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Abstract

Environmental sensitivity is a meta-concept that describes individual differences in susceptibility to both positive and negative environmental influences and has been repeatedly reported to correlate with other established personality traits, including the Big Five. The purpose of this study was to examine the correlation between the general factor of environmental sensitivity (GFS) and the general factor of personality (GFP). A total of 1,046 adult participants (52% female; M_age= 45.15, SD_age= 12.70) completed a self-report psychological questionnaire on an online form. Confirmatory factor analysis indicated that GFS had a strong negative correlation with GFP (r = −.41, 95% CI [−.52, −.30], p < .001). Focusing on the relationship with the Big Five, individuals with higher environmental sensitivity were emotionally unstable and introverted. The trait of environmental sensitivity may be described not only in relation to the Big Five but also in relation to GFP, which is assumed to be an indicator of social effectiveness.

Keywords

environmental sensitivity sensory processing sensitivity general factor of environmental sensitivity GFS Big Five general factor of personality GFP

Introduction

Traditionally, several constructs related to psychological individual differences are known to be aggregated into a single higher-order factor, such as the general intelligence factor, g, which explains the common variance of specific cognitive intelligences, including spatial, verbal, and numerical abilities in humans (Spearman, 1904). More recently, in addition to the general factor of psychopathology, the p-factor (Caspi et al., 2014), the general factor of personality, GFP (Figueredo et al., 2004; Musek, 2007), on which this study focuses and the general factor of environmental sensitivity, which we call GFS (Lionetti et al., 2018; Pluess et al., 2023) have been proposed. Twin studies suggest that the general factors involved in these individual differences are heritable (Assary et al., 2021; Haworth et al., 2010; Rushton et al., 2008; Veselka et al., 2009). Currently, there is a trend to attempt to understand individual differences in cognitive ability, personality, and psychopathology by examining associations among these general factors, but to the best of our knowledge, there are no reports of correlations between GFS and GFP. Therefore, in this study, we attempted to report for the first time the correlation between GFS and GFP.

The Genral Factor of Environmental Sensitivity (GFS)

Human neurophysiological or psychosocial development is influenced by a wide range of environments, including the quality of parenting and interpersonal relationships, but the degree of susceptibility can vary depending on individual differences in genetic (Assary et al., 2021), neurophysiological (Weyn et al., 2022a), and temperament/personality (Slagt et al., 2018) factors. Environmental sensitivity is a meta-concept that explains such individual differences in susceptibility to environmental influences, which is defined as a continuous trait describing individual differences in perception and processing of environmental stimuli in both adversarial and supportive environments (Greven et al., 2019). Individuals with higher degrees of environmental sensitivity are more likely than those with lower degrees to be negatively affected by an adversarial environment (e.g., higher depressive symptoms and problem behaviors) and positively influenced by a supportive environment (e.g., lower depressive symptoms and problem behaviors) (Belsky & Pluess, 2009). According to evolutionary neurodevelopmental psychology theory, this variation in environmental sensitivity is assumed to have evolved as a result of a “bet-hedging strategy” (Ellis et al., 2011). Specifically, because of the unpredictability of the quality of the environment in which offspring lived during the era of the ancestors, it is assumed that natural selection favored a reproductive strategy that retained both individuals that responded stably to their environment and individuals that were susceptible to change.

Recently, evidence has accumulated that a temperament/personality trait called sensory processing sensitivity (SPS) is a concept that corresponds to individual differences in environmental sensitivity (Aron et al., 2012; Hartman et al., 2023). This construct, based on an evolutionary biological basis, explains individual differences in a continuum of traits, such as being alert to novel stimuli by “pause to check,” being easily overwhelmed by stimuli, and being more aware of subtle changes in the environment (Greven et al., 2019). The factor structure of the SPS as measured by the highly sensitive person (HSP) scale, self-report psychological measure, was originally envisioned as a one-factor model (Aron & Aron, 1997), but subsequent research proposed a three-factor model including the ease of excitation, low sensory threshold, and aesthetic sensitivity subscales, which has been supported in various translated versions of the scale, including Dutch (Weyn et al., 2021), Chinese (Liu et al., 2023), and Japanese (Iimura & Kibe, 2020). More recently, evidence has accumulated that a bifactor model that includes these three factors plus a general factor that explains all items has a good fit with the data (Lionetti et al., 2018). We refer to this general factor as GFS, as do existing general factors such as the g-factor, the p-factor, and GFP. Interestingly, the existence of this GFS has been confirmed by data from both adults and children from multiple countries in the West and East (Iimura & Kibe, 2020; Iimura et al., 2023; Pluess et al., 2018, 2023; Weyn et al., 2021, 2022b).

