Subjects: Psychology >> Applied Psychology submitted time 2024-07-05
Abstract： The causal relationship between attention and value is a core topic in decision-making research. With advancements in computational modeling, particularly the application of the attentional Drift Diffusion Model (aDDM), researchers have found that attention can amplify the value of options to guide decisions, known as "choose what I see." However, the limitations and conflicting results of this model in practical applications have called this perspective into question, suggesting an alternative causal relationship—"see what I choose," where consumers make choices based on the value of items, with attention merely reflecting this value passively. This review aims to explore the application value of aDDM and its conflicting findings in elucidating the relationship of attention and value. The results revealed that, although aDDM provides concrete evidence for the "choose what I see" hypothesis, the strength of this evidence is not sufficient to fully support the view. Moreover, the modeling results uncover a more complex interaction between attention and value, suggesting a potentially bidirectional dynamic relationship between the two. Future research should refine the time windows of attention and investigate this causal relationship within a more open theoretical framework, while also considering its implications for consumer decision-making.
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Subjects: Psychology >> Applied Psychology submitted time 2024-05-15
Abstract： The spontaneous human preference for high-calorie foods often leads to imbalanced dietary intake and contributes to obesity. Therefore, reducing the appeal of high-calorie foods and enhancing the appeal of low-calorie alternatives are crucial for promoting healthy eating. The aesthetics of food, which can be divided into classical and expressive beauty—both of which are perceived as equally attractive—play a vital role in enhancing its hedonic value. This study aimed to explore how these two aesthetic classifications affect the choice of high- or low-calorie foods using a food decision-making paradigm. By investigating the behavioural and neural mechanisms underlying the influence of different aesthetic features on healthy food choices, we sought to enhance our understanding of the intrinsic processes involved in dietary decision-making.
This study (N = 31) employed a within-subjects experimental design of 2 (Aesthetic features: classical beauty, expressive beauty) × 2 (Food calories: high, low) to explore how visual aesthetics and hedonic value influence dietary decisions. We combined behavioural measures, algorithmic modelling, and electroencephalography (EEG) to investigate this interaction. Specifically, a hierarchical drift diffusion model (HDDM) was used to fit participants’ response times (RTs) and choice data and estimate decision parameters, including drift rate (v), threshold (a), and nondecision time (ndt), for each condition separately. EEG recordings were collected according to the international 10-20 system using tin electrodes mounted on a flexible cap, capturing brain activity from 64 scalp locations. The N300, N400, and CPP event-related potentials (ERPs) were analysed as indices of calorie processing, aesthetic feature processing, and decision signal accumulation, respectively.
Behavioural results revealed that participants preferred high-calorie foods, as indicated by higher choice rates and shorter RTs, compared to low-calorie foods. Additionally, foods plated with classical beauty were chosen more frequently and with shorter RTs than those plated with expressive beauty. Notably, the influence of caloric content on food choice was significantly greater than that of aesthetic features. HDDM parameter estimation showed that high-calorie foods and those plated with classical beauty had higher drift rates, suggesting faster decision-making. Furthermore, aesthetic features moderated the impact of caloric content on drift rates: classical beauty decreased rejection speeds for low-calorie foods and increased their selection probability, while expressive beauty slowed the choice process for high-calorie foods and increased their rejection probability. EEG analysis revealed that low-calorie foods elicited a larger N300 amplitude than did high-calorie foods, indicating greater cognitive processing. Foods plated with expressive beauty elicited a larger N400 amplitude than those plated with classical beauty, indicating deeper semantic processing. Additionally, for high-calorie foods, the two aesthetic classes induced significant differences in CPP; however, for low-calorie foods, no significant differences were found. This pattern indicates that conflicts between caloric and aesthetic values increase decision-making difficulty.
In conclusion, the results showed that in dietary decision-making, classical beauty (vs. expressive beauty) was associated with greater aesthetic value and greater semantic processing fluency. Aesthetic value could significantly influence the perceived reward of calorie content. Additionally, the salience of calorie value exceeded that of aesthetic value. Furthermore, both synergistic and competitive interactions between caloric and aesthetic values occurred during the decision evidence accumulation process, reflecting the intensity of motivational conflict and affecting both decision speed (v) and decision difficulty (CPP). This study revealed the moderating effect and cognitive neural basis of aesthetic value in healthy eating decisions and provided guidance on the aesthetic design of food plating for promoting healthy eating choices in practical applications.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： Splatting is a classical direct volume rendering method based on object order, which volume data exists in layers and each layer of data has similar lines. The amount of calculation data restricts the speed of image rendering. In order to further improve the rendering speed, this paper used a method based on the combination of similarity between adjacent layers and empty voxel jump to speed up the algorithm. It filtered the 3D texture data of the image in the process of reading the data, and then used the footstep table in the 3D texture data after filtering was projected in two dimensions. It calculated the gray value of each point by using the similarity between adjacent layers, and classified the data according to the gray value of each point to calculate the empty voxel that had no effect on the imaging, skiped the rendering process and speeded up the algorithm. The experimental results show that the optimized algorithm can solve and improve the spatial correlation and operation efficiency of splatting algorithm to a certain extent on the basis of ensuring the quality of the drawn image.
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