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. 2023 Dec 7;21(4):14747049231218726. doi: 10.1177/14747049231218726

Association Between Individual Differences in Dichotomous Thinking and Current and Childhood High-Crime Environments

Takahiro Mieda 1,✉, Shinya Yoshino 2, Atsushi Oshio 2
PMCID: PMC10710117  PMID: 38062631

Abstract

The way people perceive the things around them is closely related to living in a complex and challenging social environment. Dichotomous thinking (preference for dichotomy, dichotomous belief, and profit-and-loss thinking), which classifies things in a simple way, can be useful in dangerous and resource-limited environments. However, based on prior studies, people's manner of perceiving things may have developed as a response to the harshness of their childhood environment, and may not be related to their current environment. Therefore, we examined the relationship between individual differences in dichotomous thinking and high-crime environments as indicators of environmental harshness. We assessed dichotomous thinking in 41,284 Japanese residents using large-scale data from the Human Information Database FY19 compiled by NTT DATA Institute of Management Consulting, Inc. The fixed effects regression model showed that, after controlling for age, high-crime environment of the residents’ childhood was associated with dichotomous thinking, with the exception of dichotomous belief. On the other hand, their current environment of crime was not associated. In sum, our research suggests that people's dichotomous thinking tendency may be a form of adaptation to the harshness of their childhood environment rather than their current one.

Keywords: dichotomous thinking, high-crime environment, childhood environment, developmental mismatch

Introduction

How we perceive things is closely related to surviving in a complex and challenging social environment (Macrae & Bodenhausen, 2000; Simpson, 1961). However, if there are differences between our current and childhood environments, adaptive traits acquired during childhood may be disadvantageous (e.g., with respect to mental health and socially undesirable behavior) in our current environment (e.g., Bateson et al., 2004; Cosmides & Tooby, 1999; Del Giudice, 2018; Frankenhuis & Del Giudice, 2012). For example, as shown by Cosmides and Tooby (1999), people who face abuse during childhood are more likely to have been born in a social environment in which violence is an important social instrument; accordingly, they need to act in and cope with such a world. However, social environments involving violence, economics, and politics may be constantly evolving, or people themselves may move; thus, their current environment will likely be different from their childhood one. Therefore, it is important to understand the relationship between people's current and childhood environments and ways of thinking to maintain mental health and social safety.

People who grow up in an insecure and deprived environment may tend to perceive the things around them dichotomously to adapt to that environment. Dichotomous thinking refers to simplifying understanding by classifying things into two categories—black or white, good or bad, and dangerous or safe (Oshio, 2009). This thinking style has three aspects: preference for dichotomy (i.e., preferring clarity over ambiguity), dichotomous belief (i.e., the belief that everything in the world can be classified into all-or-nothing categories), and profit-and-loss thinking (i.e., the motivation to gain access to benefits and avoid disadvantages). Such a thinking style would be beneficial for surviving in an environment where available resources are limited and people might be exposed to violence and crime. This is because such simplification reduces the time required for decision-making as well as resources spent on it to enhance accuracy (Arkes, 1991; Haselton et al., 2005). Taking time to make decisions and allocating resources to ensure the accuracy of judgment can increase the risk to one's life (Haselton et al., 2005). Considering this, people with a tendency to engage in dichotomous thinking may be more likely to be found in such an environment.

Diverse evidence suggests that individual differences in dichotomous thinking are related to harsh environments. First, dichotomous thinking tendency is common within personality traits found in harsh environments—for example, narcissism (Oshio, 2009, 2012), borderline personality disorder (BPD) traits (Beck et al., 2004; Oshio, 2009, 2012; Veen & Arntz, 2000), antisocial personality disorder traits (Oshio, 2012), aggressive behavior (Oshio et al., 2016), and Dark Triad personality traits (Jonason et al., 2018), which have been discussed in relation to dangerous and deprived environments (e.g., Del Giudice, 2018; Jonason et al., 2016), are related to each aspect of dichotomous thinking. Second, some findings have shown the relationship between these traits and harsh environments: neighborhood disadvantage and aggression (Vazsonyi et al., 2006), economic recession and narcissism (Bianchi, 2014, 2015), high-crime environment and externalizing behavior (Doom et al., 2016), and neighborhood socioeconomic status and personality disorders, including BPD (Walsh et al., 2013). Thus, one of the reasons dichotomous thinking is common among certain personalities may be understood by the harshness of the environment.