The Genaral Factor of Personality (GFP)

Similar to the GFS described above, it is known that each factor in the Big Five model describing individual differences in personality can be aggregated to one higher-order factor (Musek, 2007). In general, GFP is conceptualized as a higher-order factor, or Big One, of two meta-traits (i.e., Big Two): Alpha (or Stability), which describes Agreeableness, Conscientiousness and Neuroticism, and Beta (or Plasticity), which describes Openness and Extraversion (DeYoung et al., 2002; Digman, 1997). Evidence obtained by meta-analysis suggests that models that include the GFP have a better fit than models consisting only of Alpha and Beta (van der Linden et al., 2017). Based on the Big Five, people with high GFP are described as extroverted, conscientious, open-minded, compassionate, and emotionally stable. The existence of GFP has been confirmed by data from various countries and different age groups in the West and East (Dunkel et al., 2021; Kawamoto et al., 2021; van der Linden et al., 2017; Wu et al., 2021).

However, the interpretation of GFP remains an ongoing controversy. On one side, critics argue that GFP merely represents a social desirability or statistical or methodological artifact (Bäckström et al., 2009; Hawes et al., 2023; Revelle & Wilt, 2013). In addition, factor analysis of variables that are positively correlated with each other, whether the p-factor or GFS, necessarily supports the general factor model mathematically, but it does not necessarily support the existence of a substantial underlying factor (van Bork et al., 2017).

On the other hand, researchers have pointed out that even when controlling for social desirability bias, GFP is a construct that holds more psychological meaning than that (Dunkel & van der Linden, 2014). It is important to note here that both positions are persuasive in their own right and need not necessarily be mutually exclusive (Pelt et al., 2020). Focusing on the latter position, a number of studies have accumulated findings that GFP correlates with individual differences in aspects of social effectiveness, such as ability, intelligence, and emotion. For example, an early study by Musek (2007) reported a strong positive correlation between GFP and positive emotionality, life satisfaction, and self-esteem. Subsequent studies, which continue to date, have reported positive correlations with emotional intelligence (van der Linden et al., 2017), trait resilience (Dunkel et al., 2021), leadership as rated by others (Wu et al., 2021), subjective interpersonal quality (Pelt et al., 2020), and monthly income (van der Linden et al., 2023a). GFP has also been shown to be negatively correlated with emotional and behavioral problems (Kawamoto et al., 2021), the p-factor (Etkin et al., 2020; van der Linden et al., 2023b), and Machiavellianism and psychopathy among Dark Triad models (Kowalski et al., 2016). In addition, GFP may be a continuous trait influenced by natural selection (Rushton et al., 2008), and more recently, it has been suggested that high GFP levels are a more female-typical trait in an evolutionary context (Kanazawa, 2024).

Are GFS Associated with GFP?

The association between GFS and GFP has not yet been examined. However, evidence is accumulating on correlations between these subfactors. Although the findings are based mainly on data obtained from people in Western countries, a meta-analysis by Lionetti et al. (2019) reported that higher environmental sensitivity as measured by the HSP scale was positively correlated with Neuroticism and Openness in adults and with Neuroticism in children. Not included in this meta-analysis, a recent study conducted in Japan reported that environmental sensitivity is positively correlated with Neuroticism and negatively correlated with Extraversion and Agreeableness and Conscientiousness in Japanese adults (Iimura et al., 2023; Yano et al., 2021). Based on the correlation coefficients obtained, some critics argue that the environmental sensitivity is an indistinguishable construct from the Big Five (Hellwig & Roth, 2021), while others argue that the two are distinguishable (Lionetti et al., 2024). Despite this controversy, environmental sensitivity has shown relatively robust correlations with several of the Big Five factors. These findings motivate us to examine the question of this study: Is there a correlation between GFS and GFP?

