Abstract
This study compares the experts and the novices to investigate their information processing in dealing with the different degrees of recognition of shape-match stimulus by measuring the event-related potentials (ERPs). ERPs were recorded while 20 designers and 20 novices made shape-match judgments for table and chair sets. All of the tables were in the normal style, and the chairs had different components and structures, including NormalChair, AllbyChair, and NonebyChair. The results show that the experts had fewer agreements than the novices did in matching the Normal table to the NormalChair used in the experiment. Furthermore, the experts’ ERPs elicited by AllbyChair and NonebyChair conditions had higher N170 amplitudes than those of the novices. However, the novices, in response to these AllbyChair and NonebyChair conditions for N170 exhibited greater delay latency than the experts. Additionally, the design expertise effects in object recognition provided additional evidence and confirmed similar results from many previous N170 studies. The experts comprehended the content in their familiar field, and inferred the complex design structure to the abstract level to show the hierarchical association of the knowledge structure. However, novices could not clearly understand the different and varied structures, and could only speculate from the limited information presented in existing problems. This study infers that because they used visual thinking in their training, the designers showed stronger expert performances, and were more capable than ordinary people in recognizing novel objects from bizarre entity arrangements as familiar ones.
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Abra, J. C. (1993). Competition: creativity's vilified motive. Genetic, Social, and General Psychology Monographs, 119(3), 289–342.
Atkinson, R. L., Atkinson, R. C., Smith, E. E., Bem, D. J., & Nolen-Hoeksema, S. (1996). Hilgord’s introduction to psychology. Ca: Thomson Learning Inc.
Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996). Electrophysiological studies of face perception in humans. Journal of Cognitive Neuroscience, 8, 551–565.
Bentin, S., & Deouell, L. (2000). Structural encoding and identification in face processing: ERP evidence for separate mechanisms. Cognitive Neuropsychology, 17, 35–54.
Bless, H., Fiedler, K., & Strack, F. (2004). Social cognition: How individuals construct reality. New York: Psychology Press.
Boehm, S. G., Dering, B., & Thierry, G. (2011). Category-sensitivity in the N170 range: A question of topography and inversion, not one of amplitude. Neuropsychologia, 49, 2082–2089.
Bötzel, K., & Grusser, O. J. (1989). Electric brain potentials evoked by pictures of faces and non-faces: a search for ‘face-specific’ EEG-potentials. Experimental Brain Research, 77, 349–360.
Bötzel, K., Schulze, S., & Stodieck, R. G. (1995). Scalp topography and analysis of intracranial sources of faceevoked potentials. Experimental Brain Research, 104, 135–143.
Boutsen, L., Humphreys, G. W., Praamstra, P., & Warbrick, T. (2006). Comparing neural correlates of configural processing in faces and objects: An ERP study of the Thatcher illusion. Neuroimage, 32, 352–367.
Caldara, R., Thut, G., Servoir, P., Michel, C. M., Bovet, P., & Renault, B. (2003). Face versus non-face object perception and the ‘other-race’ effect: A spatio-temporal event-related potential study. Clinical Neurophysiology, 114, 515–528.
Chen, C. M., & Huang, S. H. (2014). Web-based reading annotation system with an attention-based self-regulated learning mechanism for promoting reading performance. British Journal of Educational Technology, 45(5), 959–980.
Chen, C. M., & Lin, Y. J. (2016). Effects of different text display types on reading comprehension, sustained attention and cognitive load in mobile reading contexts. Interactive Learning Environments, 24(3), 553–571.
Chen, C. M., & Wang, J. Y. (2017). Effects of online synchronous instruction with an attention monitoring and alarm mechanism on sustained attention and learning performance. Interactive Learning Environments. https://doi.org/10.1080/10494820.2017.1341938.
Chen, C. M., Wang, J. Y., & Yu, C. M. (2017). Assessing the attention levels of students by using a novel attention aware system based on brainwave signals. British Journal of Educational Technology, 48(2), 348–369.
Chen, C. M., & Wu, C. H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108–121.
Chi, M. T. H., Glaser, R., & Farr, M. J. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates Inc.
Coles, M., & Rugg, M. D. (1996). Event-related brain potentials: An introduction. In M. D. Rugg & M. Coles (Eds.), Electrophysiology of Mind. Oxford: Oxford University Press.
Crismond, D. (2013). Design Practices and Misconceptions: Helping Beginners in Engineering Design. Science Teacher, 80(1), 50–54.
Crowley, K., Sliney, A., Pitt, I., & Murphy, D. (2010). Evaluating a brain-computer interface to categorise human emotional response. In 2010 IEEE 10th international conference on advanced learning technologies (ICALT), IEEE, Sousse, Tunisia, 276–278.
Curran, T., Tanaka, J. W., & Weiskopf, D. (2002). An electrophysiological comparison of visual categorization and recognition memory. Cognitive, Affective & Behavioral Neuroscience, 2, 1–18.
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011). Model-based influences on humans' choices and striatal prediction errors. Neuron, 69, 1204–1215.
DeBono, E. (1967). New Think: The use of lateral thinking in the generation of new ideas. New York: Basic Books.
Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136(5), 822–848.
Eimer, M. (1998). Does the face-specific N170 component reflect the activity of a specialized eye processor? NeuroReport, 9, 2945–2948.
Eimer, M. (2000a). Attentional modulations of event-related brain potentials sensitive to faces. Cognitive Neuropsychology, 17(1/2/3), 103–116.
Eimer, M. (2000b). Effects of face inversion on the structural encoding and recognition of faces: Evidence from event-related brain potentials. Cognitive Brain Research, 10, 145–158.
Eimer, M., & McCarthey, R. A. (1999). Prosopagnsosia and structural encoding of faces: evidence from event-related potentials. NeuroReport, 10, 255–259.
Frensch, P., & Sternberg, R.J. (1989). Expertise and intelligent thinking: When is it worse to know better? In R.J.Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 5, pp. 157–188). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.
Ganis, G., Smith, D., & Schendan, H. E. (2012). The N170, not the P1, indexes the earliest time for categorical perception of faces, regardless of interstimulus variance. NeuroImage, 62, 1563–1574.
Gauthier, I., Curran, T., Curby, K. M., & Collins, D. (2003). Perceptual interference supports a non-modular account of face processing. Nature Neuroscience, 6, 428–432.
Gauthier, I., Skudlarski, P., Gore, J. C., & Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3, 191–197.
Ghali, R., Ouellet, S., & Frasson, C. (2016). LewiSpace: An exploratory study with a machine learning model in an educational game. Journal of Education and Training Studies, 4(1), 192–201.
Ghergulescu, I., & Muntean, C. H. (2014). A novel sensor-based methodology for learner's motivation analysis in game-based learning. Interacting with Computers, 26(4), 305–320.
Ghergulescu, I., & Muntean, C. H. (2016). ToTCompute: A novel EEG-based TimeOnTask Threshold computation mechanism for engagement modelling and monitoring. International Journal of Artificial Intelligence in Education, 26(3), 821–854.
Herrmann, M. J., Ehlis, A. C., Ellgring, H., & Fallgatter, A. J. (2005). Early stages (P100) of face perception in humans as measured with event-related potentials (ERPs). Journal of Neural Transmission, 112, 1073–1081.
Huang, Y. M., Liu, M. C., Lai, C. H., & Liu, C. J. (2017). Using humorous images to lighten the learning experience through questioning in class. British Journal of Educational Technology, 48(3), 878–896.
Inventado, P. S., Legaspi, R., Suarez, M., & Numao, M. (2011). Predicting student emotions resulting from appraisal of ITS feedback. Research and Practice in Technology Enhanced Learning, 6(2), 107–133.
Itier, R. J., & Taylor, M. J. (2004). N170 or N1? Spatiotemporal differences between object and face processing using ERPs. Cerebral Cortex, 14, 132–142.
Je-Hun, Y., Seung-Min, P., Kwang-Eun, K., & Kwee-Bo, S. (2015). Classification of color imagination in electroencephalogram using Emotiv EPOC. In ISIS 2015 The 16th international symposium on advanced intelligent systems, 400–408.
Jeffreys, D. A., Tukmachi, E. S. A., & Rockley, G. (1992). Evoked-potential evidence for human brain mechanisms that respond to single, fixated faces. Experimental Brain Research, 91, 351–362.
Jimenez, C.O.S. Mesa, H.G.A. Rebolledo-Mendez, G., & de Freitas, S. (2011). Classification of cognitive states of attention and relaxation using supervised learning algorithms. In IEEE international games innovation conference (IGIC), IEEE, Orange, CA, 31–34.
Johnson, K. E., & Mervis, C. B. (1997). Effects of Varying Levels of Expertise on the Basic Level of Categorization. Journal of Experimental Psychology: General, 126(3), 248–277.
Joyce, C., & Rossion, B. (2005). The face-sensitive N170 and VPP components manifest the same brain processes: The effect of reference electrode site. Clinical Neurophysiology, 116, 2613–2631.
Kuhlthau, C. C. (2008). From information to meaning: Confronting challenges of the 21st century. Libri, 58, 66–73.
Kuo, Y. C., Chu, H. C., & Tsai, M. C. (2017). Effects of an integrated physiological signal-based attention-promoting and English listening system on students' learning performance and behavioral patterns. Computers in Human Behavior, 75, 218–227.
Lai, C. H., Liu, M. C., Liu, C. J., & Huang, Y. M. (2016). Using positive visual stimuli to lighten the online learning experience through in class questioning. International Review of Research in Open and Distributed Learning, 17(1), 23–41.
Li, Y., Xiao, X., Ma, W., Jiang, J., Qiu, J., & Zhang, Q. (2013). Electrophysiological evidence for emotional valence and competitive arousal effects on insight problem solving. Brain Research, 1538(13), 61–72.
Lin, C. H. (2006). Based design. Taipei: Hong Yang Books.
Lin, C. S., Lai, Y. C., Lin, J. C., Wu, P. Y., & Chang, H. C. (2014). A novel method for concentration evaluation of reading behaviors with electrical activity recorded on the scalp. Computer Methods and Programs in Biomedicine, 114(2), 164–171.
Lin, H. C. K., Su, S. H., Chao, C. J., Hsieh, C. Y., & Tsai, S. C. (2016). Construction of multi-mode affective learning system: Taking affective design as an example. Journal of Educational Technology & Society, 19(2), 132–147.
Lin, M. H., Lin, Y. S., & Wang, C. Y. (2019). Investigating the design logic of the ready-made design from semiotics viewpoint. Journal of Design, 24(2), 1–23.
Lin, M. H., Wang, C. Y., Cheng, S. K., & Cheng, S. H. (2011). An event-related potential study of semantic style-match judgments of artistic furniture. International Journal of Psychophysiology, 82, 188–195.
Luo, J., & Knoblich, G. (2007). Studying insight problem solving with neuroscientific methods. Methods, 42, 77–86.
Ma, M. Y., & Wei, C. C. (2016). A comparative study of children's concentration performance on picture books: Age, gender, and media forms. Interactive Learning Environments, 24(8), 1922–1937.
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.
Mayer, R. E. (1992). Thinking, problem solving, cognition. NY: W. H. Freeman and Company.
Mednick, S. (1962). The associative basis of the creative process. Psychological Review, 69, 220–232.
Nacke, L. E., Stellmach, S., & Lindley, C. A. (2011). Electroencephalographic assessment of player experience: A pilot study in affective ludology. Simulation & Gaming, 42(5), 632–655.
Otto, A. R., Gershman, S. J., Markman, A. B., & Daw, N. D. (2013). The curse of planning dissecting multiple reinforcement-learning systems by taxing the central executive. Psychological Science, 24(5), 751–761.
Paivio, A. (1971). Imagery and verbal processes. New York, NY: Holt, Rinehart & Winston.
Paivio, A. (2007). Mind and its evolution: A dual coding theoretical approach. Mahwah, NJ: Erlbaum.
Patel, V.L., & Ramoni, M.F. (1997). Cognitive models of directional inference in expert medical reasoning. In P.J. Feltovich, K.M. Ford, & R.R. Hoffman (Eds.), Expertise in context (pp. 67–99). London: MIT Press.
Proverbio, A. M., Zotto, M. D., & Zani, A. (2007). The emergence of semantic categorization in early visual processing: ERP indices of animal vs. artifact recognition. BMC neuroscience, 8(24), 1–16.
Robertson, S. I. (2001). Problem solving. Luton: Psychology Press.
Rodriguez Buritica, J. M., Heekeren, H. R., Li, S. C., & Eppinger, B. (2018). Developmental differences in the neural dynamics of observational learning. Neuropsychologia, 119, 12–23.
Rominger, C., Papousek, I., Perchtold, C. M., Weber, B., Weiss, E. M., & Fink, A. (2018). The creative brain in the figural domain: distinct patterns of EEG alpha power during idea generation and idea elaboration. Neuropsychologia, 18, 13–19.
Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Rossion, B., Curran, T., & Gauthier, I. (2002a). A defense of the subordinate-level expertise account for the N170 component. Cognition, 85, 189–196.
Rossion, B., Gauthier, I., Goffaux, V., Tarr, M. J., & Crommelinck, M. (2002b). Expertise training with novel objects leads to left-lateralized facelike electrophysiological responses. Psychological Science, 13, 250–257.
Rossion, B., Gauthier, I., Tarr, M. J., Despland, P., Linotte, S., Bruyer, R., et al. (2000). The N170 occipito-temporal component is enhanced and delayed to inverted faces but not to inverted objects: an electrophyiological account of face-specific processes in the human brain. NeuroReport, 11, 1–6.
Rossion, B., Joyce, C. A., Cottrell, G. W., & Tarr, M. J. (2003). Early lateralization and orientation tuning for face, word, and object processing in the visual cortex. Neuroimage, 20(3), 1609–1624.
Rossion, B., Kung, C. C., & Tarr, M. J. (2004). Visual expertise with nonface objects leads to competition with the early perceptual processing of faces in the human occipitotemporal cortex. PNAS, 101, 14521–14526.
Sakaki, M., & Niki, K. (2011). Effects of the brief viewing of emotional stimuli on understanding of insight solutions. Cognitive, Affective, and Behavioral Neuroscience, 11(4), 526–540.
Schweinberger, S. R., Huddy, V., & Burton, A. M. (2004). N250r: Aface-selective brain response to stimulus repetitions. NeuroReport, 15(9), 1501–1505.
Scott, L. S., Tanaka, J. W., & Sheinberg, D. L. (2006). A reevaluation of the electrophysiological correlates of expert object processing. Journal of Cognitive Neuroscience, 18(9), 1453–1465.
Searleman, A., & Herrmann, D. J. (1994). Memory from a broader perspective. New York: McGraw-Hill.
Semlitsch, H. V., Anderer, P., Schuster, P., & Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts applied to the P300 ERP. Psychophysiology, 23, 695–703.
Shadiev, R., Huang, Y. M., & Hwang, J. P. (2017). Investigating the effectiveness of speech-to-text recognition applications on learning performance, attention, and meditation. Educational Technology Research and Development, 65(5), 1239–1261.
Shadiev, R., Wu, T. T., & Huang, Y. M. (2017). Enhancing learning performance, attention, and meditation using a speech-to-text recognition application: Evidence from multiple data sources. Interactive Learning Environments, 25(2), 249–261.
Sternberg, R. J., & Lubart, T. I. (1996). Investing in creativity. American Psychologist, 51, 677.
Stevens, C. E., Jr., & Zabelin, D. L. (2019). Creativity comes in waves: an EEG-focused exploration of the creative brain. Current Opinion in Behavioral Sciences, 27, 154–162.
Subramaniam, K., Kounios, J., Parrish, T. B., & Jung-Beeman, M. (2009). A brain mechanism for facilitation of insight by positive affect. Journal of Cognitive Neuroscience, 21, 415–432.
Sun, J. C. Y. (2014). Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data. Computers & Education, 72, 80–89.
Sviderskaya, N. E., Taratynova, G. V., & Kozhedub, R. G. (2006). The effects of the experience of forming visual images on the spatial organization of the EEG. Neuroscience and Behavioral Physiology, 36, 941–949.
Tanaka, J. W., & Curran, T. (2001). A neural basis for expert object recognition. Psychological Science, 12, 43–47.
Taylor, M. J., McCarthy, G., Saliba, E., & Degiovanni, E. (1999). ERP evidence of developmental changes in processing of faces. Clinical Neurophysiology, 110, 910–915.
Tovée, M. J. (1998). Face processing: Getting by with a little help from its friends. Current Biology, 8(9), 317–320.
Wang, C. Y., & Chung, Y. J. (2017). Detecting the semantic differences of congruence, ambiguity, and incongruence in the picture-word matching task using the event-related potential. Journal of Design, 22(1), 25–45.
Wang, C. C., & Hsu, M. C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912–923.
Wei, C. C., & Ma, M. Y. (2017). Influences of visual attention and reading time on children and adults. Reading & Writing Quarterly, 33(2), 97–108.
Wiese, H. (2013). Do neural correlates of face expertise vary with task demands? Event-related potential correlates of own-and other-race face inversion. Frontiers in Human Neuroscience, 7, 1–13.
Wong, S. W., Chan, R. H., & Mak, J. N. (2014). Spectral modulation of frontal EEG during motor skill acquisition: A mobile EEG study. International Journal of Psychophysiology, 91(1), 16–21.
Xu, J., & Zhong, B. (2018). Review on portable EEG technology in educational research. Computers in Human Behavior, 81, 340–349.
Yu, J. H., & Sim, K.-B. (2016). Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram. Optik, 127(20), 9711–9718.
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I am grateful to Shih-Kuen Cheng and I-Chung Han at National Central University for the helpful comments on ERP analysis.
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Wang, CY. Differences in perception, understanding, and responsiveness of product design between experts and students: an early event-related potentials study. Int J Technol Des Educ 31, 1039–1061 (2021). https://doi.org/10.1007/s10798-020-09592-z
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DOI: https://doi.org/10.1007/s10798-020-09592-z