Research article Special Issues

Cognitive conflict and restructuring: The neural basis of two core components of insight

  • Received: 15 December 2018 Accepted: 27 April 2019 Published: 21 May 2019
  • Sometimes, the solution to a difficult problem simply pops into mind. Such a moment of sudden comprehension is known as “insight”. This fundamental cognitive process is crucial for problem solving, creativity and innovation, yet its true nature remains elusive, despite one century of psychological research. Typically, insight is investigated by using spatial puzzles or verbal riddles. Broadening the traditional approach, we propose to tackle this question by presenting magic tricks to participants and asking them to find out the secret method used by the magician. Combining this approach with cueing in an fMRI experiment, we were able to break down the insight process into two underlying components: cognitive conflict and restructuring. During cognitive conflict, problem solvers identify incongruent information that does not match their current mental representation. In a second step this information is restructured, thereby allowing them to correctly determine how the magic trick was done. We manipulated the occurrence of cognitive conflict by presenting two types of cues that lead participants to either maintain their perceptual belief (congruent cue) or to change their perceptual belief (incongruent cue) for the mechanism behind the magic trick. We found that partially overlapping but distinct networks of brain activity were recruited for cognitive conflict and restructuring. Posterior, predominantly visual brain activity during cognitive conflict reflected processes related to prediction error, attention to the relevant cue-specific sensory domain, and the default brain state. Restructuring on the other hand, showed a highly distributed pattern of brain activity in regions of the default mode, executive control networks, and salience networks. The angular gyrus and middle temporal gyrus were active in both cognitive conflict and restructuring, suggesting that these regions are important throughout the insight problem solving process. We believe this type of approach towards understanding insight will give lead to a better understanding of this complex process and the specific role that different brain regions play in creative thought.

    Citation: Amory H. Danek, Virginia L. Flanagin. Cognitive conflict and restructuring: The neural basis of two core components of insight[J]. AIMS Neuroscience, 2019, 6(2): 60-84. doi: 10.3934/Neuroscience.2019.2.60

    Related Papers:

  • Sometimes, the solution to a difficult problem simply pops into mind. Such a moment of sudden comprehension is known as “insight”. This fundamental cognitive process is crucial for problem solving, creativity and innovation, yet its true nature remains elusive, despite one century of psychological research. Typically, insight is investigated by using spatial puzzles or verbal riddles. Broadening the traditional approach, we propose to tackle this question by presenting magic tricks to participants and asking them to find out the secret method used by the magician. Combining this approach with cueing in an fMRI experiment, we were able to break down the insight process into two underlying components: cognitive conflict and restructuring. During cognitive conflict, problem solvers identify incongruent information that does not match their current mental representation. In a second step this information is restructured, thereby allowing them to correctly determine how the magic trick was done. We manipulated the occurrence of cognitive conflict by presenting two types of cues that lead participants to either maintain their perceptual belief (congruent cue) or to change their perceptual belief (incongruent cue) for the mechanism behind the magic trick. We found that partially overlapping but distinct networks of brain activity were recruited for cognitive conflict and restructuring. Posterior, predominantly visual brain activity during cognitive conflict reflected processes related to prediction error, attention to the relevant cue-specific sensory domain, and the default brain state. Restructuring on the other hand, showed a highly distributed pattern of brain activity in regions of the default mode, executive control networks, and salience networks. The angular gyrus and middle temporal gyrus were active in both cognitive conflict and restructuring, suggesting that these regions are important throughout the insight problem solving process. We believe this type of approach towards understanding insight will give lead to a better understanding of this complex process and the specific role that different brain regions play in creative thought.


    加载中

    Acknowledgments



    We thank Franziska Konitzer for writing the code for this experiment and Anna Gatz and Benedict Wild for their help with data collection and coding, for creating the pictorial cues as well as for conducting the pilot studies. We are indebted to magician Thomas Fraps (http://www.thomasfraps.com) for providing the magic trick stimuli. We thank Prof. Benedikt Grothe for valuable and insightful discussions. This research project was funded by one grant to AD and another one to VLF from LMU Munich's Institutional Strategy LMUexcellent within the framework of the German Excellence Initiative. VLF is funded through the German Federal Ministry of Education and Research under the Grant Code 01EO1401.

