A robust multivariate structure of interindividual covariation between psychosocial characteristics and arousal responses to visual narratives.

People experience the same event but do not feel the same way. Such individual differences in emotion response are believed to be far greater than those in any other mental functions. Thus, to understand what makes people individuals, it is important to identify the systematic structures of individual differences in emotion response and elucidate how such structures relate to what aspects of psychological characteristics. Reflecting this importance, many studies have attempted to relate emotions to psychological characteristics such as personality traits, psychosocial states, and pathological symptoms across individuals. However, systematic and global structures that govern the across-individual covariation between the domain of emotion responses and that of psychological characteristics have been rarely explored previously, which limits our understanding of the relationship between individual differences in emotion response and psychological characteristics. To overcome this limitation, we acquired high-dimensional data sets in both emotion-response (8 measures) and psychological-characteristic (68 measures) domains from the same pool of individuals (86 undergraduate or graduate students) and carried out the canonical correlation analysis in conjunction with the principal component analysis on those data sets. For each participant, the emotion-response measures were quantified by regressing affective-rating responses to visual narrative stimuli onto the across-participant average responses to those stimuli, while the psychological-characteristic measures were acquired from 19 different psychometric questionnaires grounded in personality, psychosocial-factor, and clinical-problem taxonomies. We found a single robust mode of population covariation, particularly between the 'accuracy' and 'sensitivity' measures of arousal responses in the emotion domain and many 'psychosocial' measures in the psychological-characteristics domain. This mode of covariation suggests that individuals characterized with positive social assets tend to show polarized arousal responses to life events.

Introduction

Some emotions, especially six [1-3] or up to nine [4-6] categorical emotions which are tightly associated with distinct nonverbal expressions, appear to be universal at high degrees. On the other hand, there has been evidence also showing that perception of emotion varies across cultures at significant degrees [7, 8]. Furthermore, even within the same culture, individuals substantively differ in their emotion perception or reactivity, exhibiting different responses (e.g., from being mildly surprised, to shivering, and even to crying) to the same object or event (e.g., a scene in a horror movie).

The appraisal processes [9, 10], multi-component compositions [11, 12], or unfolding dynamics [10, 13] of emotion have been suggested to be among many factors contributing to such individual differences [14]. Specifically, the appraisal process is based on an individual’s idiosyncratic life-long history of experiences, making emotional experience highly subjective. The multi-component nature of emotion implies that the same category of emotion (e.g., anger) may differ in actual composition across individuals, which leads to finely nuanced variations in emotional experience [15]. The literature on emotion dynamics indicates that individuals substantively differ in emotion duration [16] and in the variability of intensity over time [17, 18].

Not surprisingly, considering these factors contributing to individual differences in emotion, individual differences in emotion are far greater than those in any other mental functions [19, 20], on which the field of personality research is grounded: “emotions make people individuals”, as often phrased [14]. In this sense, i.e., to understand what makes people individuals, it is crucial to identify the systematic structures of individual differences in emotion and elucidate how such structures relate to what aspects of psychological characteristics including long-lasting personality traits, psychosocial states, and psychiatric symptoms. Advancing such identification and elucidation would not just advance our understanding of the idiosyncratic nature of human emotion but also provide practical solutions to the problems in diverse social or clinical situations. For instance, effective pedagogical strategies can be tailored to individuals based on individual differences in emotion to enhance the communication between tutors and students [21], or identification of emotion perception styles that are associated with certain mental disorders on social networks can be used as an indirect yet highly natural probe for detecting high-susceptible individuals from a normal population [22].

Reflecting this importance, a large volume of work has been carried out to relate emotions to psychological characteristics across individuals. These studies can be sorted in terms of what aspects of emotion were probed, namely ‘accuracy’, ‘bias’, ‘variability’, and ‘differentiability’. Previous studies that probed the accuracy of emotion [23-29], which refers to how less deviated an individual’s emotion responses are from the population norm, reported its association with personality traits or mental problems. For instance, individuals with extraversion and neuroticism traits tended to be high and low, respectively, in emotion accuracy [28]. Clinical problems, such as depression, anxiety, and schizophrenia, typically showed negative associations with emotion accuracy [23-26]. Previous studies on the bias of emotion [30-32], which refers to how biased an individual’s emotion responses are toward a certain category (e.g., ‘happy’) or affective state (e.g., ‘positive side on the valence axis’), reported its associations with psychosocial factors or personality traits. For instance, individuals with good coping skills tended to be positively biased in valence [32], or those with openness traits were positively biased both in valence and arousal [31]. The variability of emotion refers to how inconsistent an individual’s emotion responses to the same event or highly similar events are [11, 33] and has been reported to show a positive relationship with neuroticism and a negative relationship with agreeableness [11]. Lastly, the differentiability of emotion (also dubbed “emotion granularity”) refers to how finely an individual can discriminate emotional situations—how sensitive an individual is in detecting subtle nuances or differences between emotional situations [34-36]. Individuals with high differentiability of emotion are reported to exhibit high scores of self-esteem [37].

We note that the previous studies mentioned above were mostly designed to test theories or hypotheses about the relationship between certain aspects of emotion and psychological characteristics relying on pairwise comparison analysis methods. Even when multiple emotion measures and different psychological characteristics were collected within single studies, only bivariate relationships were mostly inspected [38, 39]. This ‘hypothesis-driven and regional’ approach taken by these previous studies may efficiently address their respective ‘regional’ questions by testing the predictions of interest. Despite this merit, the ‘hypothesis-driven and regional’ approach may not be ideal for revealing the systematic and global structure that governs the across-participant covariation between the domain of emotion responses and that of psychological characteristics, especially when considering the aforementioned factors contributing to large individual differences in emotion. Specifically, the hypothesis-driven and regional approach may be insensitive to the presence or absence of a potential structure that can be defined only in a multidimensional space of emotion or psychological characteristics. In other words, a significant pairwise correlation does not warrant its participation in the true global structure that governs the relationship between the two domains [40, 41]. Likewise, an insignificant pairwise correlation does not necessarily mean that it does not contribute to the true global structure [42].

