Spatial Size Can Affect Social Categorization of the Rich and the Poor

Zhang, Xiaobin; Zhang, Zhe

doi:10.3389/fpsyg.2020.01914

ORIGINAL RESEARCH article

Front. Psychol., 11 August 2020

Sec. Cultural Psychology

Volume 11 - 2020 | https://doi.org/10.3389/fpsyg.2020.01914

Spatial Size Can Affect Social Categorization of the Rich and the Poor

$\r\nXiaobin Zhang*$ Xiaobin Zhang^1*

Zhe Zhang²

¹School of Psychology, Shaanxi Normal University, Xi’an, China
²School of Psychology, Northwest Normal University, Lanzhou, China

Previous research has shown that representation of certain social-category knowledge, such as that regarding gender, involves the process of perceptual simulation. The present research extended these findings and explored whether social categorization based on wealth, which is an important dimension of social categorization, involved perceptual simulation of spatial size. In Experiment 1, we used high- and low-income occupations as stimuli; categorization of high-income occupations presented in larger font was faster relative to that of those presented in small font, and vice versa for low-income occupations. In Experiments 2, 3, and 4, we used high-income occupations without social power and low-income occupations, names designated as those of rich and poor people, and idioms describing wealth and poverty as stimuli, respectively. All three experiments showed that responses to wealth-related stimuli in larger font were faster relative to those to the same stimuli in small font, and vice versa for poverty-related stimuli. These results suggest that social categorization based on wealth is grounded in perceptual simulation of spatial size.

Introduction

Models of grounded cognition assume that cognitive representations of concepts include sensory, motor, and introspective states, which are activated via simulations (Barsalou, 1999, 2008). Recently, researchers found that a fundamental social-cognitive process, social categorization, was represented by sensorimotor activity, and cognitive representations of social categories, such as gender and political persuasion, were grounded in perceptual simulation and sensorimotor activity (Slepian et al., 2011, 2012; Zhang et al., 2014; Slepian, 2015). Extending this literature, the current work examined whether cognitive representations of social categories of the rich and the poor, which is one of most important social categorization dimensions (Leahy, 1981; Harvey and Bourhis, 2013), involved perceptual simulation of the spatial size dimension. Specifically, we explored the possibility that spatial size perceptual simulation shaped the categorization of the rich and the poor. How abstract concepts are mentally represented is an essential question within the domain of cognitive psychology. A wide array of research found that human cognition is grounded in and shaped by sensorimotor experiences, and that our conceptual representations are recruited into partial simulations, which reenact various embodied states (Barsalou, 2008). Exploring the connection between the social categories of the rich and the poor and the perceptual simulation of the spatial size dimension is an important research issue on embodied cognition and social cognition (Cloutier et al., 2005; Macrae et al., 2005; Slepian et al., 2011). First, it can extend the representation of social-category knowledge involving the process of perceptual simulation to wealth. Second, social categorization is the basis and prerequisite for stereotype activation, stereotype application, and even prejudice and discrimination (Allport, 1954), and examining the effect of spatial size on the social categorization of the rich and the poor may provide a new perspective for reducing stereotypes, prejudices, and discrimination against the rich and the poor.

Slepian et al. (2011) were the first to find that the process of social categorization involving gender was embodied within sensorimotor experience. They showed that perceivers’ gender-category representations included the sensorimotor experience of different levels of hardness, with masculinity associated with toughness and femininity associated with tenderness. Specifically, participants continuously squeezed a soft or hard ball while categorizing sexually ambiguous faces, and the results indicated that faces were categorized as feminine by participants squeezing the soft ball more often relative to those squeezing the hard ball (Slepian et al., 2011). Inspired by this research, Zhang et al. (2014) found that the process of gender categorization involved perceptual simulation of spatial height and size dimensions. Their results showed that responses to masculine faces were faster when they were in the higher position, rather than the lower position, and vice versa for feminine face categorization. Further, categorization of men’s names depicted in larger font was faster relative to that of men’s names depicted in smaller font, whereas opposite response patterns were observed for women’s names (Zhang et al., 2014). Consistent with the findings reported by Zhang et al. (2014). Lamer et al. (2017) suggested that spatial position affected visual perception of gender, in that participants perceived greater femininity in faces that appeared lower in space. In addition to the representation of knowledge regarding gender social categories, Slepian (2015) found that the categorization of Republicans and Democrats involved sensorimotor processes. Specifically, half of the faces in their study were presented next to a hard object (e.g., a rock), and the remaining faces were presented next to a soft object (e.g., a pillow); participants categorized the target faces as Republicans or Democrats. The results indicated that both perceptual simulation metaphors and sensorimotor processes (toughness) could bias social categorization of political persuasion, with Republicans associated with sensorimotor experiences of hardness and Democrats associated with sensorimotor experiences of softness.

