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An EEG study of the relationship between design problem statements and cognitive behaviors during conceptual design

Published online by Cambridge University Press:  30 May 2018

Longfan Liu
Affiliation:
School of Manufacturing Science & Engineering, Sichuan University, Chengdu 610065, China
Yan Li*
Affiliation:
School of Manufacturing Science & Engineering, Sichuan University, Chengdu 610065, China
Yan Xiong
Affiliation:
School of Manufacturing Science & Engineering, Sichuan University, Chengdu 610065, China
Juan Cao
Affiliation:
School of Manufacturing Science & Engineering, Sichuan University, Chengdu 610065, China
Ping Yuan
Affiliation:
School of Manufacturing Science & Engineering, Sichuan University, Chengdu 610065, China
*
Author for correspondence: Yan Li, E-mail: [email protected]

Abstract

In the design process, different problem statements result in different problem-solving strategies. A proper problem statement is the key to effective problem-solving. Based on the characteristics of the product design process, we divided design problem statements into open-ended (OE), decision-making (DM), and constrained (CO) statements and attempted to investigate the influences of different problem statements on designers’ cognitive behaviors from three perspectives, namely divergent thinking, convergent thinking, and mental workload. Then we provided quantification description to these influences based on electroencephalography (EEG) technology. We conducted experiments on 19 participants and used the BrainProduct™ actiChamp-32 to record the EEG data. Results are as follows: (1) The higher task-related α power was found in the temporal and occipital regions in the OE task compared with that in the DM and CO tasks. The OE statement also would help designers get novel ideas by strengthening their divergent thinking. (2) In the DM and CO tasks, there was no significant difference in the impact of the brain region on convergent thinking, but activities in the left hemisphere were stronger than that in the right hemisphere. The DM and CO tasks have better performance in convergent thinking than the OE task. (3) In the CO task, the designer's mental workload is the highest and mainly related to the activation of the centroparietal and occipital regions. These findings help designers understand the design problem-solving process from the perspective of cognitive science and monitor their thinking modes in the design process so as to improve their design performance.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2018 

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References

Alexiou, K, Zamenopoulos, T, Johnson, JH and Gilbert, SJ (2009) Exploring the neurological basis of design cognition using brain imaging: some preliminary results. Design Studies 30(6), 623647.CrossRefGoogle Scholar
Arden, R, Chavez, RS, Grazioplene, R and Jung, RE (2010) Neuroimaging creativity: a psychometric view. Behavioural Brain Research 214(2), 143.CrossRefGoogle ScholarPubMed
Arthur Cropley (2006) In praise of convergent thinking. Creativity Research Journal 18(3), 391404.Google Scholar
Başar, E, Başar-Eroğlu, C, Karakaş, S and Schürmann, M (1999) Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG? Neuroscience Letters 259(3), 165168.CrossRefGoogle ScholarPubMed
Beeman, MJ, Bowden, EM and Gernsbacher, MA (2000) Right and left hemisphere cooperation for drawing predictive and coherence inferences during normal story comprehension[J]. Brain & Language 71(2), 310336.Google Scholar
