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Dispositions Toward Flow and Mindfulness Predict Dispositional Insight

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Abstract

This study aimed to investigate whether dispositions to positive affect (PA), mindfulness, and flow states predict a disposition toward insight. Using a sample of 1069 participants, two structural equation models (SEMs) were performed; the first included positive affect, mindfulness, and flow as the predictors. The second SEM repeated this, but with the nine components of flow included separately. In the first model, mindfulness and flow significantly predicted insight; PA showed no effect. In the second model, PA and mindfulness showed an effect. The subcomponents of flow—merging of action and awareness, unambiguous feedback, and transformation of time—had the strongest effect on insight, followed by autotelic experience. Clear goals negatively affected insight.

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References

  • Asakawa, K. (2004). Flow experience and autotelic personality in Japanese college students: how do they experience challenges in daily life? Journal of Happiness Studies, 5(2), 123–154.

    Article  Google Scholar 

  • Baas, M., De Dreu, C. K., & Nijstad, B. A. (2008). A meta-analysis of 25 years of mood-creativity research: hedonic tone, activation, or regulatory focus? Psychological Bulletin, 134(6), 779–806.

    Article  PubMed  Google Scholar 

  • Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., & Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13(1), 27–45.

    Article  PubMed  Google Scholar 

  • Baumann, N. (2012). Autotelic personality. In S. Engeser (Ed.), Advances in flow research (pp. 165–186). New York: Springer.

    Chapter  Google Scholar 

  • Baumann, N., & Kuhl, J. (2002). Intuition, affect, and personality: unconscious coherence judgments and self-regulation of negative affect. Journal of Personality and Social Psychology, 83(5), 1213–1223.

    Article  PubMed  Google Scholar 

  • Baumann, N., & Kuhl, J. (2005). Positive affect and flexibility: overcoming the precedence of global over local processing of visual information. Motivation and Emotion, 29(2), 123–134. https://doi.org/10.1007/s11031-005-7957-1.

    Article  Google Scholar 

  • Baumann, N., & Scheffer, D. (2010). Seeing and mastering difficulty: the role of affective change in achievement flow. Cognition and Emotion, 24(8), 1304–1328. https://doi.org/10.1080/02699930903319911.

    Article  Google Scholar 

  • Baumann, N., Kaschel, R., & Kuhl, J. (2005). Striving for unwanted goals: stress-dependent discrepancies between explicit and implicit achievement motives reduce subjective well-being and increase psychosomatic symptoms. Journal of Personality and Social Psychology, 89(5), 781.

    Article  PubMed  Google Scholar 

  • Beeman, M. J., Friedman, R. B., Grafman, J., Perez, E., Diamond, S., & Lindsay, M. B. (1994). Summation priming and coarse semantic coding in the right hemisphere. Journal of Cognitive Neuroscience, 6(1), 26–45.

    Article  PubMed  Google Scholar 

  • Begley, S. (2007). Train your mind, change your brain: how a new science reveals our extraordinary potential to transform ourselves. New York: Random House Digital, Inc..

    Google Scholar 

  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.

    Article  PubMed  Google Scholar 

  • Bentler, P. M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117.

    Article  Google Scholar 

  • Bergomi, C., Tschacher, W., & Kupper, Z. (2013). The assessment of mindfulness with self-report measures: existing scales and open issues. Mindfulness, 4(3), 191–202.

    Article  Google Scholar 

  • Bishop, S. R., Lau, M., Shapiro, S., Carlson, L., Anderson, N. D., Carmody, J.,... Velting, D. (2004). Mindfulness: a proposed operational definition. Clinical Psychology: Science and Practice, 11(3), 230–241.

  • Bodner, T. E. (2000). On the assessment of individual differences in mindful information processing. (PhD Thesis), Harvard University, Cambridge, United States. Retrieved from https://elibrary.ru/item.asp?id=5317264

  • 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.

    Article  PubMed  Google Scholar 

  • Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate problems. Behavior Research Methods, 35(4), 634–639.

    Article  Google Scholar 

  • Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84(4), 822–848.

    Article  PubMed  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park: Sage.

    Google Scholar 

  • Carver, C. S., & Scheier, M. F. (1990). Origins and functions of positive and negative affect: a control-process view. Psychological Review, 97(1), 19–35.

    Article  Google Scholar 

  • Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods and Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciencies (2nd ed.). New York: Routledge.

    Google Scholar 

  • Cortina, J. M., & Landis, R. S. (2009). When small effect sizes tell a big story, and when large effect sizes don’t. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: doctrine, verity and fable in the organizational and social sciences (pp. 287–308). New York: Routledge.

    Google Scholar 

  • Csikszentmihalyi, M. (1991). Flow: the psychology of optimal experience. New York: Harper Perennial.

    Google Scholar 

  • Csikszentmihalyi, M. (2000). Beyond boredom and anxiety: experiencing flow in work and play. San Francisco: Jossey-Bass.

    Google Scholar 

  • Csikszentmihalyi, M. (2014). Toward a psychology of optimal experience. In M. Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology (pp. 209–226). Dordrecht: Springer.

    Google Scholar 

  • Davidson, J. E., & Sternberg, R. J. (2003). The psychology of problem solving. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Duncker, K. (1945). On problem-solving. Psychological Monographs, 58(5), 1–113.

    Article  Google Scholar 

  • Eubanks, D. L., Murphy, S. T., & Mumford, M. D. (2010). Intuition as an influence on creative problem-solving: the effects of intuition, positive affect, and training. Creativity Research Journal, 22(2), 170–184. https://doi.org/10.1080/10400419.2010.481513.

    Article  Google Scholar 

  • Fleck, J. I., & Weisberg, R. W. (2013). Insight versus analysis: evidence for diverse methods in problem solving. Journal of Cognitive Psychology, 25(4), 436–463. https://doi.org/10.1080/20445911.2013.779248.

    Article  Google Scholar 

  • Gasper, K., & Clore, G. L. (2002). Attending to the big picture: mood and global versus local processing of visual information. Psychological Science, 13(1), 34–40.

    Article  PubMed  Google Scholar 

  • Gilhooly, K., & Fioratou, E. (2009). Executive functions in insight versus non-insight problem solving: an individual differences approach. Thinking and Reasoning, 15(4), 355–376.

    Article  Google Scholar 

  • Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: implications for theory testing. Marketing Science, 23(4), 519–529.

    Article  Google Scholar 

  • Gulliksen, H., & Tukey, J. W. (1958). Reliability for the law of comparative judgment. Psychometrika, 23(2), 95–110.

    Article  Google Scholar 

  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.

    Google Scholar 

  • Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Article  Google Scholar 

  • Isen, A. M., Johnson, M. M., Mertz, E., & Robinson, G. F. (1985). The influence of positive affect on the unusualness of word associations. Journal of Personality and Social Psychology, 48(6), 1413–1426.

    Article  PubMed  Google Scholar 

  • Isen, A. M., Daubman, K. A., & Nowicki, G. P. (1987). Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 52, 1122–1131.

    Article  PubMed  Google Scholar 

  • Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: the flow state scale-2 and dispositional flow scale-2. Journal of Sport and Exercise Psychology, 24(2), 133–150.

    Article  Google Scholar 

  • Jackson, S. A., Martin, A. J., & Eklund, R. C. (2008). Long and short measures of flow: the construct validity of the FSS-2, DFS-2, and new brief counterparts. Journal of Sport and Exercise Psychology, 30(5), 561–587.

    Article  PubMed  Google Scholar 

  • 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 Biology, 2(4), 500–510.

  • Kershaw, T. C., Flynn, C. K., & Gordon, L. T. (2013). Multiple paths to transfer and constraint relaxation in insight problem solving. Thinking & Reasoning, 19(1), 96–136. https://doi.org/10.1080/13546783.2012.742852.

    Article  Google Scholar 

  • Kounios, J., & Beeman, M. (2014). The cognitive neuroscience of insight. Annual Review of Psychology, 65, 71–93.