The goal of the current work is to identify the systematic and global structure that governs the across-individual covariation between the domain of emotion and that of psychological characteristics. To achieve this goal effectively, we took the ‘data-driven and global’ approach—as an alternative to the hypothesis-driven and regional approach—and considered several other important aspects, as follows. First, we used visual narrative stimuli, 15-second long film excerpts of various genres, to probe emotion responses. Visual narratives can be considered ideal for promoting the across-individual variability because they contain the aforementioned ingredients contributing to individual differences of emotion: various affective states are expected to be unfolded [13, 43] as people undergo the appraisal process [9, 10] by integrating multiple cues under complex and natural contexts [11, 12]. Second, we collected as many and diverse measures as possible in both domains. As for the domain of psychological characteristics, we collected a total of 68 measures using 19 different batteries of psychometric questionnaires, which cover the subdomains including ‘personality’, ‘psychosocial factors’, and ‘clinical problems’. As for the domain of emotion, we acquired two-dimensional (‘arousal’ and ‘valence’) affective-state responses to visual narratives and derived the four measures that have been reported by the previous work to be associated with certain psychological characteristics (we named the measures as ‘accuracy’, ‘bias’, ‘consistency’, and ‘sensitivity’). Lastly, we carried out multivariate analyses to discover a global structure of the across-individual covariation between the emotion-response and psychological-characteristics domains. In doing so, to address the known limitations of multivariate analysis methods associated with the dimensionality and interpretability issues, we conducted the canonical correlation analysis (CCA) [44] in conjunction with the principal component analysis (PCA) [45], which allowed us to effectively search a compact and interpretable feature space for significant across-individual covariations between specific styles of emotion responses and particular profiles of psychological characteristics.

To anticipate results, the multivariate analyses on the data collected from 86 individuals revealed a single robust mode of covariation that links the domains of emotion responses and psychological characteristics. Specifically, the ‘accuracy’ and ‘sensitivity’ measures of arousal responses in the emotion domain and many ‘psychosocial-factor’ measures in the psychological-characteristics domain contributed to the mode of population covariation. Based on further analyses on those measures with significant contributions, we reached an interpretation that the mode reflects the tendency of individuals characterized with positive social perspectives to show polarized arousal responses to life events.

Methods

Participants

We recruited 86 Korean undergraduate students of similar ages (41 females, M = 21.4, age range: 18–24 years; Table 1). We justified the sample size by proceeding with simulations for power and specificity (see S1 Appendix for detail). All participants were interviewed by trained clinicians to be prescreened for neurological and/or psychiatric disorders. Six participants who had high (moderate to severe) Beck Depression Inventory (BDI) or Beck Anxiety Inventory (BAI) scores were excluded from further analysis. Participants all had a normal or corrected-to-normal vision. In addition, participants were also cataloged for their sex, age, IQ, and family income to statistically de-confound the individual differences that might potentially confound the relationship between psychological characteristics and emotion responses. This study was approved by the Seoul National University Research Ethics Committee, and informed written consent was obtained from all participants prior to actual participation.

Table 1

Demographic summary of participants.

Demographic variables		Frequency (n)	Percentage (%)
Sex	Male	45	52.3
	female	41	47.7
Age	18–20	28	32.6
	20–24	58	67.4
Monthly household income	less than $ 1,000	2	2.3
	$ 1,000-$ 1,999	2	2.3
	$ 2,000-$ 2,999	10	11.6
	$ 3,000-$ 3,999	11	12.8
	$ 4,000-$ 4,999	13	15.1
	$ 5,000-$ 5,999	12	14.0
	$ 6,000-$ 6,999	9	10.5
	$ 7,000-$ 7,999	3	3.5
	$ 8,000-$ 8,999	8	9.3
	$ 9,000-$ 9,999	3	3.5
	$ 10,000 or more	10	11.6
	Not stated	3	3.5
IQ	91–100	2	2.3
	101–110	13	15.1
	111–120	36	41.9
	121–130	30	34.9
	131–140	5	5.8
Total		86	100.0

Psychological-characteristic measures

To acquire a comprehensive and unbiased set of psychological characteristics, we used a total of 19 psychometric questionnaires that were associated with diverse taxonomies that capture individual differences. These taxonomies included: (i) the personality taxonomies that capture individual differences in relatively enduring behavioral tendencies based on the ‘Big Five’ model [46, 47], the ‘Reinforcement Sensitivity’ theory [48, 49], and Cloninger’s ‘Psychobiological model of temperament and character’ [50]; (ii) the psychosocial-factor taxonomies that capture individual differences in capacity for recovery after significant adversity [51], in-person social support [52, 53], perceived social rank [54], capacity for empathy [55], perceived quality of life [56], recent life experience [57], and attitude towards themselves [58]; (iii) the clinical-problem taxonomies that capture individual differences in propensity for major mental problems such as psychological personality disorders [59], anxiety disorders [60, 61], substance abuse [62, 63], suicidal thinking [64], depression [65], and affective disorders [66]. A full list of the psychological-characteristic measures and psychometric questionnaires is available in the supporting information (S1–S3 Tables). All the questionnaires were provided in Korean, being translated if needed.

Since it took roughly 5 hours to complete the entire questionnaires, participants brought the questionnaires their home and turned in their answers a week later. Considering the cognitive burden on participants [67], we instructed them to fill in the questionnaires over multiple days by taking breaks of sufficient length. To prevent the incompletion of the questionnaires [68], we checked whether there exist any missed or inappropriate responses to items upon reception of the questionnaires and, if so, asked participants to respond to such items on site.