The categorization of individuals according to wealth is another very important and common dimension of social categorization, and social stratification based on wealth is obvious in most countries (Leahy, 1981; Harvey and Bourhis, 2013). For example, children aged 3–5 years have been seen to understand the terms poor and rich and classify people as such (Ramsey, 1991), and those aged 6–11 years begin to believe that poor and rich people possess differences in certain traits such as intelligence and effort (Leahy, 1981). One of the largest perceptual differences between rich and poor involves differences in spatial size. For example, in China, because rich people are able to eat nutritious food and do not need to engage in high-intensity manual labor, they are often perceived as more likely to be heavier relative to poor people. In fact, Chinese researchers have found that the weight and height of adolescents in the wealthy areas of China is significantly greater than those of adolescents in the poor areas of China (Han and Xu, 2014). In addition, in everyday life, being rich is associated with the possession of larger houses and vehicles. We often describe wealth using words related to large spaces, such as in the idiom “rich family and large room ( www.frontiersin.org ).” In contrast, we often describe poverty using words related to small spaces, such as in the Chinese idiom “too poor to have place to lay a cone ().” Therefore, perceptual simulations of spatial size can provide a foundation for representations of wealth and poverty, and perceivers can call the bipolar extremes of this difference to mind when they think of the rich (“bigger spatial size”) and the poor (“small spatial size”).

It should be pointed out that previous research has examined spatial size in terms of social power, which, although distinct from wealth, can provide ideas of how spatial size may relate to wealth. These studies showed that categorization of groups with different levels of power, based on the process of power judgment, involves vertical position and the perceptual simulation of spatial size (Schubert, 2005; Schubert et al., 2009). For instance, Schubert (2005) found that judgment of a group’s power was influenced by the group’s vertical position in space, and reactions to power were faster when it was presented at the top, relative to the bottom of the screen. Further, Schubert et al. (2009) showed that power categorization was affected by spatial size, and reaction times to stimuli representing the categorization of power presented in larger font were faster relative to those depicted in smaller font, whereas opposite response patterns were observed for stimuli representing powerlessness. Although this research indicated that groups with high and low social power were associated with perceptual simulations of vertical position and spatial size, the findings only indicated that sensorimotor processes could influence the process of power judgment rather than the process of social categorization. In addition, social power and wealth are not equivalent concepts. Social power means the ability to influence others or control others’ outcomes (Fiske, 1993). Wealth, on the other hand, is the possession of a large amount of money. Although some people with social power are wealthy, individuals with power are not necessarily wealthy, and wealthy people do not necessarily possess social power.

We proposed that the social categorization process based on wealth, which is an important social categorization dimension (Leahy, 1981; Harvey and Bourhis, 2013), may involve sensory metaphors of different spatial sizes, and the aim of the current study was to determine whether perceptual simulation of spatial size would affect the categorization of the rich and the poor. We hypothesized that categorization as rich would be faster when stimuli representing rich people were presented in large spatial size (vs. small spatial size), and categorization as poor would be faster when stimuli representing poor people were presented in small spatial size (vs. large spatial size). In Experiment 1, we used high-income and low-income occupations to represent wealth and poverty, respectively (generally speaking, in China, high-income earners have more wealth than low-income earners), and explored whether spatial size affected the categorization of these occupations. In Experiment 2, we compared high-income occupations without social power and low-income occupations and examined whether spatial size affected the categorization of high- and low-income occupations. In Experiment 3, we explored whether spatial size affected categorization of names designated as those of rich and poor people. In Experiment 4, we explored whether spatial size affected categorization of abstract idioms describing wealth and poverty.

Experiment 1

Materials and Methods

Ethics Statement

This study was reviewed and approved by the committee for the protection of subjects at Northwest Normal University, School of Psychology Ethics Committee. Written consent was obtained from all participants before the experiment, according to the established guidelines of the committee. This procedure was followed in all experiments.

