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Understanding Perceptual and Conceptual Fluency at a Large Scale

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

Abstract

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)).

Keywords

Marketing application Visual design Cognitive information processing Construal level theory 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

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