Visual narrative stimuli

One of the authors (Y.K.), a film expert who majored in film art and worked in film-editing companies, built the library of visual narratives (VNs) by referring to the Internet Movie Database (IMDb) and Schaefer and his colleagues’ work [69] relying on the following guidelines. First, the referred video sources were diverse and balanced in the genre, including action adventure, biography, comedy, documentary, drama, family, fantasy, mystery, horror, romance, sci-fi, sport, thriller, and western genres. Second, the scenes were selected such that they collectively covered a wide range of affective states, both in the valence and arousal dimensions. Third, every excerpt consisted of events that constituted a coherent piece of storytelling, such that it could be readily described with a few sentences. The last guideline was considered to ensure that an affective state was induced as a ‘visual story’ unfolds for each clip.

Most of the VN stimuli were excerpted from motion pictures (130 clips from 124 different motion pictures). Some affective states (e.g., states of low arousal and neutral valence) rarely occur in the motion-picture database that we referred to. To cover such affective states, music videos (7 clips from 4 music videos) or TV commercials (7 clips from 4 TV commercials) were also referred to. There is no specific reason to believe that these non-motion-picture clips differ from the motion-picture clips in the effectiveness of inducing affective states because both types of clips induce affective states with visual stories in the same manner. Notwithstanding, we confirmed that the emotion measures acquired using only the 130 motion-picture clips were highly correlated with those using the entire clips (S1 Fig, panel B). More details of the VN stimuli are provided in the supporting information (S1 File).

All VN stimuli were edited to be 15-second long, which is close to the length of commercial ads on TV or the Internet. They were made soundless to focus on nonverbal emotion perception and standardized in size (1400-pixel width, 744-pixel height), temporal frequency (24 frames per second), and color format (8-bit RGB). Stimulus presentation and collection of participants’ responses were controlled using Psychophysics Toolbox extensions [70-72] in conjunction with MATLAB 2014b on an iMac computer with OS X (Apple Inc.).

Emotion rating task

On each trial, participants were asked to indicate their emotional states after viewing freely (and without fixation) a 15-second VN stimulus displayed on the computer screen. Emotion ratings were collected for the dimensions of valence and arousal using the 9-point self-assessment manikin scale (SAM) [73]. Participants were given as much time as they required to rate stimuli before submitting their final ratings to the computer system. The rating session consisted of 6 blocks of 24 trials, and the order of 144 VN stimuli was randomized across participants. Four practice trials were completed prior to the main task to ensure that participants understood the instructions. The data from these practice trials were excluded from the analysis.

The across-participant averages of the affective states assigned to the VN stimuli (Fig 1A) were widely distributed over both dimensions of the affective space, exhibiting a typical ‘V-shape’ pattern—valence scores tend to bifurcate toward the negative and positive poles as arousal scores increase–, which has been repeatedly reported in previous work [74, 75]. We also expected that our VN stimuli would induce substantial individual variability in emotion responses. Indeed, the rating scores for the same VN stimulus varied considerably between individuals (Fig 1B), which resulted in standard deviations ranging from 0.63 to 1.88 in the valence dimension and from 1.10 to 2.02 in the arousal dimension.

Fig 1

The procedure of acquiring emotion responses and defining emotion measures.

(A-B) Emotion-rating responses to visual narratives (VNs). (A) Normative (i.e., averaged across participants) rating responses to the 144 VN stimuli plotted in the affective-state space of valence and arousal. The colored dots are the normative responses to the three example VN stimuli (VN 15, VN 24, and VN 81). (B) Contour histograms of individual emotion ratings of the three example VN stimuli. The colored dots are the same as the corresponding ones in A. (C) Definition of the emotion measures. Left three panels: the ‘bias’, ‘sensitivity’, and ‘consistency’ measures were defined by regressing individual participants’ emotion rating responses onto the normative responses. Schematic examples of regression lines or confidence intervals are shown for individuals with high (black) and low (gray) scores of the ‘bias’ (left), ‘sensitivity’ (middle), and ‘consistency’ (right) measures. Right panel: the ‘accuracy’ measures were defined as the mean of absolute deviations from the normative responses. Example vectors of the absolute deviations are shown for individuals with high (black) and low (gray) scores of the ‘accuracy’ measure.

Emotion-response measures

Having confirmed that our VN stimuli covered the affective space in a representative manner while inducing sufficient individual differences (Fig 1A and 1B), we quantified those individual differences with a set of ‘emotion-response measures’, which measures how much the emotion rating patterns of individuals deviate from the ‘normative’ pattern in several aspects. Here, the ‘normative’ pattern refers to the distribution of emotion ratings averaged across all participants for the entire library of VN stimuli (Fig 1A). This population average can be considered as the ‘typical’ emotion responses that are shared across participants and thus represent empirical approximations of the ‘normative’ affective states induced by the VN stimuli.

Specifically, for a given individual i, the normative response was calculated as the average across the entire population except for the individual i. We then linearly regressed the participant i’s response to a visual narrative l, r, onto the normative ratings, , over the 144 VN stimuli, using the following regression model: where ε is the error, which was minimized to estimate the intercept, α, and slope, β. To get the best-unbiased estimators of regression coefficients, the regression model was fit using the method of weighted least squares rather than ordinary least squares, because VN stimuli differed substantially in across-participant variability (standard deviations ranged from 1.10 to 2.02 for arousal ratings and from 0.63 to 1.88 for valence ratings). As a result, squared residual errors were weighted by the reciprocals of variances [76]. After fitting the regression model, we computed the proportion of the variance of r explained by the linear regression onto . This triplet of regression parameters, {α, β, δ}, provides a complementary set of distinct aspects that reflect how an individual’s emotion responses deviate from the normative responses, as follows: α reflects the extent to which a given individual i’s emotion responses are biased, providing a ‘bias’ measure; β reflects how sensitively a given individual i’s emotion responses change as the normative responses change, providing a ‘sensitivity’ measure; δ reflects how noisy or unpredictable a given individual i’s emotion responses are, providing a ‘consistency’ measure (Fig 1C). Besides these measures based on regression analysis, we calculated the accuracy measures that have widely been used by the previous studies on individual differences in emotion responses [27, 28, 77]. The ‘accuracy’ measure, σ, was defined as the mean of absolute deviations from the normative responses across all 144 VN stimuli. The sign was reversed (-1×mean of absolute deviation) so that higher values mean higher degrees of accuracy. Considering the possibility that individual differences may exist independently between the two dimensions, the regressions and the accuracy calculation were performed separately for the arousal and valence dimensions. In sum, the way a given individual assigns emotional states to the VN stimuli was described by a vector of eight measures, , where the subscripts, a and v denote the two subdomains of emotion, ‘arousal’ and ‘valence’, respectively.