Participants and Design

Thirty Chinese university students (M = 21.27, SD = 2.79 years; six men) were recruited as participants. Using G^∗Power 3.1 (Faul et al., 2009), we calculated an optimal sample size of N = 29 using a conventional threshold of α = 0.05 for Type I error, a power of 0.8, and an effect size of η_p² = 0.07. Based on the sample size in a similar previous study (Meier and Robinson, 2004), we decided to recruit 30 participants prior to data collection. This method of participant selection was performed for all four experiments. This experiment involved a 2 (occupation type: high income, low income) × 2 (font size: large, small) repeated measures design.

Materials

The experimental materials were 16 high-income and 16 low-income occupation names selected as follows. First, 78 common occupation names (such as doctor, farmer) were selected via an Internet search. Second, 15 participants who did not participate in the main experiment rated the income level of each occupation (such as doctor) on a five-point scale, in which “1” indicated “very low income” and “5” indicated “very high income.” These participants also rated the familiarity of each occupation on a five-point scale, in which “1” indicated “low familiarity” and “5” indicated “high familiarity.” Third, based on scores for the income dimension, 16 high-income occupations (M = 4.22, SD = 0.30) and 16 low-income occupations (M = 2.17, SD = 0.24) were selected, and a significant difference was found in the average scores of income between these selected 16 high-income and 16 low-income occupations, t(30) = 19.92, p < 0.001. The scores for familiarity for all occupations were > 3, and there was no significant difference in the familiarity scores between high- (M = 3.82, SD = 0.26) and low-income occupations (M = 3.71, SD = 0.19), t(30) = 1.23, p = 0.238.

Procedure

Participants were asked to sit in front of a computer screen to complete the experiment. In each trial, a fixation cross was presented at the center of the screen for 800 ms, followed by a high- or low-income occupation name in large (font size 50) or small (font size 30) font for 2,000 ms or until the participant responded. Participants were required to classify occupations according to their income (high vs. low) as quickly and as accurately as possible by pressing the “high income” or “low income” key (Meier and Robinson, 2004). The keys were counterbalanced across the participants. If the response time exceeded 750 ms, the words “Please respond quickly” appeared in red font at the center of the screen for 700 ms.

The experiment included 128 trials. Each occupation was presented twice in both large and small font respectively. Prior to the main experiment, the participants completed 24 practice trials.

Results and Discussion

Mean categorization latency was the dependent measure of interest¹. We firstly performed log transformation of the data, however, untransformed means are reported for ease of interpretation. Response times that were outside of 3 SDs from the mean (32 trials, 0.83%) and trials during which errors were made (275 trials, 7.16%) were excluded from the analysis (307 trials, 7.99%). A 2 (occupation type) × 2 (font size) repeated measures ANOVA was performed to examine the participants’ reaction times. The results showed that the main effect of occupation type was significant, and the mean reaction time for high-income occupations (M = 530.80, SD = 82.33) was significantly shorter relative to that for low-income occupations (M = 563.24, SD = 101.96), F(1, 29) = 96.87, p < 0.001, η_p² = 0.109. The main effect of font size was non-significant, F(1, 29) = 0.243, p = 0.622, η_p² = 0.001. As predicted, the interaction between occupation type and font size was significant, F(1, 29) = 35.19, p < 0.001, η_p² = 0.042 (see Figure 1). Further simple effect analysis showed that the mean response time for high-income occupations in large font size (M = 523.04, SD = 82.44) was significantly shorter relative to that for those in small font size (M = 536.74, SD = 81.52), F(1, 29) = 23.05, p < 0.001, η_p² = 0.028. In contrast, the mean response time for low-income occupations in large font size (M = 571.14, SD = 108.43) was significantly longer relative to that for small font size (M = 555.75, SD = 94.88), F(1, 29) = 12.99, p < 0.001, η_p² = 0.016.

FIGURE 1

Figure 1. Mean reaction latency as a function of occupation type and occupation font size (Experiment 1).

These results provided initial support for our hypothesis that the process of social categorization based on wealth involves perceptual simulation of spatial size. Wealth was associated with large spatial size, and poverty was associated with small spatial size. If a social category was consistent with the perceptual simulation (e.g., high-income occupations presented in large font), the process of social categorization based on wealth was facilitated. If a social category was incongruent with the perceptual simulation (e.g., high-income occupations presented in small font), the process of social categorization based on wealth was restricted. In Experiment 1, we used high-income occupations with social power (e.g., mayor, commander in chief, and secretary of state). Previous research has shown that the categorization of high and low levels of social power is influenced by spatial size metaphors (Schubert et al., 2009). Were the results of Experiment 1 induced by social power attached to high-income occupations? To answer this question and verify the results of Experiment 1, we examined high-income occupations without social power (e.g., doctor) in Experiment 2.