The Current Study

The purpose of this preregistered study was to investigate the correlation between GFS and GFP. For this purpose, we conducted an online survey and collected data from 1,046 Japanese adults aged 20–69 years. As reviewed previously, no findings have examined the association between GFS and GFP, so we did not hypothesize a priori about the direction or size of the correlation between the two. We examined the correlation between the two in an exploratory manner using confirmatory factor analysis. In our analysis, the GFS was composed of three different models (i.e., a bifactor model, a hierarchical model, and a one-factor model, respectively), and the correlation coefficients with GFP, composed as higher-order factors in alpha and beta, respectively, were calculated. The correlation between GFS and GFP was interpreted based on estimates from the best-fitting model.

Method

Procedure and Participants

A total of 1,142 Japanese adults (50% female) participated in the study. Attention checks using the Directed Questions scale (Maniaci & Rogge, 2014) revealed that 96 (8.4%) of the participants inappropriately responded to the item “Please select option of this item strongly disagree,” so these samples were excluded from the dataset for the analysis. Finally, data from 1,046 participants (52% female, M_age= 45.15, SD_age= 12.70, MIN_age= 20, MAX_age= 69) were used in the analysis. Of the participants, 72.5% were married, 72.7% had at least one child, and the range of annual household income with the highest percentage was 4–6 million yen (approximately 26,666–40,000 USD; 18.8%).

This study has been approved by the ethics review committee of Soka University (Approval No. 2023042). In addition, the protocol for this study has been preregistered with the Open Science Framework (OSF; https://doi.org/10.17605/OSF.IO/THSVM). We recruited Japanese adults from survey panelists registered with the market research firm Macromill, Inc. Informed consent was obtained via an online questionnaire from participants who expressed interest in our study. After the survey was completed, the market research company gave participants points that could be exchanged for cash.

Measures

To measure environmental sensitivity as a personality trait, we utilized the 10-item Japanese version of the HSP scale (Iimura et al., 2023). This scale consists of three subscales: ease of excitation (5 items, e.g., Do changes in your life shake you up?), low sensory threshold (3 items, e.g., Are you bothered by intense stimuli, like loud noises or chaotic scenes?), and aesthetic sensitivity (2 items, e.g., Do you notice and enjoy delicate or fine scents, tastes, sounds, works of art?). Ease of excitation and low sensory threshold correspond to sensitivity to negative environments, while aesthetic sensitivity relates to sensitivity to positive environments. Each item was rated on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). Internal consistency showed Cronbach's α = .88 (McDonald's ω total = .92) for all 10 items, α = .88 (ω total = .88) for ease of excitation, α = .85 (ω total = .85) for low sensory threshold, and α = .71 (ω total = .73) for aesthetic sensitivity. Confirmatory factor analysis revealed that in the three subfactor model, each goodness-of-fit index was .969 for comparative fit index (CFI), .957 for Tucker–Lewis index (TLI), .068 for root mean square error of approximation (RMSEA), and .038 for standardized root mean square (SRMR) (See next section for interpretation of the goodness-of-fit indices). The goodness of fit of the model composed of higher-order factors explaining the three subfactors also showed the same values as the three-factor model. The bifactor model (see also next section for details) had a slightly better fit than the three-factor and higher-order factor models, with .986 for CFI, .975 for TLI, .052 for RMSEA, and .021 for SRMR.

The Japanese version of the Big Five Inventory-2 (Yoshino et al., 2022) was used to measure Big Five personality traits. This scale consists of 60 items, including Extraversion (12 items, e.g., Is outgoing, sociable), Agreeableness (12 items, e.g., Is compassionate, has a soft heart), Conscientiousness (12 items, e.g., Is systematic, likes to keep things in order), Neuroticism (12 items, e.g., Is moody, has up and down mood swings), and Openness (12 items, e.g., Is curious about many different things), respectively. Participants rated each item on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Internal consistency showed α = .86 (ω total = .86) for Extraversion, α = .78 (ω total = .78) for Agreeableness, α = .83 (ω total = .83) for Conscientiousness, α = .87 (ω total = .87) for Neuroticism, and α = .79 (ω total = .79) for Openness.