Canonical Correlation Analysis (CCA)

To meet the prerequisites of CCA and to avoid redundancy, we preprocessed the raw psychological-characteristics data, an 80×68 (subject × individual-characteristics measures) matrix C, and the raw emotion data, an 80×8 (subject × emotion measures) matrix E, before carrying out the CCA in the following procedure. First, the raw measures were screened for extreme distributions of values by applying two criteria, ‘extreme homogeneity’ and ‘extreme outliers’. A distribution with ‘extreme homogeneity’ was defined as one in which more than 90% of participants had an identical single value, whereas a distribution with ‘extreme outliers’ was defined as one in which the average squared deviation of values from their median was smaller by 100-fold than the maximum squared deviation, as follows: where θ refers to a participant i’s psychological-characteristics or emotion measure. Only one psychological-characteristics measure was excluded in this step, which resulted in C(80×67 matrix) for the psychological-characteristics data and E (= E) for the emotion-response data. Next, to avoid the unwanted effects of hidden outlier values and to satisfy the assumption of normal distribution, which is required of the CCA [78, 79], we rescaled the values of C and E into rank values and then ‘Gaussianized’ those rank-scaled values by mapping them onto the normalized value space [80], producing C and E (Fig 2A and 2B).

Fig 2

The procedure of the multivariate analyses.

(A) A matrix of psychological-characteristics measures (C). Columns, 67 screened measures. Rows, 80 screened participants. The rows are identical among the matrices shown in A-F. The color hues and saturations of pixels correspond to the signs and strengths, respectively, of the normalized psychological-characteristics scores. (B) A matrix of emotion-response measures (E). Columns, 8 measures. The color hues and saturations correspond to the signs and strengths, respectively, of the normalized emotion-response measures. (C) A PCA-score matrix for the psychological-characteristics measures (C). Columns, 27 subject-wise eigenvectors with largest eigenvalues. In the actual analysis, the number of eigenvectors was varied from 8 to 30. (D) A PCA-score matrix for the emotion-response measures (E). Columns, eight subject-wise eigenvectors. (C, D) The color hues and saturations of pixels correspond to the signs and strengths, respectively, of the normalized PCA scores. (E) A CCA-score matrix of the psychological-characteristics measures (C). Columns, eight canonical variates. (F) A CCA-score matrix of the emotion-response measures (E). Columns, eight canonical variates. (E, F) The color hues and saturations of pixels correspond to the signs and degrees, respectively, of the normalized CCA scores. (G) There was a strong and significant correlation between the canonical variates paired in the first (k = 1) CCA mode. The CCA scores of the first canonical psychological-characteristics variate (C) are plotted against those of the first canonical emotion variate (E) over individual participants (gray dots). A line is the linear regression of E onto C. (H) Correlations of C with the individual columns of C in A. The order of bars is identical to that of the columns in A. (I) Correlations of E with the individual columns of E in B. The order of bars is identical to that of the columns in B.

Next, the PCA was conducted on C and E, which resulted in C and E (Fig 2A and 2B), to avoid the overfitting due to the high dimensionality of the psychological-characteristics measures and to orthogonalize the individual measures in C and E. In building C, the number of PCs was varied from 8 to 30 because it may affect the results of CCA. This particular range of PC numbers was determined by applying two criteria: (i) more than 60% of the total variance of C should be explained; (ii) the canonical correlation should be significant. As for E, the number of PCs was fixed to 8. The subjects-to-subjects covariance matrix was fed into eigenvalue decomposition to determine subject-wise eigenvectors with the largest eigenvalues for each measure type [78]. As a result, 100% of the total variance of E was explained by E while 61.35% (for 8 eigenvectors) to 92.18% (for 30 eigenvectors) of the total variance of C was explained by C. Although the dimension of E was low, we applied PCA to E because we wanted to use the procedure identical to that used for C (but the results remain almost unchanged whether PCA was applied to E or not). To prevent potential confounds with socio-demographic factors, C and E were de-confounded for age, sex, IQ, and income scores prior to CCA. Specifically, those socio-demographic variables underwent a rank-based inverse normal transformation and were regressed out from both C and E.

We conducted the CCA on C and E using the ‘canoncorr’ function in the Statistics and Machine Learning Toolbox of MATLAB. The CCA initially identified an orthogonal set of ‘pairs of canonical variates’ ({(C, E), (C, E),…,(C, E),…,(C, E)}) that maximizes the pairwise correlations between linear combinations of C and E. The first CCA mode, (C, E), was defined as follows: where

Similarly, the remaining CCA modes, {(C, E),…,(C, E),…,(C, E)} were sequentially defined by finding a pair of vectors, and , which maximizes the correlation between paired variates, C and E, with a constraint that these newly added variates, C and E, must be orthogonal (uncorrelated) to all the preceding modes (Fig 2E and 2F). To assess the statistical significances of the CCA modes, we permuted C over participants 10,000 times and computed the correlation for each pair of the corresponding columns of C and E.

As the final step, we computed the correlations of the paired canonical variates that constitute the significant first CCA mode, C and E, with their raw measures, C and E, respectively, to identify the specific psychological characteristics and the emotion-response measures that covary across participants via the first CCA mode.

Results

Distribution and reliability of the emotion-response measures

The across-participant distributions of the emotion-response measures are summarized in Table 2. For the measures of consistency (δ) and accuracy (σ), the distribution means were greater in valence than in arousal (δ: t(158) = 3.62, p < .001; σ: t(158) = 4.75, p < 0.001). By contrast, for all the measures except for sensitivity (β), the standard deviations of the distributions were greater in arousal than in valence (α: F(79,79) = 6.54, p < 0.001; β: F(79,79) = 1.41, p = 0.127; δ: F(79,79) = 2.26, p < 0.001; σ: F(79,79) = 3.10, p < 0.001).

Table 2

Summary statistics of emotion measures.

Measures	Arousal		Valence
	μ	SD	μ	SD
Bias (α)	0.00	0.70	0.00	0.27
Sensitivity (β)	0.99	0.30	1.00	0.25
Consistency (δ)	0.68	0.14	0.75	0.09
Accuracy (σ)	-1.18	0.39	-0.94	0.22

We evaluated the reliability of the emotion-response measures in two aspects. First, when the trials were split into two subsets, such that two different sets of VN stimuli were used in those two subsets, the measures were highly consistent between those subsets (see S1 Fig for detailed procedures and results). Second, the emotion-response measures remain consistent even when the normative emotion responses (i.e., across-participant average responses) were defined from much a smaller number of subjects (see S2 Fig for detailed procedures and results).

Distribution of the psychological-characteristics measures

We classified the psychological-characteristics measures into three groups, namely the ‘psychosocial factors’, ‘clinical problems’, and ‘personality’ measures, depending on the original purposes of the questionnaires (Fig 3A). For the purpose of screening out the measures with unhealthy across-participant distributions, we inspected whether a given distribution contains a few individuals with extremely outlying scores (Fig 3B) and whether it is too narrow for individuals to be distinguished from one another (Fig 3C). As a result, the FTND (Fagerström Test for Nicotine Dependence) measure was screened out because its distribution was extremely narrow (i.e., too homogenous because 95% of the participants were non-smokers; Fig 3E) compared to the remaining variables (the distributions of two example measures are shown in Fig 3D and 3F).

Fig 3

Distribution analysis of the psychological-characteristics measures.

(A) The group identities and measure numbers of the 19 psychological-characteristics questionnaires. The questionnaires from which individual measures (horizontal bars) and the groups to which the questionnaires belong are indicated by the word labels with vertical bars and brackets, respectively. The measure that was not used for further analysis is indicated by the horizontal empty bar. (B,C) Results of the distribution analysis. (B) None of the measurements had extremely outlying scores, as indicated by the dots that all fell below the criterion (the red dotted line). (C) A single measure (FTND) had an extremely homogenous distribution, in which the majority (95%) of participants had the same score, as indicated by the dot located above the criterion (a red dotted line). (D-F) The distributions of three example measures. By comparing the distribution shapes and their corresponding scores in C, the exceptionally strong homogeneity of the FTND distribution can readily be appreciated. KRQ, Korean resilience quotient; MSSS, MacArthur scale of subjective social status; LES, life experiences survey; SSS, Social Support Scale; WHOQOL, world health organization quality of Life; IRI, interpersonal reactivity index; ULS, UCLA Loneliness Scale; RSES, Rosenberg self-esteem scale; SCID-II, structured clinical interview schedule for DSM-IV Axis-II disorder; SSI-Beck, Beck scale for suicidal ideation; AUDIT-K, Alcohol Use disorder identification test; BAI, Beck anxiety inventory; BDI, Beck depression inventory; FTND, Fagerström Test for Nicotine Dependence; TEMPS, temperament evaluation of Memphis, Pisa, Paris and San Diego; STAI, state-trait Anxiety Inventory; NEO, revised NEO personality inventory; TCI, temperament and character inventory; BAS/BAS, behavioral approach/inhibition system.

The robustness of the first CCA mode

Regardless of the varying number of PC components that used to define the psychological-characteristics input to CCA (C; see Methods for the rationale for choosing the 23 different PC numbers), only the first CCA mode remains significant (permutation-test results are shown in Table 3; see Methods for the detailed procedure of permutation tests). In what follows, given this robustness of the first mode, we assessed the contributions of the raw measures (i.e., the individual columns of C and E) to the population covariation between the psychological-characteristics and emotion-response domains based on the correlations between the canonical variables of the first CCA mode (C and E) and the raw measures (C and E), as graphically illustrated in Fig 2H and 2I.

Table 3

Correlation coefficient and permutation test result of the first CCA mode.

Number of principal components		corr(CM1, EM1)	p
Emotion measures	Psychological characteristics
8	8	0.69	0.002
	9	0.70	0.002
	10	0.70	0.004
	11	0.72	0.003
	12	0.73	0.003
	13	0.74	0.007
	14	0.75	0.006
	15	0.76	0.005
	16	0.78	0.003
	17	0.79	0.003
	18	0.80	0.003
	19	0.80	0.007
	20	0.80	0.009
	21	0.80	0.011
	22	0.80	0.019
	23	0.80	0.033
	24	0.83	0.006
	25	0.83	0.013
	26	0.84	0.009
	27	0.85	0.014
	28	0.85	0.017
	29	0.85	0.024
	30	0.85	0.035

The psychological-characteristics measures contributing to the first CCA mode

To identify the psychological-characteristics measures contributing significantly to the CCA mode, we tested the significance of the correlation of the first-mode psychological-characteristics variate (C in Fig 2) with the individual, raw psychological-characteristics measures (individual columns of C in Fig 2). We repeated this significance test 23 times, one for each of the 23 first-mode variates defined using the 23 different numbers of PCs (Table 3). Finally, we judged a given measure to be the one that makes a robust contribution to the CCA only when it showed more than 22 significant (p < 0.05 with the Benjamini-Hochberg method) correlations with the 23 first-mode variates. As a result, we identified a total of 10 measures. Their across-variate averages of correlations are summarized in Fig 4A. (For the psychological-characteristic measures that failed to meet this rather strict criterion (22 significant results out of 23 tests) but showed at least one significant correlation with the psychological-characteristics variate (C), see S3A and S3C Fig).

Fig 4

The contributions of the psychological-characteristics and emotion-response measures to the CCA mode.

(A) The across-variate averages of the correlations between the raw measures of psychological characteristics and the first-mode psychological-characteristics variate (C). (B) The across-variate averages of the correlations between the raw measures of emotion responses and the first-mode emotion-response variate (E). (A, B) Only the raw measures that showed significant correlations with more than 22 out of the 23 different CCA variates are shown. Error bars, 95% confidence intervals. SSS, social support scale; KRQ, Korean resilience quotient; RSES, Rosenberg self-esteem scale; LES, life experience survey; Audit-K, Alcohol Use Disorder Identification Test; ULS, UCLA Loneliness Scale.

Most of the measures that make robust contributions to the CCA belong to the ‘psychosocial factors’ class, especially those that are known to reflect the degree to which a given individual receives various kinds of social support from the life environment. The individuals’ psychological-characteristics variate (C) tended to increase as they reported that they receive a wider range of social support (three measures of ‘social support scale (SSS)’), are more connected to others (‘self-expansion’ measure of ‘Korean resilience quotient (KRQ)’), are more able to establish and maintain social relationships (‘communication’ measure of KRQ), have higher degrees of overall self-esteem (‘Rosenberg self-esteem scale (RSS)’), experienced more severe and frequent negative life events (two measures of ‘life experience survey (LES)’) or feel lesser degrees of subjective loneliness and social isolation (‘UCLA loneliness scale (ULS)’).

Among the measures that do not belong to the ‘psychosocial’ class, only one measure, ‘Audit-K’ in the ‘clinical-problem’ class, robustly contributed to the CCA. The psychological-characteristics variate (C) tended to increase as the scores of ‘Audit-K’, which indicates the degree of excessive alcohol drinking, increased.

The emotion-response measures contributing to the first CCA mode

Using the same procedure and criterion used for the psychological-characteristic measures, we identified the emotion-response measures that make robust contributions to the CCA mode. As a result, two measures in the arousal dimension were identified, the ‘accuracy (σ)’ and ‘sensitivity (β)’ measures (Fig 4B). (For the emotion-response measures that showed at least one significant correlation with the emotion-response variate (C), see S3B and S3D Fig).

The emotion-response variate of the CCA mode (E) tended to increase as the accuracy measure decreased and the sensitivity measure increased. Since the accuracy measure reflects an extent to which a given individual’s responses deviate from the normative responses (the fourth panel in Fig 2I), the negative correlation between σ and E means that the individuals with higher values of the emotion-response variate tended to show the arousal responses that are more deviant from the population-average responses to the visual narrative stimuli. On the other hand, the sensitivity measure reflects an extent to which changes between a given individual’s responses to different VN stimuli are greater than those expected from the normative responses to VN stimuli (the second left panel in Fig 2I). Therefore, the positive correlation between β and E means that the individuals with higher values of the emotion-response variate tended to show the arousal responses that are more exaggerated than the population-average responses.

Polarized arousal responses in the individuals with high CCA variates

Having identified the two arousal measures contributing to the CCA mode, we carried out further analysis to find a critical feature that jointly describes the relationships of the accuracy and sensitivity measures with the CCA mode in a unified manner.

As the first step of the analysis, we classified the individuals into two groups based on their canonical-variate scores (E) and plotted the group-averaged values of absolute deviation of arousal responses from the normative responses (Fig 5A) and arousal responses (Fig 5B) against the normative responses across the 144 VN stimuli. The canonical-variate scores (E) used here was the one defined with the CCA based on 27 PCs, which produced the most representative results. The absolute deviations were greater for the almost entire range of the normative responses in the high-variate-score group (Fig 5A) while the reported arousal scores varied more steeply as a function of the normative response in the high-variate-score group (Fig 5B). As a plausible scenario that is coherent with both of these two patterns, we considered the possibility that the individuals with high variate scores tend to show more ‘polarized arousal responses’ than those with low variate scores. Specifically, the extent to which responses are ‘polarized’ refers to the extent to which responses are attractively biased toward both of the two extreme poles. Thus, if a given individual’s responses are more polarized than the normative responses, her or his responses will be not just more deviant from the normative responses but also more exaggerated than the normative responses.

Fig 5

Polarized arousal responses in the individuals with high CCA variates.

(A) The averaged absolute deviations of arousal responses from the normative responses plotted against the normative responses for the high (maroon dots) and low (teal dots) E-score groups. The lines are the moving averages (window size, 10) of the averaged absolute deviations. (B) The averaged arousal responses plotted against the normative responses for the high and low E-score groups. (C) The comparison of the distributions of arousal responses between the high and low E-score groups. Top, the histograms of arousal responses that are binned according to the normative response (six panels from left), the merged histograms of the entire arousal responses (the second-rightmost panel), and the relative differences in proportion between the merged histograms (the rightmost panel, where ‘HG’ and ‘LG’ stand for the high and low E-score groups, respectively). The histograms for the high and low E-score groups are shown in maroon and teal, respectively. Bottom, the table summarizes the statistics of the histograms shown above. The columns’ locations are matched to the histograms that they describe. nr stands for the normative responses.