    [1] Danek AH (2018) Magic tricks, sudden restructuring and the Aha! experience: A new model of non-monotonic problem solving, In: Vallée-Tourangeau F (ed), Insight: On the origins of new ideas, London: Routledge, 51–78.
    [2] Danek AH, Wiley J, Öllinger M (2016) Solving classical insight problems without Aha! experience: 9 Dot, 8 Coin, and Matchstick Arithmetic Problems. J Probl Solving 9: 47–57.
    [3] Bowden EM (1997) The effect of reportable and unreportable hints on anagram solution and the Aha! experience. Conscious Cogn 6: 545–573.
    [4] Cushen PJ, Wiley J (2012) Cues to solution, restructuring patterns, and reports of insight in creative problem solving. Conscious Cogn 21: 1166–1175. doi: 10.1016/j.concog.2012.03.013
    [5] Stroop JR (1935) Studies of interference in serial verbal reactions. J Exp Psychol 18: 643–662. doi: 10.1037/h0054651
    [6] Luo J, Knoblich G (2007) Studying insight problem solving with neuroscientific methods. Methods 42: 77–86. doi: 10.1016/j.ymeth.2006.12.005
    [7] Ohlsson S (1984) Restructuring revisited: II. An information processing theory of restructuring and insight. Scand J Psychol 25: 117–129.
    [8] Smith SM (1995) Getting into and out of mental ruts: A theory of fixation, incubation, and insight, In: Sternberg RJ, Davidson JE (eds), The nature of insight, Cambridge, MA: MIT Press, 229–251.
    [9] Wertheimer M (1925) Über Schlussprozesse im produktiven Denken. In: Wertheimer M (ed), Drei Abhandlungen zur Gestalttheorie, Erlangen: Verlag der Philosophischen Akademie, 164–184.
    [10] Knoblich G, Ohlsson S, Haider H, et al. (1999) Constraint relaxation and chunk decomposition in insight problem solving. J Exp Psychol Learn Mem Cogn 25: 1534–1555. doi: 10.1037/0278-7393.25.6.1534
    [11] Ohlsson S (1992) Information-processing explanations of insight and related phenomena, In: Keane MT, Gilhooly KJ (eds), Advances in the psychology of thinking, London: Harvester-Wheatsheaf, 1–44.
    [12] Ohlsson S (2011) Deep learning: How the mind overrides experience, New York: Cambridge University Press.
    [13] Sandkühler S, Bhattacharya J (2008) Deconstructing insight: EEG correlates of insightful problem solving. PLoS One 3: 1–12.
    [14] Smith RW, Kounios J (1996) Sudden insight: All-or-none processing revealed by speed-accuracy decomposition. J Exp Psychol Learn Mem Cogn 22: 1443–1462. doi: 10.1037/0278-7393.22.6.1443
    [15] Metcalfe J (1986) Feeling of knowing in memory and problem solving. J Exp Psychol Learn Mem Cogn 12: 288–294. doi: 10.1037/0278-7393.12.2.288
    [16] Metcalfe J, Wiebe D (1987) Intuition in insight and noninsight problem solving. Mem Cognit 15: 238–246. doi: 10.3758/BF03197722
    [17] Tik M, Sladky R, Luft CDB, et al. (2018) Ultra-high-field fMRI insights on insight: Neural correlates of the Aha!-moment. Hum Brain Mapp 39: 3241–3252. doi: 10.1002/hbm.24073
    [18] Jung-Beeman M, Bowden EM, Haberman J, et al. (2004) Neural activity when people solve verbal problems with insight. PLoS Biol 2: 500–510.
    [19] Starchenko MG, Bekhtereva NP, Pakhomov SV, et al. (2003) Study of the brain organization of creative thinking. Hum Physiol 29: 652–653. doi: 10.1023/A:1025836521833
    [20] Bechtereva NP, Korotkov AD, Pakhomov SV, et al. (2004) PET study of brain maintenance of verbal creative activity. Int J Psychophysiol 53: 11–20. doi: 10.1016/j.ijpsycho.2004.01.001
    [21] Luo J, Niki K, Phillips S (2004) Neural correlates of the Aha! reaction. NeuroReport 15: 2013–2017. doi: 10.1097/00001756-200409150-00004
    [22] Dietrich A, Kanso R (2010) A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychol Bull 136: 822–848. doi: 10.1037/a0019749
    [23] Bowden EM, Jung-Beeman M (2003) Aha! Insight experience correlates with solution activation in the right hemisphere. Psychon Bull Rev 10: 730–737. doi: 10.3758/BF03196539
    [24] Kounios J, Beeman M (2014) The cognitive neuroscience of insight. Annu Rev Psychol 65: 71–93. doi: 10.1146/annurev-psych-010213-115154
    [25] Sprugnoli G, Rossi S, Emmendorfer A, et al. (2017) Neural correlates of Eureka moment. Intelligence 62: 99–118. doi: 10.1016/j.intell.2017.03.004
    [26] Luo J, Niki K, Phillips S (2004) The function of the anterior cingulate cortex (ACC) in the insightful solving of puzzles: The ACC is activated less when the structure of the puzzle is known. J Psychol Chin Soc 5: 195–213.