Borghini, G, Vecchiato, G, Toppi, J, Astolfi, L, Maglione, A, Isabella, R, Caltagirone, C, Kong, W, Wei, D, Zhou, Z, Polidori, L, Vitiello, S and Babiloni, F (2012) Assessment of Mental Fatigue During Car Driving by Using High Resolution EEG Activity and Neurophysiologic Indices. 34th International IEEE EMBC Conference (Vol. 2012, pp. 6442–5). IEEE.CrossRefGoogle Scholar
Bowden, EM and Jung-Beeman, M (2003) Aha! Insight experience correlates with solution activation in the right hemisphere. Psychonomic Bulletin & Review 10(3), 730737.Google Scholar
Brookhuis, KA and de Waard, D (2010) Monitoring drivers’ mental workload in driving simulators using physiological measures. Accident Analysis & Prevention 42(3), 898903.CrossRefGoogle ScholarPubMed
Brookings, JB, Wilson, GF and Swain, CR (1996) Psychophysiological responses to changes in workload during simulated air traffic control. Biological Psychology 42(3), 361377.Google Scholar
Chrysikou, EG and Thompson Schill, SL (2011) Dissociable brain states linked to common and creative object use. Human Brain Mapping 32(4), 665675.CrossRefGoogle ScholarPubMed
Cross, N (2008) Engineering Design Methods: Strategies for Product Design. West Sussex, England: Wiley.Google Scholar
Cross, N (2002) Creative cognition in design: processes of exceptional designers. Conference on Creativity & Cognition (pp. 14–19). ACM.CrossRefGoogle Scholar
De Dreu, CK, Baas, M and Nijstad, BA (2008) Hedonic tone and activation level in the mood-creativity link: toward a dual pathway to creativity model. Journal of Personality and Social Psychology 94(5), 739756.CrossRefGoogle Scholar
Dietrich, A and Kanso, R (2010) A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin 136(5), 822.CrossRefGoogle ScholarPubMed
Dong, S, Reder, LM, Yuan, Y, Liu, Y and Chen, F (2015) Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty. 1616, 146–156.Google Scholar
Fink, A and Benedek, M (2014) EEG alpha power and creative ideation. Neuroscience & Biobehavioral Reviews 44(100), 111.Google Scholar
Fink, A, Grabner, RH, Benedek, M and Neubauer, AC (2006) Divergent thinking training is related to frontal electroencephalogram alpha synchronization. European Journal of Neuroscience 23(8), 22412246.Google Scholar
Fink, A, Grabner, RH, Benedek, M, Reishofer, G, Hauswirth, V, Fally, M and Neubauer, AC (2009a) The creative brain: investigation of brain activity during creative problem solving by means of EEG and fMRI. Human Brain Mapping 30(3), 734748.Google Scholar
Fink, A, Grabner, RH, Gebauer, D, Reishofer, G, Koschutnig, K and Ebner, F (2010) Enhancing creativity by means of cognitive stimulation: evidence from an fMRI study. NeuroImage 52(4), 1687.Google Scholar
Fink, A, Graif, B and Neubauer, AC (2009b) Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers. NeuroImage 46(3), 854862.CrossRefGoogle ScholarPubMed
Fink, A, Schwab, D and Papousek, I (2011) Sensitivity of EEG upper alpha activity to cognitive and affective creativity interventions. International Journal of Psychophysiology 82(3), 233239.CrossRefGoogle ScholarPubMed
Geake, JG and Hansen, PC (2010) Functional neural correlates of fluid and crystallized analogizing. NeuroImage 49(4), 3489.CrossRefGoogle ScholarPubMed
Gero, JS (1990) Design prototypes: a knowledge representation schema for design. Ai Magazine 11(4), 2636.Google Scholar
Gevins, A and Smith, ME (2006) Electroencephalography (EEG) in Neuroergonomics. Oxford series in human-technology interaction, vol.15.CrossRefGoogle Scholar
Goel, V and Vartanian, O (2005) Dissociating the roles of right ventral lateral and dorsal lateral prefrontal cortex in generation and maintenance of hypotheses in set-shift problems. Cerebral Cortex 15(8), 11701177.CrossRefGoogle ScholarPubMed
Gola, M, Magnuski, M, Szumska, I and Wróbel, A (2013) EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly participants. International Journal of Psychophysiology 89(3), 334341.Google Scholar
