognition, 42(1), 67–83.
Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive science, 5(2), 121–152.
Chronicle, E. P., MacGregor, J. N., & Ormerod, T. C. (2004). What makes an insight problem? The roles of heuristics, goal conception, and solution recoding in knowledge-lean problems. Journal of Experimental Psychology: Learning, memory, and cognition, 30(1), 14.
Chronicle, E. P., Ormerod, T. C., & MacGregor, J. N. (2001). When insight just won’t come: The failure of visual cues in the nine-dot problem. Quarterly Journal of Experimental Psychology, 54A(3), 903–919.
Cunnings, I. (2012). An overview of mixed-effects statistical models for second language researchers. Second Language Research, 28(3), 369–382.
Danek, A. H., Wiley, J., & Öllinger, M. (2016). Solving classical insight problems without aha! experience: 9 dot, 8 coin, and matchstick arithmetic problems. The Journal of Problem Solving, 9(1), 47–57.
De Dreu, C. K. W., Nijstad, B. A., Baas, M., Wolsink, I., & Roskes, M. (2012). Working memory benefits creative insight, musical improvisation, and original ideation through maintained task-focused attention. Personality and Social Psychology Bulletin, 38, 656–669.
Dow, G. T., & Mayer, R. E. (2004). Teaching students to solve insight problems: Evidence for domain specificity in creativity training. Creativity Research Journal, 16(4), 389–398.
Draine, S. (1998). Inquisit. Version 4.0.10.0. Seattle, WA: Millisecond Software. https://www.millisecond.com/. Accessed 23 June 2018.
Fleck, J. I., & Weisberg, R. W. (2004). The use of verbal protocols as data: An analysis of insight in the candle problem. Memory & Cognition, 32(6), 990–1006.
Gardner, W., Mulvey, E. P., & Shaw, E. C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin, 118(3), 392–404.
Gilhooly, K. J., & Fioratou, E. (2009). Executive functions in insight versus non-insight problem solving: An individual differences approach. Thinking & Reasoning, 15(4), 355–376.
Gilhooly, K. J., Fioratou, E., & Henretty, N. (2010). Verbalization and problem solving: Insight and spatial factors. British Journal of Psychology, 101(1), 81–93.
Gilhooly, K. J., & Murphy, P. (2005). Differentiating insight from non-insight problems. Thinking & Reasoning, 11(3), 279–302.
Goel, V. (2014). Creative brains: designing in the real world. Frontiers in Human Neuroscience, 8(241), 1–14.
Hoey, M. (2005). Lexical priming: A new theory of words and language. London: Psychology Press.
Jones, M. N., Kintsch, W., & Mewhort, D. J. (2006). High-dimensional semantic space accounts of priming. Journal of memory and language, 55(4), 534–552.
Jones, M. N., Willits, J., Dennis, S., & Jones, M. (2015). Models of semantic memory. In Busemeyer, J. R., Wang, Z., Townsend, J. T., & Eidels, A. (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 232–254). Oxford: Oxford University Press.
Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., Kounios, J. (2004). Neural activity when people solve verbal problems with insight. PLoS Biol, 2(4), e97.
Kershaw, T. C., & Ohlsson, S. (2004). Multiple causes of difficulty in insight: the case of the nine-dot problem. Journal of experimental psychology: learning, memory, and cognition, 30(1), 3.
Kliegl, R., Masson, M. E., & Richter, E. M. (2010). A linear mixed model analysis of masked repetition priming. Visual Cognition, 18(5), 655–681.
Knoblich, G., Ohlsson, S., Haider, H., & Rhenius, D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(6), 1534.
Knoblich, G., Ohlsson, S., & Raney, G. E. (2001). An eye movement study of insight problem solving. Memory & cognition, 29(7), 1000–1009.
Köhn, A. (2015). What’s in an embedding? Analyzing word embeddings through multilingual evaluation, pp. 2067–2073. http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP000.pdf. Accessed 23 June 2018.
Kounios, J., & Beeman, M. (2014). The cognitive science of insight. Annual Review of Psychology, 65, 71–93.
Levy, O., & Goldberg, Y. (2014a). Dependency-based word embeddings. In Proceedings of the 52nd annual meeting of the association for computational linguistics (pp. 302–308). Baltimore: ACL.
Levy, O., & Goldberg, Y. (2014b). Linguistic regularities in sparse and explicit word representations. In Proceedings of the eighteenth conference on computational natural language learning (pp. 171–180). Baltimore: ACL.