    [27] Zhao Q, Zhou Z, Xu H, et al. (2013) Dynamic neural network of insight: a functional magnetic resonance imaging study on solving chinese 'chengyu' riddles. PloS One 8: e59351. doi: 10.1371/journal.pone.0059351
    [28] Aziz-Zadeh L, Kaplan JT, Iacoboni M (2009) "Aha!": The neural correlates of verbal insight solutions. Hum Brain Mapp 30: 908–916. doi: 10.1002/hbm.20554
    [29] Danek AH, Fraps T, von Müller A, et al. (2014) Working wonders? Investigating insight with magic tricks. Cognition 130: 174–185.
    [30] Danek AH, Öllinger M, Fraps T, et al. (2015) An fMRI investigation of expectation violation in magic tricks. Front Psychol 6: 48.
    [31] Danek AH, Fraps T, von Müller A, et al. (2013) Aha! experiences leave a mark: facilitated recall of insight solutions. Psychol Res 77: 659–669. doi: 10.1007/s00426-012-0454-8
    [32] Ekroll V, Sayim B, Wagemans J (2013) Against better knowledge: The magical force of amodal volume completion. Iperception 4: 511–515.
    [33] Ekroll V, Sayim B, Wagemans J (2017) The other side of magic: the psychology of perceiving hidden things. Perspect Psychol Sci 12: 91–106. doi: 10.1177/1745691616654676
    [34] Ekroll V, Sayim B, Van der Hallen R, et al. (2016) Illusory visual completion of an object's invisible backside can make your finger feel shorter. Curr Biol 26: 1029–1033. doi: 10.1016/j.cub.2016.02.001
    [35] Pétervári J, Danek AH (under review) Problem solving of magic tricks: Guiding to and through an impasse with solution cues.
    [36] Danek AH, Wiley J (2017) What about false insights? Deconstructing the Aha! experience along its multiple dimensions for correct and incorrect solutions separately. Front Psychol 7: 2077.
    [37] Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15: 155–163. doi: 10.1016/j.jcm.2016.02.012
    [38] Nichols TE (2012) Multiple testing corrections, nonparametric methods, and random field theory. NeuroImage 62: 811–815. doi: 10.1016/j.neuroimage.2012.04.014
    [39] Woo C-W, Krishnan A, Wager TD (2014) Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. NeuroImage 91: 412–419. doi: 10.1016/j.neuroimage.2013.12.058
    [40] Eickhoff SB, Stephan KE, Mohlberg H, et al. (2005) A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25: 1325–1335. doi: 10.1016/j.neuroimage.2004.12.034
    [41] Auzias G, Coulon O, Brovelli A (2016) MarsAtlas: A cortical parcellation atlas for functional mapping. Hum Brain Mapp 37: 1573–1592. doi: 10.1002/hbm.23121
    [42] Carlén M (2017) What constitutes the prefrontal cortex? Science 358: 478–482. doi: 10.1126/science.aan8868
    [43] Nee DE, Brown JW, Askren MK, et al. (2013) A meta-analysis of executive components of working memory. Cereb Cortex 23: 264–282. doi: 10.1093/cercor/bhs007
    [44] Lobel E, Kahane P, Leonards U, et al. (2001) Localization of human frontal eye fields: anatomical and functional findings of functional magnetic resonance imaging and intracerebral electrical stimulation. J Neurosurg 95: 804–815. doi: 10.3171/jns.2001.95.5.0804
    [45] Caspers S, Eickhoff SB, Geyer S, et al. (2008) The human inferior parietal lobule in stereotaxic space. Brain Struct Funct 212: 481–495. doi: 10.1007/s00429-008-0195-z
    [46] Thomas C, Didierjean A (2016) Magicians fix your mind: How unlikely solutions block obvious ones. Cognition 154: 169–173. doi: 10.1016/j.cognition.2016.06.002
    [47] Thomas C, Didierjean A, Kuhn G (2018) It is magic! How impossible solutions prevent the discovery of obvious ones? Q J Exp Psychol 71: 2481–2487. doi: 10.1177/1747021817743439
    [48] Wright RD, Lawrence MW (2008) Orienting of attention, Oxford: Oxford University Press.
    [49] Moore T, Fallah M (2004) Microstimulation of the frontal eye field and its effects on covert spatial attention. J Neurophysiol 91: 152–162. doi: 10.1152/jn.00741.2002
    [50] Wang H, Callaghan E, Gooding-Williams G, et al. (2016) Rhythm makes the world go round: An MEG-TMS study on the role of right TPJ theta oscillations in embodied perspective taking. Cortex 75: 68–81. doi: 10.1016/j.cortex.2015.11.011