Grabner, RH, Fink, A and Neubauer, AC (2007) Brain correlates of self-rated originality of ideas: evidence from event-related power and phase-locking changes in the EEG. Behavioral Neuroscience 121(1), 224230.CrossRefGoogle ScholarPubMed
Green, AE, Kraemer, DJ, Fugelsang, JA, Gray, JR and Dunbar, KN (2010) Connecting long distance: semantic distance in analogical reasoning modulates frontopolar cortex activity. Cerebral Cortex 20(1), 7076.CrossRefGoogle ScholarPubMed
Guilford, JP (1967) The Nature of Human Intelligence. New York: McGraw-Hill.Google Scholar
Jauk, E, Benedek, M and Neubauer, AC (2012) Tackling creativity at its roots: evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing. International Journal of Psychophysiology 84(2), 219225.CrossRefGoogle ScholarPubMed
Jin, Y and Chusilp, P (2006) Study of mental iteration in different design situations. Design Studies 27(1), 2555.Google Scholar
Johnson, MK, Blanco, JA, Gentili, RJ and Jaquess, KJ (2015) Probe-independent EEG assessment of mental workload in pilots. In International IEEE/EMBS Conference on Neural Engineering (pp. 581–584). IEEE.Google Scholar
Kijkuit, B and Ende, JVD (2007) The organizational life of an idea: integrating social network, creativity and decision-making perspectives. Journal of Management Studies 44(6), 863882.Google Scholar
Klimesch, W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences 16(12), 606617.CrossRefGoogle ScholarPubMed
Kotovsky, K, Hayes, JR and Simon, HA (1985) Why are some problems hard? Evidence from tower of Hanoi ☆. Cognitive Psychology 17(2), 248294.Google Scholar
Lal, SK and Craig, A (2002) Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 39(3), 313321.CrossRefGoogle ScholarPubMed
Lee, CS and Therriault, DJ (2013) The cognitive underpinnings of creative thought: a latent variable analysis exploring the roles of intelligence and working memory in three creative thinking processes. Intelligence 41(5), 306320.CrossRefGoogle Scholar
Liu, YC, Chakrabarti, A and Bligh, T (2003) Towards an “ideal” approach for concept generation. Design Studies 24(4), 341355.CrossRefGoogle Scholar
Martindale, C (1999) Biological bases of creativity. In Sternberg, R (ed.). Handbook of Creativity. Cambridge: University Press, pp. 137152.Google Scholar
Martindale, C and Hines, D (1975) Creativity and cortical activation during creative, intellectual and EEG feedback tasks. Biological Psychology 3(2), 91100.CrossRefGoogle ScholarPubMed
Metusalem, R, Kutas, M, Urbach, TP and Elman, JL (2016) Hemispheric asymmetry in event knowledge activation during incremental language comprehension: a visual half-field ERP study. Neuropsychologia 84, 252271.Google Scholar
Mölle, M, Marshall, L, Lutzenberger, W, Pietrowsky, R, Fehm, HL and Born, J (1996) Enhanced dynamic complexity in the human EEG during creative thinking. Neuroscience Letters 208(1), 6164.Google Scholar
Mölle, M, Marshall, L, Wolf, B, Fehm, HL and Born, J (2010) EEG complexity and performance measures of creative thinking. Psychophysiology 36(1), 95104.CrossRefGoogle Scholar
Nguyen, TA and Zeng, Y (2014) A physiological study of relationship between designer's mental effort and mental stress during conceptual design. Computer-Aided Design 54, 318.CrossRefGoogle Scholar
Megalakaki, O, Tijus, C, Baiche, R and Poitrenaud, S (2012) The effect of semantics on problem solving is to reduce relational complexity. Thinking & Reasoning 18(2), 159182.Google Scholar
Pfurtscheller, G and Fh, LDS (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology 110(11), 1842.CrossRefGoogle ScholarPubMed