To confirm the ‘polarized-arousal-response’ scenario, we compared the distributions of arousal rating scores between the low-variate-score and high-variate-score groups (Fig 5C). As anticipated by the ‘polarized-arousal-response’ scenario, the response distributions were indeed different between the two groups (see F-test results at the bottom of Fig 5C) and more polarized in the high-variate-score group than in the low-variate-score group (see kurtosis and skewness results at the bottom of Fig 5C). For the merged distributions (the second-rightmost panel of Fig 5C), the kurtosis was significantly lower—i.e., flatter—in the high-variate-score group (1.76) than in the low-variate-score group (2.16). This difference in kurtosis resulted mainly from the fact that the arousal responses were more polarized in the high-variate-score group than in the low-variate-score group. The tendency of making polarized arousal responses in the high-variate-score group was also evident in the local distributions that were binned according to the normative responses (the 6 panels from left in Fig 5C). As the range of the normative responses becomes lower or higher (i.e., approaches toward extreme values), the response distributions become more skewed in the high-variate-score group than in the low-variate-score group (as indicated by the skewness values in Fig 5C). On the other hand, as the range of the normative responses approaches toward intermediate values, the response distributions become flatter in the high-variate-score group than in the low-variate-score group (as indicated by the kurtosis values in Fig 5C).

We also inspected the distributions of the valence responses with the same procedure used for the arousal responses but did not find substantial differences between the high-variate-score group and the low-variate-score group (S4 Fig).

We note that there is a, rather trivial, alternative account for the observed differences in distributions between the high and low E-score groups: such differences may also arise from the differences in the overall tendency of given individuals to report extreme values whatever being measured. To address this issue, we (i) estimated such tendency for the individual participants from their reports in the psychological-characteristics questionnaires, (ii) de-confounded the data for such tendency, and (iii) repeated the CCA analysis (for details, see Methods in S2 Appendix). The results of this de-confounded CCA were quite similar to those of the original CCA: the arousal accuracy (, E(r) = −0.55) and arousal sensitivity (, E(r) = 0.28) still showed the robust relationship with psychosocial factors similar to our main findings (see S2 Appendix Fig 1 for details). These results suggest that the polarized arousal responses are unlikely to be explained away by the overall tendency of reporting extreme values.

Comparisons of the pairwise correlations and the results of the multivariate analysis

To directly compare our work with previous work, which mostly took the hypothesis-driven regional approach based on pairwise correlations, and also to further understand the structure of population co-variation between the psychological-characteristics and emotion-response domains, we calculated pairwise Pearson correlations for all the possible pairs between the measures of the two domains (left panel of Fig 6) and compared those correlations with the CCA variates (right panel of Fig 6). By comparing the Pearson correlations and the CCA-variate correlations, all the possible relationships between the measures of the two domains can be classified into four different types, as follows: first, the relationships that were insignificant in both types of correlation; second, those that were significant in Pearson correlation but insignificant in CCA correlation; third, those that were insignificant in Pearson correlation but significant in CCA correlation; lastly, those that were significant in both types of correlation. We note that all the ‘significant’ Pearson correlations turned out insignificant after being corrected for multiple comparisons (Benjamini-Hochberg correction).

Fig 6

Comparisons of the pairwise correlations and the results of the multivariate analysis.

Left, the rows and columns of the matrix represent the psychological-characteristics and emotion-response measures, respectively. The empty rectangles mark the pairs of measures that were significant in Pearson correlation but did not make significant contributions to the CCA mode. The solid rectangles mark the pairs of measures that not only were significant in Pearson correlation (p < 0.05, uncorrected for multiple comparisons) but also made significant contributions to the CCA mode. Colors of the rectangles indicate the signs of Pearson correlation (red, positive; blue, negative). Right, the schematic structure of the CCA mode illustrated based on the correlations of the measures with the CCA variates. The inset plots the psychological-characteristics variate against the emotion-response variate over individual participants, which is identical to the panel of Fig 2G. The empty squares mark the measures that made significant contributions to the CCA mode but failed to show significant Pearson correlations. The solid squares mark the measures that not only made significant contributions to the CCA mode but also showed significant Pearson correlations. Colors of the squares indicate the signs of correlations with the CCA variates (red, positive; blue, negative). KRQ, Korean resilience quotient; MSSS, MacArthur scale of subjective social status; LES, life experiences survey; SSS, Social Support Scale; WHOQOL, world health organization quality of Life; IRI, interpersonal reactivity index; ULS, UCLA Loneliness Scale; RSES, Rosenberg self-esteem scale; SCID-II, structured clinical interview schedule for DSM-IV Axis-II disorder; SSI-Beck, Beck scale for suicidal ideation; AUDIT-K, Alcohol Use disorder identification test; BAI, Beck anxiety inventory; BDI, Beck depression inventory; TEMPS, temperament evaluation of Memphis, Pisa, Paris and San Diego; STAI, state-trait Anxiety Inventory; NEO, revised NEO personality inventory; TCI, temperament and character inventory; BAS/BAS, behavioral approach/inhibition system.

Many (17) relationships fell into the class in which their Pearson correlation was significant but their CCA correlation was insignificant (those marked by empty rectangles in the left panel of Fig 6). On the other hand, four psychological-characteristics measures (KRQ-communication, SSS-informative, ULS, and Audit-K, which are marked by the empty squares in the right panel of Fig 6), despite their significant correlations with the CCA variate, did not show significant Pearson correlations either with the arousal sensitivity measure (β) or with the arousal accuracy measure (σ). On the contrary, six psychological-characteristics measures (KRQ-self-expansion, LES-frequency of negative experience, LES-severity of negative experience, SSS-emotional, SSS-evaluative, and RSES) not just contributed, jointly with the arousal sensitivity (β) and accuracy (σ) measures, to the CCA mode (as marked by the solid squares in the right panel of Fig 6) but also showed significant Pearson correlations with those two emotion-response measures (as marked by the solid rectangles in the left panel of Fig 6). This result, if we put together the signs of Pearson and CCA correlations, helps us interpret a refined structure of the CCA mode. That is, the CCA mode mainly consists of the positive covariation of the arousal sensitivity measure in the emotion-response domain with the KRQ-self-expansion, LES-severity of negative experience, SSS-emotional, and SSS-evaluative in the psychological-characteristics domain (as indicated by the solid red rectangles in the left panel of Fig 6) and the negative covariation of the arousal accuracy measure in the emotion-response domain with the LES-frequency of negative experience, and RSES measures in the psychological-characteristics domain (as indicated by the solid blue rectangles in the left panel of Fig 6).

