Artificial intelligence (AI) tools are rapidly transforming the field of traditional artistic creation, influencing painting processes and human creativity. This study explores human–AI cooperation in real-time artistic drawing by using the AIGC tool KREA.AI. Participants wear eye trackers and perform drawing tasks by adjusting the AI parameters. The research aims to investigate the impact of cross-screen and non-cross-screen conditions, as well as different viewing strategies, on cognitive load and the degree of creative stimulation during user–AI collaborative drawing. Adopting a mixed design, it examines the influence of different cooperation modes and visual search methods on creative efficacy and visual perception through eye-tracking data and creativity performance scales. The cross-screen type and task type have a significant impact on total interval duration, number of fixation points, average fixation duration, and average pupil diameter in occlusion decision-making and occlusion hand drawing. There are significant differences in the variables of average gaze duration and average pupil diameter among different task types and cross-screen types. In non-cross-screen situations, occlusion and non-occlusion have a significant impact on average gaze duration and pupil diameter. Tasks in non-cross-screen environments are more sensitive to visual processing. The involvement of AI in hand drawing in non-cross-screen collaborative drawing by designers has a significant impact on their visual perception. These results help us to gain a deeper understanding of user behaviour and cognitive load under different visual tasks and cross-screen conditions. The analysis of the creative efficiency scale data reveals significant differences in designers’ ability to supplement and improve AI ideas across different modes. This indicates that the extent of AI participation in the designer’s hand-drawn creative process significantly impacts the designer’s behaviour when negotiating design ideas with the AI.

Individual decision-making is easily governed by contextual features, and the attraction effect is a common context effect. Previous studies have extensively investigated the boundary conditions of the attraction effect, but all have rarely considered the relationship between interface visual presentation and the attraction effect. In this work, we attempt to consider how the interface layout and the relative placement of choices affects the attraction effect. We constructed a virtual shopping interface for two experiments. In the first experiment, we successfully replicated the attraction effect in both horizontal and vertical layouts, but found no influence of layout and relative placement. To further explore the influence of more layouts on the attraction effect and the stability of the effect, in the second experiment, we expanded the choices set and found that the attraction effect appeared stably under all three layouts, but there was a difference in intensity and the relative placement of the choices having a significant effect under the horizontal layout. Through this study, we extend the boundary conditions of attraction effect and provides new clues for future research. In addition, the results can help online environments such as shopping to better apply the attraction effect.

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.