    [51] Vernet M, Quentin R, Chanes L, et al. (2014) Frontal eye field, where art thou? Anatomy, function, and non-invasive manipulation of frontal regions involved in eye movements and associated cognitive operations. Front Integr Neurosci 8: 66.
    [52] Greicius MD, Krasnow B, Reiss AL, et al. (2003) Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci 100: 253–258. doi: 10.1073/pnas.0135058100
    [53] Kizilirmak JM, Schott BH, Thuerich H, et al. (2019) Learning of novel semantic relationships via sudden comprehension is associated with a hippocampus-independent network. Conscious Cogn 69: 113–132. doi: 10.1016/j.concog.2019.01.005
    [54] Gilmore AW, Nelson SM, McDermott KB (2015) A parietal memory network revealed by multiple MRI methods. Trends Cogn Sci 19: 534–543. doi: 10.1016/j.tics.2015.07.004
    [55] Zacks JM, Speer NK, Swallow KM, et al. (2007) Event perception: A mind-brain perspective. Psychol Bull 133: 273–293. doi: 10.1037/0033-2909.133.2.273
    [56] Albright TD (2012) On the perception of probable things: neural substrates of associative memory, imagery, and perception. Neuron 74: 227–245. doi: 10.1016/j.neuron.2012.04.001
    [57] Ochsner KN, Hughes B, Robertson ER, et al. (2009) Neural systems supporting the control of affective and cognitive conflicts. J Cogn Neurosci 21: 1841–1854. doi: 10.1162/jocn.2009.21129
    [58] Botvinick MM (2007) Conflict monitoring and decision making: Reconciling two perspectives on anterior cingulate function. Cogn Affect Behav Neurosci 7: 356–366. doi: 10.3758/CABN.7.4.356
    [59] Kizilirmak JM, Thuerich H, Folta-Schoofs K, et al. (2016) Neural correlates of learning from induced insight: a case for reward-based episodic encoding. Front Psychol 7: 1693.
    [60] Dandan T, Haixue Z, Wenfu L, et al. (2013) Brain activity in using heuristic prototype to solve insightful problems. Behav Brain Res 253: 139–144. doi: 10.1016/j.bbr.2013.07.017
    [61] Seghier ML (2013) The Angular Gyrus: Multiple Functions and Multiple Subdivisions. The Neuroscientist 19: 43–61. doi: 10.1177/1073858412440596
    [62] Binder JR, Desai RH, Graves WW, et al. (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19: 2767–2796.
    [63] Vandenberghe R, Price C, Wise R, et al. (1996) Functional anatomy of a common semantic system for words and pictures. Nature 383: 254–6. doi: 10.1038/383254a0
    [64] Ye Z, Zhou X (2009) Conflict control during sentence comprehension: fMRI evidence. Neuroimage 48: 280–90. doi: 10.1016/j.neuroimage.2009.06.032
    [65] Spratling MW (2016) Predictive coding as a model of cognition. Cogn Process 17: 279–305. doi: 10.1007/s10339-016-0765-6
    [66] Benn Y, Webb TL, Chang BPI, et al. (2014) The neural basis of monitoring goal progress. Front Hum Neurosci 8: 688.
    [67] Craig AD (2009) How do you feel-now? The anterior insula and human awareness. Nat Rev Neurosci 10: 59–70.
    [68] Tian F, Tu S, Qiu J, et al. (2011) Neural correlates of mental preparation for successful insight problem solving. Behav Brain Res 216: 626–630. doi: 10.1016/j.bbr.2010.09.005
    [69] Boccia M, Piccardi L, Palermo L, et al. (2015) Where do bright ideas occur in our brain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity. Front Psychol 6: 1195.
    [70] Sridharan D, Levitin DJ, Menon V (2008) A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci 105: 12569. doi: 10.1073/pnas.0800005105
    [71] Beaty RE, Benedek M, Barry Kaufman S, et al. (2015) Default and executive network coupling supports creative idea production. Sci Rep 5: 10964. doi: 10.1038/srep10964
    [72] Menon V (2015) Salience Network. Brain Mapp Encycl Ref 2: 597–611.
    [73] Parris BA, Kuhn G, Mizon GA, et al. (2009) Imaging the impossible: An fMRI study of impossible causal relationships in magic tricks. NeuroImage 45: 1033–1039. doi: 10.1016/j.neuroimage.2008.12.036
    [74] Grahn JA, Parkinson JA, Owen AM (2008) The cognitive functions of the caudate nucleus. Prog Neurobiol 86: 141–155. doi: 10.1016/j.pneurobio.2008.09.004
    [75] Schneider M, Leuchs L, Czisch M, et al. (2018) Disentangling reward anticipation with simultaneous pupillometry/fMRI. Neuroimage 178: 11–22. doi: 10.1016/j.neuroimage.2018.04.078
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(7316) PDF downloads(1703) Cited by(12)

Article outline

Figures and Tables

Figures(5)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog