Understanding Perceptual and Conceptual Fluency at a Large Scale

  • Shengli HuEmail author
  • Ali Borji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)


We create a dataset of 543,758 logo designs spanning 39 industrial categories and 216 countries. We experiment and compare how different deep convolutional neural network (hereafter, DCNN) architectures, pretraining protocols, and weight initializations perform in predicting design memorability and likability. We propose and provide estimation methods based on training DCNNs to extract and evaluate two independent constructs for designs: perceptual distinctiveness (“perceptual fluency” metrics) and ambiguity in meaning (“conceptual fluency” metrics) of each logo. We provide evidences of causal inference that both constructs significantly affect memory for a logo design, consistent with cognitive elaboration theory. The effect on liking, however, is interactive, consistent with processing fluency (e.g., Lee and Labroo (2004), and Landwehr et al. (2011)).


Marketing application Visual design Cognitive information processing Construal level theory 


  1. 1.
    Bornstein, R.F., D’Agostino, P.R.: The attribution and discounting of perceptual fluency: Preliminary tests of a perceptual fluency/attributional model of the mere exposure effect. Soc. Cognit. 12(2), 103–128 (1994)CrossRefGoogle Scholar
  2. 2.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  3. 3.
    Donderi, D.C.: Visual complexity: a review. Psychol. Bull. 132(1), 73–97 (2006)CrossRefGoogle Scholar
  4. 4.
    Estes, W.K.: Array models for category learning. Cognit. Psychol. 18(4), 500–549 (1986)CrossRefGoogle Scholar
  5. 5.
    Flavell, J.H.: Metacognition and cognitive monitoring: a new area of cognitive-developmental inquiry. Am. Psychol. 34(10), 906–911 (1979)CrossRefGoogle Scholar
  6. 6.
    Hagtvedt, H.: The impact of incomplete typeface logos on perceptions of the firm. J. Market. 75(4), 86–93 (2011)CrossRefGoogle Scholar
  7. 7.
    Halberstadt, J., Rhodes, G.: The attractiveness of nonface averages: implications for an evolutionary explanation of the attractiveness of average faces. Psychol. Sci. 11(4), 285–289 (2000)CrossRefGoogle Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  10. 10.
    Hintzman, D.L.: Human learning and memory: connections and dissociations. Annu. Rev. Psychol. 41(1), 109–139 (1990)CrossRefGoogle Scholar
  11. 11.
    Isola, P., Xiao, J., Parikh, D., Torralba, A., Oliva, A.: What makes a photograph memorable? IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1469–1482 (2014)CrossRefGoogle Scholar
  12. 12.
    Isola, P., Xiao, J., Torralba, A., Oliva, A.: What makes an image memorable? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 145–152 (2011)Google Scholar
  13. 13.
    Jiang, Y., Gorn, G.J., Galli, M., Chattopadhyay, A.: Does your company have the right logo? How and why circular-and angular-logo shapes influence brand attribute judgments. J. Consum. Res. 42(5), 709–726 (2016)CrossRefGoogle Scholar
  14. 14.
    Khosla, A., Bainbridge, W., Torralba, A.: Modifying the memorability of face photographs. In: Proceedings of the IEEE (2013)Google Scholar
  15. 15.
    Khosla, A., Xiao, J., Torralba, A.: Memorability of image regions. In: Advances in Neural (2012)Google Scholar
  16. 16.
    Khosla, A., Das Sarma, A., Hamid, R.: What makes an image popular? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 867–876. ACM (2014)Google Scholar
  17. 17.
    Khosla, A., et al.: Understanding and predicting image memorability at a large scale. In: International Conference on Computer Vision (ICCV) (2015)Google Scholar
  18. 18.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  20. 20.
    Landwehr, J.R.: Gut liking for the ordinary: Incorporating design fluency improves automobile sales forecasts. Market. Sci. 30(3), 416–429 (2011)CrossRefGoogle Scholar
  21. 21.
    Langlois, J.H., Roggman, L.A.: Attractive faces are only average. Psychol. Sci. 1(2), 115–121 (1990)CrossRefGoogle Scholar
  22. 22.
    Lee, A.Y., Labroo, A.A.: The effect of conceptual and perceptual fluency on brand evaluation. J. Market. Res. 41(2), 151–165 (2004)CrossRefGoogle Scholar
  23. 23.
    Rahinel, R., Nelson, N.M.: When brand logos describe the environment: design instability and the utility of safety-oriented products. J. Consum. Res. 43(3), 478–496 (2016)CrossRefGoogle Scholar
  24. 24.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  25. 25.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  26. 26.
    Veenman, M.V., Van Hout-Wolters, B.H., Afflerbach, P.: Metacognition and learning: Conceptual and methodological considerations. Metacognition and learning 1(1), 3–14 (2006)CrossRefGoogle Scholar
  27. 27.
    Wang, M.C., Haertel, G.D., Walberg, H.J.: What influences learning? A content analysis of review literature. J. Educ. Res. 84(1), 30–43 (1990)CrossRefGoogle Scholar
  28. 28.
    Whittlesea, B.W.: Illusions of familiarity. J. Exp. Psychol. Learn. Mem. Cognit. 19(6), 1235–1253 (1993)CrossRefGoogle Scholar
  29. 29.
    Winkielman, P., Cacioppo, J.T.: Mind at ease puts a smile on the face: psychophysiological evidence that processing facilitation elicits positive affect. J. Personal. Soc. Psychol. 81(6), 989–1000 (2001)CrossRefGoogle Scholar
  30. 30.
    Winkielman, P., Halberstadt, J., Fazendeiro, T., Catty, S.: Prototypes are attractive because they are easy on the mind. Psychol. Sci. 17(9), 799–806 (2006)CrossRefGoogle Scholar
  31. 31.
    Winkielman, P., Schwarz, N., Fazendeiro, T., Reber, R., Musch, J., Klauer, K.C.: The hedonic marking of processing fluency: implications for evaluative judgment. In: The Psychology of Evaluation: Affective Processes in Cognition and Emotion, pp. 189–217 (2003)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA
  2. 2.University of Central FloridaOrlandoUSA

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