Data Analysis

First, summary statistics (e.g., mean and standard deviation) for each variable and correlation coefficients between variables were calculated to describe the characteristics of the data in this study. Next, we analyzed the following three models (Figure 1), referring to the method examined in van der Linden et al. (2017) to examine the association between GFS and GFP. The first is a bifactor model (Figure 1A; see Rodriguez et al., 2016 for specific methodology). In this model, GFS is modeled in a bifactor structure, consisting of a general factor explaining shared variance among all the items plus a set of group factors explaining variance in excess of the variance shared by the general factor (i.e., ease of excitation, low sensory threshold, and aesthetic sensitivity). The second is a hierarchical model (Figure 1B). In this model, GFS is modeled as a higher-order factor that explains three subfactors. The third is a one-factor model (Figure 1C). In this model, GFS was modeled as a single factor explaining a single observed scale score. In each model, GFP was represented as a higher-order factor with an Alpha factor explaining Agreeableness, Conscientiousness and Neuroticism, and a Beta factor explaining Openness and Extraversion, as supported by a meta-analysis by van der Linden et al. (2017).

Figure 1.

Three models for examining the relationship between the GFS and the GFP. Note. GFP = the general factor of personality, GFS = the general factor of environmental sensitivity, EOE = ease of excitation, LST = low sensory threshold, AES = aesthetic sensitivity.

The goodness of fit of each model to the data was evaluated based on CFI, TLI, RMSEA, and SRMR. CFI and TLI are above .90 and RMSEA and SRMR are below .08, suggesting a good fit (Hu & Bentler, 1999; Schermelleh-Engel et al., 2003). We interpreted the correlations between GFS and GFP based on the model with a good fit. Although various interpretations of correlation coefficients exist, we regarded r = .10 as small, .20 as moderate, and .30 as large effect sizes (Gignac & Szodorai, 2016). The dataset used for analysis did not contain missing values. The significance level (alpha) was set at 5% for this study. Additionally, the sample size of this study provided sufficient statistical power (= .90) to detect small effect sizes (r = .10). Data analysis was conducted using R version 4.2.2 and its interface, R Studio version 2023.06.1. The analysis script has been uploaded to OSF (https://x.gd/y95yr).

Results

Preliminary Analysis

Histograms and frequency distributions for each variable are available in Supplemental Figures 1–10 uploaded to OSF (https://x.gd/y95yr). Summary statistics for each variable are presented in Table 1. As depicted in Supplemental Table 1, females exhibited higher means than males for environmental sensitivity, ease of excitation, low sensory threshold, aesthetic sensitivity, Agreeableness, and Neuroticism (t(1044) = 3.27 to 6.11, Cohen's d = 0.20 to 0.38, p ≤ .001). Males reported a higher mean for Extraversion than females (t(1044) = 3.27, d = 0.20, p = .001).

Table 1.

Descriptive Statistics (N = 1,046)

	M	SD	MIN	MAX	Skewness	Kurtosis
Environmental sensitivity	4.18	1.04	1.00	6.90	−0.35	0.25
Ease of excitation	4.15	1.24	1.00	7.00	−0.33	−0.01
Low sensory threshold	4.22	1.36	1.00	7.00	−0.32	−0.33
Aesthetic sensitivity	4.19	1.25	1.00	7.00	−0.26	0.12
Agreeableness	3.32	0.49	1.17	5.00	−0.04	1.01
Conscientiousness	3.15	0.57	1.00	5.00	−0.06	0.82
Neuroticism	3.01	0.64	1.17	4.92	0.29	0.24
Openness	3.04	0.54	1.08	4.92	0.13	0.46
Extraversion	2.78	0.64	1.00	4.75	−0.07	−0.03

Table 2 displays the bivariate correlation coefficients. Environmental Sensitivity exhibited a weak negative correlation with Conscientiousness (r = −.14, 95% CI [−.20, −.08], p < .001), a strong positive correlation with Neuroticism (r = .50, 95% CI [.45, .55], p < .001), and a strong negative correlation with Extraversion (r = −.31, 95% CI [−.36, −.25], p < .001).

Table 2.