Luo, J., & Knoblich, G. (2007). Studying insight problem solving with neuroscientific methods. Methods, 42(1), 77–86.
Luo, J., & Niki, K. (2003). Function of hippocampus in “insight” of problem solving. Hippocampus, 13(3), 316–323.
MacGregor, J. N., Ormerod, T. C., & Chronicle, E. P. (2001). Information processing and insight: a process model of performance on the nine-dot and related problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(1), 176.
McNamara, T. P. (2005). Semantic priming: Perspectives from memory and word recognition. New York: Psychology Press.
Metcalfe, J., & Wiebe, D. (1987). Intuition in insight and noninsight problem solving. Memory & cognition, 15(3), 238–246.
Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90, 227–234.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space, pp. 1–12. arXiv:1301.3781.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111–3119). Lake Tahoe: NIPS.
Nęcka, E., Żak, P., & Gruszka, A. (2016). Insightful imagery is related to working memory updating. Frontiers in Psychology, 7, 1664–1078.
Ohlsson, S. (1992). Information-processing explanations of insight and related phenomena. Advances in the psychology of thinking, 1, 1–44.
Öllinger, M., Jones, G., Faber, A. H., & Knoblich, G. (2013). Cognitive mechanisms of insight: the role of heuristics and representational change in solving the eight-coin problem. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(3), 931.
Öllinger, M., Jones, G., & Knoblich, G. (2014). The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem. Psychological research, 78(2), 266–275.
Olteţeanu, A. M., & Falomir, Z. (2015). comRAT-C: a computational compound Remote Associates Test solver based on language data and its comparison to human performance. Pattern Recognition Letters, 67, 81–90.
Ormerod, T. C., MacGregor, J. N., & Chronicle, E. P. (2002). Dynamics and constraints in insight problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(4), 791.
Paulewicz, B., Chuderski, A., & Nęcka, E. (2007). Insight problem solving, fluid intelligence, and executive control: A structural equation modeling approach. In Proceedings of the 2nd European cognitive science conference (pp. 586–591). Hove: Erlbaum.
Raven, J. C., Court, J. H., & Raven, J. (1983). Manual for Raven’s Progressive Matrices and Vocabulary Scales (Sect. 4, Advanced Progressive Matrices). London: H. K. Lewis.
Reed, S. K. (2016). The structure of ill-structured (and well-structured) problems revisited. Educational Psychology Review, 28(4), 691–716.
Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4, 181–201.
Sloane, P., & MacHale, D. (1994). Great lateral thinking puzzles. New York: Sterling Publishing Company, Inc.
Sternberg, R. J., & Davidson, J. E. (1995). The nature of insight. Cambridge: MIT Press.
R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Austria. http://www.R-project.org. Accessed 24 June 2018.
Webb, M. E., Little, D. R., & Cropper, S. J. (2016). Insight is not in the problem: Investigating insight in problem solving across task types. Frontiers in psychology, 7, 1424.
Webb, M. E., Little, D. R., & Cropper, S. J. (2017). Once more with feeling: Normative data for the aha experience in insight and noninsight problems. Behavior Research Methods. https://doi.org/10.3758/s13428-017-0972-9.
Weisberg, R. W., & Alba, J. W. (1981). An examination of the alleged role of “fixation” in the solution of several “insight” problems. Journal of Experimental Psychology: General, 110(2), 169.
Wu, L., Knoblich, G., & Luo, J. (2013). The role of chunk tightness and chunk familiarity in problem solving: evidence from ERPs and fMRI. Human Brain Mapping, 34(5), 1173–1186.
Wu, L., Knoblich, G., Wei, G., & Luo, J. (2009). How perceptual processes help to generate new meaning: An EEG study of chunk decomposition in Chinese characters. Brain research, 1296, 104–112.
Acknowledgements
We want to thank Dr. Tobias Sommer-Blöchl and his research group as well as two anonymous reviewers for their very thoughtful comments.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval
The ethics committee of the German society for psychology approved of this study and the procedures performed in it were in accordance with the 1964 Helsinki declaration and its later amendments.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Conflict of interest
MB was funded by the German Science Foundation (SFB 936/C7). The authors declare no conflict of interest.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Becker, M., Wiedemann, G. & Kühn, S. Quantifying insightful problem solving: a modified compound remote associates paradigm using lexical priming to parametrically modulate different sources of task difficulty. Psychological Research 84, 528–545 (2020). https://doi.org/10.1007/s00426-018-1042-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00426-018-1042-3