The crime index of the environment used in this study is a marker of environmental harshness. High-crime environments lack social cohesion among residents to supervise and monitor children (Sampson et al., 1997). Such residents are exposed to multiple risk factors daily, which can be chronic or acute stressors (e.g., Baum et al., 1981; Bauman et al., 2006; Brody et al., 2013; Evans et al., 2013). Considering this, the evolutionary model posits that chronic stress shapes children's lower inhibitory control and greater orientation toward immediate rewards to adapt to their environment (Mittal & Griskevicius, 2014; Mittal et al., 2015; Sturge-Apple et al., 2016; Suor et al., 2017). In other words, growing up in a high-crime environment may affect such people's dichotomous thinking style, especially profit-and-loss thinking.

Distinguishing between current and childhood high-crime environments is useful to unravel the relationship between individual differences in dichotomous thinking and those environments. Based on the evolutionary model (Mittal & Griskevicius, 2014; Mittal et al., 2015; Sturge-Apple et al., 2016; Suor et al., 2017) described above, people raised in high-crime environments may be prone to dichotomous thinking. Nevertheless, given the research on the relationship between personality and environmental severity (e.g., Bianchi, 2014, 2015; Doom et al., 2016; Vazsonyi et al., 2006), it is not clear how people's current high-crime environment is related to their thinking styles. We, therefore, examined the relationship between current and childhood environments and individual differences in dichotomous thinking.

In summary, dichotomous thinking would be beneficial in harsh environments because judgments based on it are quicker and take up fewer resources. Thus, people engaging in it may be more likely to be in high-crime environments. Considering the findings of personality development based on the evolutionary model, the present study examined the relationship between individual differences in dichotomous thinking and current and childhood high-crime environments to test the following hypothesis: individual differences in dichotomous thinking are associated with the childhood high-crime environment, rather than the current high-crime environment. In this case, there may be a developmental mismatch between the way people perceive things based on how they adapt to the two environments if the level of harshness of their current environment is different from that of their childhood. Research suggests that developmental mismatches between traits developed based on adaptation to the childhood environment and behaviors appropriate to the current environment are detrimental to people's social activities and mental health (e.g., Bateson et al., 2004; Cosmides & Tooby, 1999; Del Giudice, 2018; Frankenhuis & Del Giudice, 2012). Therefore, it is important to understand this relationship to improve people's social order and mental health.

Materials and Methods

Participants and Procedures

We used the data from the Human Information Database FY19 compiled by NTT DATA Institute of Management Consulting, Inc. from November to December 2019. The participants in this survey were 51,024 Japanese people between the ages of 15 and 92 from all prefectures of Japan. Participants born after 1958, who were 15 in 1973, were included in the analysis because the available crime rates by prefecture were after 1973 in the survey. The total number of participants in this analysis was 41,284 (19,840 men and 21,444 women; mean age = 40.99 years; SD = 11.99 years). All participants provided informed consent prior to participation. They responded to a questionnaire including the scale of dichotomous thinking, demographic variables, their current area of residence, and the area where they had spent the most time by the time they were 15 years old.

Variables

Dichotomous Thinking

Individual differences in dichotomous thinking were assessed using the Dichotomous Thinking Inventory (Oshio, 2009), which consists of three subscales: preference for dichotomy (Cronbach's α = .81), dichotomous belief (α = .86), and profit-and-loss thinking (α = .85). Items were rated on a 6-point Likert scale (1 = strongly disagree to 6 = strongly agree) and were averaged to create an index of individual differences in all three subscales.

