Understanding Perceptual And Conceptual Fluency At A Large Scale

Keywords

Cognitive information processing; Construal level theory; Marketing application; Visual design

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)).

Publication Date

1-1-2018

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11220 LNCS

Number of Pages

697-712

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-030-01270-0_41

Socpus ID

85055087574 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85055087574

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