Razoumnikova, OM (2000) Functional organization of different brain areas during convergent and divergent thinking: an EEG investigation. Cognitive Brain Research 10(1–2), 1118.CrossRefGoogle ScholarPubMed
Razumnikova, OM (2007a) Creativity related cortex activity in the remote associates task. Brain Research Bulletin 73(1–3), 96102.Google Scholar
Razumnikova, OM (2007b) The functional significance of α 2, frequency range for convergent and divergent verbal thinking. Human Physiology 33(2), 146156.CrossRefGoogle Scholar
Razumnikova, OM, Volf, NV and Tarasova, IV (2009) Strategy and results: sex differences in electrographic correlates of verbal and figural creativity. Human Physiology 35(3), 285294.Google Scholar
Redish, AD and Mizumori, SJY (2015) Memory and decision making. Neurobiology of Learning & Memory 117(4), 13.CrossRefGoogle ScholarPubMed
Ryd, N (2004) The design brief as carrier of client information during the construction process. Design Studies 25(3), 231249.Google Scholar
Ryu, K and Myung, R (2005) Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic. International Journal of Industrial Ergonomics 35(11), 9911009.CrossRefGoogle Scholar
Sauseng, P, Griesmayr, B, Freunberger, R and Klimesch, W (2010) Control mechanisms in working memory: a possible function of EEG theta oscillations. Neuroscience & Biobehavioral Reviews 34(7), 10151022.Google Scholar
Schwab, D, Benedek, M, Papousek, I, Weiss, EM and Fink, A (2014) The time-course of EEG alpha power changes in creative ideation. Frontiers in Human Neuroscience 8(8), 310.Google Scholar
Silk, EM, Daly, S, Jablokow, K, Yilmaz, S and Rosenberg, M (2014) The Design Problem Framework: Using Adaption-Innovation Theory to Construct Design Problem Statements. In ASEE Conference and Exposition.Google Scholar
Spradlin, D (2012) Are you solving the right problem? Asking the right questions is crucial. Harvard Business Review 90(9), 84101.Google Scholar
Tanaka, M, Shigihara, Y, Ishii, A, Funakura, M, Kanai, E and Watanabe, Y (2012) Effect of mental fatigue on the central nervous system: an electroencephalography study. Behavioral and Brain Functions 8(1), 48.Google Scholar
Trejo, LJ, Knuth, K, Prado, R, Rosipal, R, Kubitz, K, Kochavi, R and Zhang, Y (2007) EEG-based estimation of mental fatigue: convergent evidence for a three-state model. In International Conference on Foundations of Augmented Cognition (pp. 201–211). Springer Berlin Heidelberg.Google Scholar
Trejo, LJ, Kubitz, K, Rosipal, R, Kochavi, RL and Montgomery, LD (2015) EEG-based estimation and classification of mental fatigue. Psychology 6(6), 572589.Google Scholar
Uzzi, B, Mukherjee, S, Stringer, M and Jones, B (2013) Atypical combinations and scientific impact. Science 342(6157), 468472.CrossRefGoogle ScholarPubMed
Wang, T, Zhang, Q, Li, H, Qiu, J, Tu, S and Yu, C (2009) The time course of Chinese riddles solving: evidence from an ERP study. Behavioural Brain Research 199(2), 278282.CrossRefGoogle Scholar
Watson, CE and Chatterjee, A (2012) A bilateral frontoparietal network underlies visuospatial analogical reasoning. Neuroimage 59(3), 28312838.CrossRefGoogle ScholarPubMed
Wickens, CD (2008) Multiple resources and mental workload. Human Factors 50, 449455.Google Scholar
Yilmaz, S and Gonzalez, CMSR (2010) Cognitive heuristics in design: instructional strategies to increase creativity in idea generation. Artificial Intelligence for Engineering Design Analysis & Manufacturing 24(3), 335355.CrossRefGoogle Scholar
Zabelina, D, Saporta, A and Beeman, M (2016) Flexible or leaky attention in creative people? Distinct patterns of attention for different types of creative thinking. Memory & Cognition 44(3), 488498.Google Scholar
Zhang, J (1997) The nature of external representations in problem solving. Cognitive Science 21(2), 179217.Google Scholar