Understanding Perceptual And Conceptual Fluency At A Large Scale

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))

The differential effects of fluency due to repetition and fluency due to color contrast on judgments of truth

Two experiments contrast the effects of fluency due to repetition and fluency due to color contrast on judgments of truth, after participants learn to associate high levels of fluency with falseness (i.e., a reversal of the fluency-truth link). Experiment 1 shows that the interpretation of fluency as a sign of truth is harder to reverse when learning is promoted with repetition rather than with perceptual fluency. Experiment 2 shows that when color contrast and repetition are manipulated orthogonally, the reversal of the truth effect learned with color contrast does not generalize to repetition. These results suggest specificities in the processing experiences generated by different sources of fluency, and that their influences can be separated in contexts that allow the contrast of their distinctive features. We interpret and discuss these results in light of the research addressing the convergence vs. dissociation of the effects elicited by different fluency sources