Influence of Visual Coding Based on Attraction Effect on Human-Computer Interface

Abstract

Decision-making is often influenced by contextual information on the human-computer interface (HCI), with the attraction effect being a common situational effect in digital nudging. To address the role of visual cognition and coding in the HCI based on the attraction effect, this research takes online websites as experimental scenarios and demonstrates how the coding modes and attributes influence the attraction effect. The results show that similarity-based attributes enhance the attraction effect, whereas difference-based attributes do not modulate its intensity, suggesting that the influence of the relationship driven by coding modes is weaker than that of coding attributes. Additionally, variations in the strength of the attraction effect are observed across different coding modes under the coding attribute of similarity, with color coding having the strongest effect, followed by size, and labels showing the weakest effect. This research analyzes the stimulating conditions of the attraction effect and provides new insights for exploring the relationship between cognition and visual characterization through the attraction effect at the HCI. Furthermore, our findings can help apply the attraction effect more effectively and assist users in making more reasonable decisions.

Keywords: attraction effect; decision-making; eye movement; information coding; visual cognition.

Figure 1

The alternative property space (the shadow area represents the range of property values for D governed by T. Decoy: D, Target: T, Competitor: C).

Figure 2

Coding attributes and coding modes of the experimental materials (taking headphones as an example).

Figure 3

Experiment procedures.

Figure 4

The RST of coding modes, respectively (the first line of each coding mode is a binary set, and the second line is a ternary set).

Figure 5

The RST of coding attributes, respectively (the first line of each coding mode is a binary set, and the second line is a ternary set).

Figure 6

The significance test for the RST distribution map under the coding attributes of similarity (left) and difference (right). (* means p < 0.05 and ** means p < 0.01).

Figure 7

Significance test of RST for all coding modes under coding attribute of similarity (ternary sets only). (* means p < 0.05 and ** means p < 0.01).

Figure 8

Significance test of RST in same coding mode under different coding attributes (ternary sets only). (** means p < 0.01).

Figure 9

GP and HM with different coding modes under coding attribute of similarity.

Figure 10

GP and HM with different coding attributes under color coding.

Figure 11

GP and HM with different coding attributes under size coding.

Figure 12

GP and HM with different coding attributes under label coding.

Figure 13

Significance test of RFT of different coding modes under coding attribute of similarity. (* means p < 0.05 and ** means p < 0.01).

Figure 14

Significance test of RFT of different coding attributes under three coding modes. (* means p < 0.05 and ** means p < 0.01).

Figure 15

Theoretical relationship in this research.