Correlation Coefficients (N = 1,046)

	1	2	3	4	5	6	7	8	9	10
1. Gender (male = 1, female = 2)	—
2. Age	−.04	—
3. Environmental sensitivity	.17**	−.14**	—
4. Ease of excitation	.19**	−.20**	.91**	—
5. Low sensory threshold	.12**	−.04	.84**	.63**	—
6. Aesthetic sensitivity	.04	−.02	.54**	.30**	.30**	—
7. Agreeableness	.10**	.10**	−.03	−.08**	−.07*	.19**	—
8. Conscientiousness	−.02	.19**	−.14**	−.27**	−.05	.14**	.41**	—
9. Neuroticism	.10**	−.24**	.50**	.53**	.40**	.11**	−.37**	−.35**	—
10. Openness	−.04	.07*	.06	−.09**	−.05	.53**	.32**	.35**	−.15**	—
11. Extraversion	−.10**	.06	−.31**	−.37**	−29**	.10**	.22**	.27**	−.35**	.37**

Note. *p < .05. **p < .01.

Associations Between GFS and GFP

Confirmatory factor analysis revealed that among the three models, Model 1 (bifactor model) had the best fit to the data, while Model 2 (hierarchical model) and Model 3 (one-factor model) had significantly lower fit.

As depicted in Figure 2, Model 1 exhibited a strong negative correlation between GFS and GFP (r = −.41, 95% CI [−.52, −.30], p < .001). The goodness of fit for the model was .940 for CFI, .915 for TLI, and .087 for both RMSEA and SRMR. Although RMSEA and SRMR were slightly higher, overall, the fit between the data and model was good.

Figure 2.

Correlations between the GFS and the GFP (Bifactor Model). Note. GFP = the general factor of personality, GFS = the general factor of environmental sensitivity, EOE = ease of excitation, LST = low sensory threshold, AES = aesthetic sensitivity. We confirmed that the same analysis with reversal code for Neuroticism yielded the same goodness-of-fit index.

Model 2 (see Supplemental Figure 1 for details) yielded a very strong negative correlation coefficient between GFS and GFP (r = −.73, 95% CI [−.80, −.66], p < .001). However, the goodness of fit for the model was .843 for CFI, .804 for TLI, .112 for RMSEA, and .107 for SRMR, suggesting that Model 2 did not fit the data.

For Model 3, no correlation coefficient between the general factors was obtained due to the lack of convergence of the solutions. More details on other estimates of these models can be found in OSF (https://x.gd/y95yr).

Discussion

The aim of this study was to examine the correlation between the GFS and the GFP. We analyzed data from 1,046 Japanese adults ranging in age from 20 to 69 years and found a strong negative correlation between GFS and GFP. This means that, overall, adults with higher environmental sensitivity tended to report lower GFP scores, characterized by more introversion, emotional instability, less industriousness, less openness, and less agreeableness. Consequently, we have provided new opportunities to describe environmental sensitivity in relation to GFP.

Focusing on the correlation with the Big Five, similar to the findings of a meta-analysis based on Western adult samples (Lionetti et al., 2019), higher environmental sensitivity in Japanese adults was strongly and positively correlated with Neuroticism. Meanwhile, in contrast to the existing meta-analysis, which reported no correlation between environmental sensitivity and extraversion, Japanese adults with high environmental sensitivity were more introverted. Our findings are consistent with several previous studies analyzing Japanese adult data (Iimura et al., 2023; Yano et al., 2021) and seem to indicate relatively robust evidence that environmental sensitivity in Japanese adults is characterized by low levels of both Emotional Stability and Extraversion. Such discrepancies across regions and/or cultures are not surprising, as previous research has noted variations in the associations among personality traits across different cultural contexts (Schmitt et al., 2007). Data collected from 56 regions and/or countries indicated that East Asians, including Japanese, exhibited relatively higher levels of introversion and emotional instability (Schmitt et al., 2007). Consequently, the association between susceptibility, which correlates strongly with emotional instability, and introversion may appear more pronounced among East Asians. These cultural and regional differences may ultimately influence the relationship between GFS and GFP, as well as the relationship between their respective subfactors. However, there are currently no studies directly examining these potential effects, and the mechanisms underlying the co-evolution of culture, GFS, and GFP remain largely unexplored. Addressing this gap is an important task for future research in this field.