Crime Index

We obtained five crime rates at prefecture-level in Japan from 1973 to 2019 from a white paper on Japanese Police Agency [Retrieved from https://www.npa.go.jp/publications/whitepaper/index_keisatsu.html]. Each rate refers to the number of crimes per 100,000 people per year divided by the population of each area. The five types of crime examined are as follows: felony, which covers murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault; violent offenses, which cover unlawful assembly with dangerous weapons assault, bodily injury, bodily injury resulting in death, intimidation, and extortion; larceny/theft, which covers burglary, vehicle theft, and non-burglary; white-collar offenses of fraud, embezzlement, counterfeiting, illegal proceeds from mediation, and breach of trust; and moral offenses, which cover gambling, indecent assault, and public indecency.

We used the crime indices for participants’ current and childhood environments. The crime index for the current was based on the crime rates in 2019 because the participants took part in this survey at that time. The crime index for childhood was based on the crime rates of the region and the year in which they had spent the most time until the age of 15; for example, in case they were 30 years old in 2019 and selected Tokyo as the prefecture where they spent their childhood, their crime index referred to the crime rates in Tokyo in 2004. Some participants’ residences in 2019 differed from the residences in their childhood.

We created the crime indices for current and childhood using a principal component analysis to integrate information on the five crime rates (felony, violent offenses, larceny/theft, white-collar offenses, and moral offenses). The crime index for the current was calculated on 47 data, which were the number of Japanese prefectures. The crime index for childhood was calculated on 2,153 data. We used the first principal component as both crime indices. The first principal component accounted for 48% of the variance for the childhood crime index, with an eigenvalue of 2.42. It accounted for 63% of the variance for the current crime index, with an eigenvalue of 3.15.

Confounding Variables

The present study used participants’ age as a confounding variable because the variance in childhood crime index is presumably influenced by the era.

Results

Correlation Analysis

The descriptive statistics, correlation coefficients, and sex differences for all variables are shown in Table 1. Childhood crime index had a positive correlation with preference for dichotomy for men (r = .015, p < .05) and a negative correlation with dichotomous belief for women (r = −.016, p < .05). Current crime index was related to profit-and-loss thinking for woman (r = −.015, p < .05). Age was negatively associated with dichotomous thinking (rs = −.133 – −.079, ps < .001), positively associated with childhood crime index (rs = .059 – .104, ps < .001) and with current crime index (rs = .014 – .040, ps < .05).

Table 1.

Descriptive Statistics, Correlation Coefficients, and Sex Differences for All Variables.

Men
(n = 19,836)		 Women
(n = 21,448)
Mean SD Mean SD Welch's t d 1 2 3 4 5 6
1. Preference for dichotomy 3.38 0.79 3.36 0.78 2.83** .028 .627 *** .777 *** .012 −.010 −.130 ***　
2. Dichotomous belief 3.14 0.89 2.96 0.87 20.45*** .202 .662 *** .516 *** −.016 * −.015 * −.079 ***
3. Profit-and-loss thinking 3.61 0.86 3.64 0.85 2.66** .026 .789 *** .555 *** .009 −.003 −.133 ***
4. Crime index (childhood) 0.60 1.26 0.60 1.27 0.12 .001 .015 * −.003 .011 .409 *** .104 ***
5. Crime index (current) 0.93 1.15 0.92 1.17 0.84 .008 .010 .001 .004 .420 *** .040 ***
6. Age 41.54 11.72 40.48 12.22 9.00*** .088 −.111 *** −.120 *** −.111 *** .059 *** .014 *　 　
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Note. The zero-order correlation coefficient for men is shown in the lower triangle; for women, it is shown in the upper triangle. ***

* p < .05, ** p < .01, *** p < .001.

Fixed Effects Models

We examined the intraclass correlations (ICC) of the dichotomous thinking at the current prefecture level and at the prefecture in childhood level. ICC coefficients at the current prefecture level were −.001 (95%CI [−.001, .000]), .001 (95%CI [.000, .003]), and −.001 (95%CI [−.001, .000]) for men, and .000 (95%CI [−.001, .002]), .001 (95%CI [.000, .003]), and .001 (95%CI [.000, .002]) for women, for preference for dichotomy, dichotomous belief, and profit-and-loss thinking, respectively. ICC coefficients at the prefecture in childhood level were .001 (95%CI [.000, .002]), .000 (95%CI [.000, .002]), and .000 (95%CI [−.001, .002]) for men, and .000 (95%CI [−.001, .001]), .000 (95%CI [−.001, .001]), and .001 (95%CI [.000, .003]) for women, for preference for dichotomy, dichotomous belief, and profit-and-loss thinking, respectively. The scores of dichotomous thinking were not explained at both levels, given the quite small ICC.

According to the findings of personality development based on the evolutionary model, we examined the effect of childhood environments. The analysis in this study adopted the fixed effects models (FEM: Allison, 2009; McNeish & Kelley, 2019). The FEM omits the variance at the cluster level and only estimates coefficients at the individual level. We tested the FEM using a de-meaning method, in which mean scores at the prefecture in childhood level are subtracted from observed values for all variables for each sex group. The regression analyses using these variables were conducted with cluster robust standard errors.

 Table 2 displays the coefficients of the results. There were significant positive associations between childhood crime index and dichotomous thinking; that is, preference for dichotomy and profit-and-loss thinking for both men and women.

Table 2.

The Results of Fixed Effects Regression Model Predicting Each Score of the Dichotomous Thinking.

Men
PD DB PT
B   Robust SE B   Robust SE B   Robust SE
Age −.008 *** .000 −.009 *** .001 −.008 *** .000
Childhood crime index .017 * .006 −.001 .005 .013 * .006
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Note. PD = Preference for dichotomy, DB = Dichotomous belief, PT = Profit-and-loss thinking.

All variables were centered on the cluster mean at the prefecture in childhood level.

B = regression coefficient

*p < .05, **p < .01, ***p < .001.

The multiple regression analyses were conducted using the current crime index with controlling for age. The current crime index was not significantly associated with any scores of the dichotomous thinking for both men and women (see Table S1). Furthermore, the results for the childhood crime index remained when we conducted the FEM model controlling for the current crime index (see Table S2).

Discussion

We perceive things around us to deal with the complexity of our environment (Macrae & Bodenhausen, 2000; Simpson, 1961). Dichotomous thinking—that is, the tendency to simplify the process of understanding things—may be useful in harsh environments. This is because in environments where resources are limited and people are more likely to be exposed to violence and crime, it may be beneficial to conserve resources and act quickly (Haselton et al., 2005). However, when the traits that helped people adapt to their childhood environment are different from those that are appropriate for their current environment, the former become disadvantageous (e.g., Bateson et al., 2004; Cosmides & Tooby, 1999; Del Giudice, 2018; Frankenhuis & Del Giudice, 2012). Therefore, it is important to clarify the relationship between individual differences in dichotomous thinking and environmental harshness to facilitate improvement in social order and public health. In this study, we used crime as a marker of environmental harshness. People living in high-crime environments are exposed to chronic stress (Baum et al., 1981; Bauman et al., 2006; Brody et al., 2013; Evans et al., 2013). Based on the evolutionary developmental model described earlier, growing up exposed to chronic stress shapes certain cognitive styles in people (Mittal & Griskevicius, 2014; Mittal et al., 2015; Sturge-Apple et al., 2016; Suor et al., 2017). In light of these findings, we distinguished between current and childhood environments to investigate the above-mentioned relationship. We hypothesized that the childhood high-crime environment would be associated with individual differences in dichotomous thinking, and our results suggest that this hypothesis may be true.