    Article  PubMed  Google Scholar 

  • Kounios, J., & Beeman, M. J. (2015). The Eureka factor: creative insights and the brain. London: Random House.

    Google Scholar 

  • Kounios, J., Frymiare, J. L., Bowden, E. M., Fleck, J. I., Subramaniam, K., Parrish, T. B., & Jung-Beeman, M. (2006). The prepared mind: neural activity prior to problem presentation predicts subsequent solution by sudden insight. Psychological Science, 17(10), 882–890.

    Article  PubMed  Google Scholar 

  • Kuhl, J. (2000). The volitional basis of personality systems interaction theory: applications in learning and treatment contexts. International Journal of Educational Research, 33(7), 665–703.

    Article  Google Scholar 

  • Kuhl, J., & Kazén, M. (1999). Volitional facilitation of difficult intentions: joint activation of intention memory and positive affect removes Stroop interference. Journal of Experimental Psychology: General, 128(3), 382.

    Article  Google Scholar 

  • Langer, E. J. (1997). The power of mindful learning. Boston: Da Capo Press.

    Google Scholar 

  • Langer, E. J. (2000). Mindful learning. Current Directions in Psychological Science, 9(6), 220–223.

    Article  Google Scholar 

  • Langer, E. J., & Moldoveanu, M. (2000). The construct of mindfulness. Journal of Social Issues, 56(1), 1–9.

    Article  Google Scholar 

  • Lau, M. A., Bishop, S. R., Segal, Z. V., Buis, T., Anderson, N. D., Carlson, L.,... Devins, G. (2006). The Toronto mindfulness scale: development and validation. Journal of Clinical Psychology, 62(12), 1445–1468.

  • Løvstad, M., Funderud, I., Meling, T., Krämer, U. M., Voytek, B., Due-Tønnessen, P.,... Solbakk, A. K. (2012). Anterior cingulate cortex and cognitive control: neuropsychological and electrophysiological findings in two patients with lesions to dorsomedial prefrontal cortex. Brain and Cognition, 80(2), 237–249. https://doi.org/10.1016/j.bandc.2012.07.008.

  • Lutz, A., Greishcar, L. L., Rawlings, N. B., Ricard, M., & Davidson, R. J. (2004). Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. PANAS, 101, 16369–16373.

    Article  Google Scholar 

  • Maier, N. R. F. (1940). The behavior mechanisms concerned with problem solving. Psychological Review, 47(1), 43.

    Article  Google Scholar 

  • Martin, A., & Jackson, S. (2008). Brief approaches to assessing task absorption and enhanced subjective experience: examining ‘short’ and ‘core’ flow in diverse performance domains. Motivation & Emotion, 32(3), 141–157. https://doi.org/10.1007/s11031-008-9094-0.

    Article  Google Scholar 

  • Martinsen, Ø. (1993). Insight problems revisited: the influence of cognitive styles and experience on creative problem solving. Creativity Research Journal, 6(4), 435–447.

    Article  Google Scholar 

  • Maruyama, G. M. (1997). Basics of structural equation modeling. London: Sage Publications.

    Google Scholar 

  • McGaw, B., & Glass, G. V. (1980). Choice of the metric for effect size in meta-analysis. American Educational Research Journal, 17(3), 325–337.

    Article  Google Scholar 

  • Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69(3), 220–232.

    Article  PubMed  Google Scholar 

  • Metcalfe, J., & Wiebe, D. (1987). Intuition in insight and noninsight problem solving. Memory and Cognition, 15(3), 238–246.

    Article  PubMed  Google Scholar 

  • Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). New York: Oxford University Press.

    Google Scholar 

  • Oaksford, M., Morris, F., Grainger, B., & Williams, J. M. G. (1996). Mood, reasoning, and central executive processes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(2), 476–492.

    Google Scholar 

  • Ostafin, B. D., & Kassman, K. T. (2012). Stepping out of history: mindfulness improves insight problem solving. Consciousness and Cognition, 21, 1031–1036.