Given both the existing evidence that GFP is negatively correlated with the p-factor (Etkin et al., 2020; van der Linden et al., 2023) and that environmental sensitivity is positively correlated with psychopathology (Greven et al., 2019), it is reasonable to expect a negative correlation between GFS and GFP. Additionally, if GFP represents social effectiveness, including high emotional intelligence and trait resilience (Dunkel et al., 2021; van der Linden et al., 2017), GFS may be negatively associated with variables reflecting social effectiveness at the bivariate correlation level. However, in light of the various theories of evolutionary developmental psychology (Belsky & Pluess, 2009; Ellis et al., 2011), it is important to consider the goodness of fit with the environment in order to properly understand the relationship between environmental sensitivity and adjustment. Furthermore, if a high level of GFP indicates a slow life history strategy (Kanazawa, 2024; Rushton et al., 2008), GFS negatively correlated with GFP may be an indicator of fast life history strategy. However, it would be undesirable to oversimplify this speculation: In addition to the view that GFP is associated with both ends of the life history strategy continuum (Del Giudice, 2018), some individuals with high environmental sensitivity are more susceptible to supportive environments (Pluess & Belsky, 2013), which may reflect slow life history strategies.

One conceivable scenario, in terms of a “bet-hedging strategy” (Ellis et al., 2011), is as follows: The quality of the environment experienced during childhood emerges as one of the most promising mediating variables for explaining the association between GFS and GFP. In a supportive environment, individuals with high GFS may exhibit higher GFP due to their adoption of slow life history strategies. Conversely, those with high GFS exposed to harsh environments are likely to develop lower GFP, opting for faster life history strategies. Moreover, individuals with low GFS are less susceptible to the influences of both positive and negative environments. In the light of such a scenario, the observed negative correlation between GFS and GFP in this study could be interpreted as a consequence of the fact that a relative majority of Japanese participants were particularly vulnerable to adverse childhood contexts. If the dataset included an equal number of participants who experienced positive and adverse childhood environments, it is plausible that the association between GFS and GFP would be uncorrelated. To gain a deeper understanding of the association between GFS and GFP, future studies would benefit from including measures of childhood environment quality.

Finally, we note some important limitations and additional future challenges in this study. First, and importantly, this study did not find a biological basis underlying the association between GFS and GFP. Therefore, we cannot rule out the possibility that it is merely an association between statistical artifacts (van Bork et al., 2017). Given the existing controversy, as this study relied on self-reported data, it would be beneficial for future research to explore the relationship between GFS and GFP while considering measures of social desirability bias. Second, because this is the first study to examine the relationship between GFS and GFP, it is currently unable to provide specific discussion. In order to discuss GFS in relation to social effectiveness and life history strategies, future studies need to directly examine the correlation between GFS and these factors. Third, although existing studies suggest that both GFS and GFP have relatively stable factor structures regardless of ethnicity, there may be cultural and/or regional differences in their association. Future examination of the correlation between GFS and GFP based on a large, culturally diverse sample would be beneficial for understanding the association between the two. Additionally, it would also be useful to examine using samples from childhood to adolescence.

Supplemental Material

sj-docx-1-evp-10.1177_14747049241254727 - Supplemental material for The General Factor of Environmental Sensitivity: Relationships with the General Factor of Personality

Supplemental material, sj-docx-1-evp-10.1177_14747049241254727 for The General Factor of Environmental Sensitivity: Relationships with the General Factor of Personality by Shuhei Iimura and Kosuke Yano in Evolutionary Psychology

Footnotes

Author Contributions

Shuhei Iimura contributed to conceptualization, investigation, data analysis, and writing—original draft preparation; Kosuke Yano contributed to investigation, data analysis, and writing—original draft preparation.

Data Availability

Supplementary materials were uploaded to the Open Science Framework (). Those who wish to make secondary use of the data should contact the corresponding author.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval

This study was approved by the Ethics Review Committee of Soka University (approval number: 2023042).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by JSPS KAKENHI to the first author (Grant Number 22K03049) and the second author (Grant Number 22K20329).

Informed Consent

Informed consent was obtained from all individual participants included in the study.

ORCID iD

Shuhei Iimura

Supplemental Material

Supplemental material for this article is available online.

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