Individual differences in dichotomous thinking were found to be somewhat related to the childhood high-crime environment after controlling for age. The crime index of the environment in which people were born and raised was positively related to individual differences in dichotomous thinking, except for dichotomous belief, after controlling for age. On the other hand, the current high-crime environment was not associated with individual differences in dichotomous thinking. This result is consistent with the findings of developmental studies of cognitive preferences based on evolutionary models (Mittal & Griskevicius, 2014; Mittal et al., 2015; Sturge-Apple et al., 2016; Suor et al., 2017). In other words, although the present study did not measure childhood stress, the stress induced by growing up in a high-crime environment likely contributed to individual differences in dichotomous thinking because the current high-crime environment had no effect. The tendency to think dichotomously as a form of adaptation to the environment may be triggered by stress in childhood. In addition, the influence of crime in the childhood environment was partially different depending on the aspects of dichotomous thinking—the reason for this is not clear at present. However, the results of the profit-and-loss thinking are consistent with the finding that the tendency to seek immediate rewards is formed in children from harsh environments (Mittal & Griskevicius, 2014; Mittal et al., 2015; Sturge-Apple et al., 2016; Suor et al., 2017).

Dichotomous thinking has been found to be common among people with specific personality traits (Beck et al., 2004; Jonason et al., 2018; Oshio, 2009, 2012; Oshio et al., 2016; Veen & Arntz, 2000) that have been discussed in relation to harsh environments (e.g., Del Giudice, 2018; Jonason et al., 2016). This may be due in part to the fact that people having those personality traits who engage in dichotomous thinking were raised in harsh environments, as indicated by the finding of a positive association between individual differences in dichotomous thinking and childhood high-crime environment.

In conclusion, these differences may be partly attributable to the high-crime environment people spent their childhood in. Adopting the evolutionary developmental model perspective, dichotomous thinking may develop in harsh environments, and the daily stresses and dangers of living in a high-crime environment during childhood may be one of the triggers for its development. However, individual differences in personality development are explained by genetics and a variety of potentially triggering environmental factors, and the magnitude of partial environmental effects on personality development is limited (e.g., Harris, 1998). Indeed, the very small effect sizes found in the present study suggest that the impact of these environmental changes on dichotomous thinking is trivial. In addition, the indicators of high crime environment used in this study include various social disadvantages, such as poverty and lack of social networks (e.g., Baum et al., 1981; Bauman et al., 2006; Brody et al., 2013; Evans et al., 2013). We were unable to obtain these indicators in this study; therefore, the relationship between these indicators and individual differences in dichotomous thinking is unclear. To make our findings more robust, we will need to consider a variety of environmental factors in the future.

Research based on a socio-ecological view can provide insights into the relationship between individual differences and contextual-level variables (Freedman & Woods, 2013). However, the effect sizes of the context-level variables are small (e.g., Bianchi, 2014, 2015; Freedman & Woods, 2013), and the effect sizes found in this study were also small. If people's childhood and current environments are different, the traits they develop to cope with the harshness of the former may be disadvantageous in the latter (e.g., Bateson et al., 2004; Cosmides & Tooby, 1999; Del Giudice, 2018). In other words, dichotomous thinking tendency adapted to the harsh environment of childhood may bring problems for people in their current environment. From a public health perspective, changes in the environment that reduce the tendency toward dichotomous thinking, which is associated with many socially undesirable personality traits (Beck et al., 2004; Jonason et al., 2018; Oshio, 2009, 2012; Oshio et al., 2016; Veen & Arntz, 2000), would be of interest. However, given that only high-crime environments of childhood are associated with individual differences in dichotomous thinking, it may take a decade or more for environmental changes to reflect in these differences. Despite the limitations in testing for causality, this study provides evidence that high crime rates during childhood are positively associated with dichotomous thinking, but current crime rates are not; this indicates that dichotomous thinking may be related to developmental mismatches in current and childhood environments.

Acknowledgements

We are grateful to NTT DATA Institute of Management Consulting, Inc. for sharing its dataset for this study.

We would also like to extend our gratitude to the Associate Editor for the guidance and support throughout the review process.

Footnotes

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by JSPS KAKENHI Grant Number JP20K03345.

ORCID iD: Takahiro Mieda https://orcid.org/0000-0001-8611-7522

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