    Article  PubMed  Google Scholar 

  • Ovington, L. A., Saliba, A. J., Moran, C. C., Goldring, J., & MacDonald, J. B. (2015). Do people really have insights in the shower? The when, where and who of the aha! moment. The Journal of Creative Behavior, In Press. https://doi.org/10.1002/jocb.126.

  • Ovington, L. A., Saliba, A. J., & Goldring, J. (2016). Dispositional insight scale: development and validation of a tool that measures propensity toward insight in problem solving. Creativity Research Journal, 28(3), 342–347. https://doi.org/10.1080/10400419.2016.1195641.

    Article  Google Scholar 

  • Phillips, L. H., Bull, R., Adams, E., & Fraser, L. (2002). Positive mood and executive function: evidence from stroop and fluency tasks. Emotion, 2(1), 12–22.

    Article  PubMed  Google Scholar 

  • Ritchhart, R., & Perkins, D. N. (2000). Life in the mindful classroom: nurturing the disposition of mindfulness. Journal of Social Issues, 56(1), 27–47.

    Article  Google Scholar 

  • Rowe, G., Hirsh, J. B., & Anderson, A. K. (2007). Positive affect increases the breadth of attentional selection. PANAS, 104(1), 383–388.

    Article  Google Scholar 

  • Sakaki, M., & Niki, K. (2011). Effects of the brief viewing of emotional stimuli on understanding of insight solutions. Cognitive, Affective, & Behavioral Neuroscience, 11(4), 1–15.

  • Smith, S. M. (1995a). Fixation, incubation, and insight in memory and creative thinking. In S. M. Smith, T. B. Ward, & R. A. Finke (Eds.), The creative cognition approach (pp. 135–156). London: The MIT Press.

    Google Scholar 

  • Smith, S. M. (1995b). Getting into and out of mental ruts. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 229–251). London: The MIT Press.

    Google Scholar 

  • Steiger, J. H. (1998). A note on multiple sample extensions of the RMSEA fit index. Structural Equation Modeling: A Multidisciplinary Journal, 5(4), 411–419. https://doi.org/10.1080/10705519809540115.

    Article  Google Scholar 

  • Subramaniam, K., Kounios, J., Parrish, T. B., & Jung-Beeman, M. (2008). A brain mechanism for facilitation of insight by positive affect. Journal of Cognitive Neuroscience, 21(3), 415–432.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Michigan: Allyn and Bacon.

    Google Scholar 

  • Ulrich, M., Keller, J., Hoenig, K., Waller, C., & Grön, G. (2014). Neural correlates of experimentally induced flow experiences. NeuroImage, 86, 194–202.

    Article  PubMed  Google Scholar 

  • Walach, H., Buchheld, N., Buttenmüller, V., Kleinknecht, N., & Schmidt, S. (2006). Measuring mindfulness: the Freiburg mindfulness inventory (FMI). Personality and Individual Differences, 40(8), 1543–1555.

    Article  Google Scholar 

  • Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063.

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was funded under an Australian Government Research Training Program Scholarship. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors. We thank the Charles Sturt University Writing Circle for providing insightful comments on the content and expression of ideas.

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LO: designed and executed the study, and conducted the analyses of the data, and wrote the paper. AS: collaborated on the design of the study, writing, and editing of the manuscript. JG: collaborated on the design of the study, writing, and editing of the manuscript.

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Correspondence to Linda A. Ovington.

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The study was approved by the Charles Sturt University, Faculty of Arts Ethics Committee. All procedures performed in the study were in accordance with the ethical standards of the institution and with the 1964 Helsinki declaration and its later amendments. Participants gave informed consent through accessing the study online via a link in an email inviting potential respondents to participate.

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The authors declare that they have no competing interests.

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Ovington, L.A., Saliba, A.J. & Goldring, J. Dispositions Toward Flow and Mindfulness Predict Dispositional Insight. Mindfulness 9, 585–596 (2018). https://doi.org/10.1007/s12671-017-0800-4

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