古典美还是表现美：摆盘美学影响健康饮食决策的计算与神经机制

doi:10.3724/SP.J.1041.2024.01061

摘要/Abstract

摘要：

前人研究发现食物摆盘的美感水平会影响个体的食物选择, 但未有研究进一步探讨相同美感、不同审美特征对健康饮食决策的影响机制。本研究招募被试34名, 采用基于价值的食物决策范式, 使用2 (审美特征：古典美, 表现美) × 2 (食物热量：高, 低)被试内设计, 通过分离计算模型参数与脑电指标来检验不同审美特征差异化的审美价值, 以及审美价值对热量影响的调节效应及其认知神经基础。结果显示：(1)古典美(vs. 表现美)审美价值更高, 食物选择率和漂移率(v)更高、N400振幅更低; (2)审美价值调节热量价值, 但热量价值的突显性高于审美, 热量信息的神经处理时间更早(240~320 ms); (3)审美价值的调节效应发生在决策证据积累过程中, 影响漂移率(v)以及中央顶叶正波(CPP)。本研究在理论层面揭示了健康饮食决策中审美价值的调节效应及认知神经基础, 同时在实践应用方面为助推健康饮食选择提供了食物摆盘的审美设计指导。

关键词: 健康饮食, 价值决策, 审美特征, 计算建模, 事件相关电位

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.

Key words: healthy eating, value-based decision making, aesthetic characteristics, decision modeling, event-related potentials

中图分类号:

B842

刘梦颖, 蒋婧怡, 杨依琳, 江波, 黄建平. (2024). 古典美还是表现美：摆盘美学影响健康饮食决策的计算与神经机制. 心理学报, 56(8), 1061-1075.

LIU Mengying, JIANG Jingyi, YANG Yilin, JIANG Bo, HUANG Jianping. (2024). Classical or expressive aesthetics: Computational and neural mechanisms by which plating aesthetics influence healthy eating decisions. Acta Psychologica Sinica, 56(8), 1061-1075.

图/表 12

图1 实验材料示例

图2 实验程序示意图

图3 所有试次、所有被试(n = 31)的刺激锁时(A)与反应锁时(B)的广义平均波形。线条颜色代表不同电极点, 峰值活动时间点的头皮拓扑图绘制在波形图上方。彩图见电子版, 下同

图4 食物热量与摆盘美学的描述性统计值。 A)不同条件下不同食物选择模式的总试次分布; B)不同条件下食物选择率均值; C)不同条件下不同食物选择模式所对应的反应时中位数; D)不同条件下的反应时均值。注：误差棒为SE, ***为p < 0.001, **为p < 0.01, *为p < 0.05

图5 决策模型中代表阈值a参数的收敛相关图。 A) 所有迭代的轨迹图(10000次, 前1100次被舍弃); B) 拟合的最后100次迭代自相关; C) 阈值的估计直方图, 在算法收敛的数值周围呈正态分布。直方图显示了算法每次迭代的估计值频率

表1 四个HDDM的DIC值

模型	DIC
热量◊审美特征	13623
热量	13652
审美特征	14015
无	14197

图6 决策参数估计的后验分布A) 决策阈值a; B) 非决策时间t; C) 漂移率v; D) 不同条件下漂移率v的平均数和数据分布, 误差棒反映标准误SE。注: HC为高热量古典美摆盘; HE为高热量表现美; LC为低热量古典美; LE为低热量表现美, 误差棒为SE, ***为p < 0.001, **为p < 0.01

表2 后验分布概率差异比较

变量	配对	P (%)
热量	P (HC > LC)	100
热量	P (HE > LE)	99.86
审美	P (HC > HE)	99.56
审美	P (LC > LE)	89.08
热量×审美	P (HC > LE)	100
热量×审美	P (HE > LC)	97.28
方向	P (HE > 0)	98.88
	P (HC > 0)	100
	P (LE < 0)	98.36
	P (LC < 0)	65.66

图7 刺激锁时与反应锁时ERP波形图与不同条件在CPP上的描述性统计。 A)刺激锁时枕叶皮层电极团的N300成分波形图及其对应地形图, 图下横线代表N300时间窗, 图右上方为局部放大的N300时间窗波形图; B) 刺激锁时枕叶皮层电极团的N400成分波形图及其对应地形图, 图下横线代表N400时间窗, 图右上方为局部放大的N400时间窗波形图; C) 反应锁时Pz点波形图, 波形图下横线代表不同条件信号积累加速的时间窗; D)各条件在CPP上的均值。注：HC为高热量古典美; HE为高热量表现美; LC为低热量古典美; LE为低热量表现美, 误差棒为SE, **为p < 0.01

附表1 美学维度操纵检验

古典美	表现美	t	df	p	95% Confidence Interval		效应量Cohen's d
古典美	表现美	t	df	p	Lower	Upper	效应量Cohen's d
美感	美感	−1.69	29	0.102	−0.342	0.0326	−0.31
古典美感	古典美感	3.76	29	<0 .001	0.225	0.7617	0.687
表现美感	表现美感	−13.7	29	< 0.001	−1.888	−1.3974	−2.5

附表2 食物热量操纵检验

高热量	低热量	t	df	p	95% Confidence Interval		效应量Cohen's d
高热量	低热量	t	df	p	Lower	Upper	效应量Cohen's d
热量	热量	16.62	29	<0 .001	1.51	1.9341	3.035

附表3 不同摆盘的同种食物可被识别为相同食物的操纵检验

	t	df	p	95% Confidence Interval		效应量Cohen's d
	t	df	p	Lower	Upper	效应量Cohen's d
比较	13.3	29	< 0.001	1.59	2.18	2.42

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