Discussion

Being motivated to identify a systematic structure that governs the population covariation between the emotion-response and psychological-characteristics domains, we took a data-driven and global approach by carrying out a series of multivariate analyses on a high-dimensional data set consisting of the eight emotion-response measures and 68 psychological-characteristics measures that were acquired from a cohort of 86 human participants. Having had identified a single, robust, canonical mode of covariation using the CCA in conjunction with PCA, we projected that canonical mode back onto the raw measures in both domains and carried out further analyses to explore ‘interpretable’ inter-domain relationships underlying the canonical mode. We found one such relationship: individuals who can be characterized by being ‘rich in psychosocial assets’ tend to show ‘polarized arousal responses’ to emotion-inducing visual narratives.

Polarized arousal responses

Emotion differentiation, which is also known as emotion granularity [36], refers to people’s ability to distinguish between similar emotions. In the studies which probed categorical emotion responses in the two-dimensional affective space [81, 82], individuals with high emotion differentiability showed emotion responses that were widely distributed mainly along the ‘arousal’ dimension, which can be interpreted to correspond to the polarized responses in the arousal dimension contributing to the canonical mode in the current work (Fig 5C). On the other hand, another previous work reported that individual differences in emotion differentiation were positively correlated with those who show high degrees of resilience and self-esteem [35, 36], which matches the psychosocial measures contributing to the canonical mode in the current work. Put together, these reports on emotion differentiation appear highly consistent with the canonical mode of population covariation and our interpretation of it.

Association between psychosocial assets and polarized arousal responses

We conjecture that the observed tight linkage between richness in psychosocial assets and polarized arousal responses might have to do with a phenomenon called “the social sharing of emotions [83]” and an influential view developed upon this phenomenon [84]. According to this view, emotional experiences are not short-lived and intrapersonal but actively shared with other individuals, functioning as social signals of communicating one’s internal states, which eventually promotes social interactions. For instance, by crying, a baby can send a parent a signal of hunger, and that signal, in turn, triggers further interactions between the baby and the parent. Supporting this view, intensive emotional experiences are known to be more likely to be expressed to others [85-87] and even discussed with others to some degree [88]. For example, people tend to talk more with strangers after watching together the movies that are emotionally intense than after watching those that are not [89]. According to this view, the individuals who showed more polarized arousal responses to the visual narratives, compared to those who showed less polarized responses, in the current work are more likely to express their emotions to others in their daily life and thus more likely to be engaged in social interactions. And such increased social interactions would be translated into the high scores on the psychosocial factors that indicate the richness in psychosocial assets, such as those that reflect ‘receiving more social supports’, ‘feeling connected with others’, ‘having good communication with others’, and ‘subjective feeling of heightened self-esteem’.

We stress that the proposed account above should be considered as one plausible hypothesis for the observed association between the emotion-response and psychological-characteristics domains. Thus, the validity of this hypothesis must be verified in empirical studies. Especially, it would be ideal if such studies can address the issue of the direction of influences between social interactions and emotion expression, given that our findings do not imply any causal relationship between the emotion-response and psychological-characteristics domains.

Association between negative life experiences and polarized arousal responses

The CCA mode identified in the current work indicates that the tendency of showing polarized arousal responses was also associated with that of having negative experiences more frequently and severely. As a hypothetical account for this association, we considered a possibility that stressful life events are likely to make individuals react to emotion-inducing stimuli more sensitively. In line with this possibility, it has been reported that reading stressful stories tends to make people better categorize emotions [90, 91], which could be interpreted as increasing the level of attention to emotional events under uncertain and threatening situations [91].

A negligible contribution of clinical problems to the population covariation

Previous studies reported that some emotion measures are correlated across individuals with the psychological-characteristics measures on mental disorders (‘clinical problems’ according our labeling scheme), especially the anxiety-related measures [23, 30]. However, the contribution of the clinical-problem measures to the population covariation between the emotion and characteristics domains was almost negligible, if any, in the current work. Although we acquired many (N = 24) clinical-problem measures from a comprehensive set of diverse and representative questionnaires, we found that none of them, except for one (‘alcohol-use’ measured with Audit-K), significantly contributed to the population covariation. The outcomes of the pairwise comparison analysis (Fig 6, left) suggest one plausible reason for the difference between the previous and current works. Initially, we found many significant pairwise correlations including the clinical-problem measures from the questionnaires such as SCID-II, SSI Beck, BAI, TEMPS, and STAI. However, they all fail to be significant once corrected for multiple comparison. This suggests that the correlations involving clinical-problem measures were reported to be significant in the previous work because they were tested individually in isolation despite not being sufficiently strong to survive the correction for multiple comparisons. To be sure, we do not insist that those pairwise comparisons are inappropriate. They served the main purpose of the previous work, which was to verify specific hypothesis-driven predictions. Our findings suggest that the contribution of clinical-problem measures to the population covariation between the emotion-response and psychological-characteristics domains is not as strong as the psychosocial measures.

As mentioned above, the measure of ‘alcohol-use’, unlike all the other clinical-problem measures, was significantly correlated with the canonical mode of population covariation. We considered two possible scenarios for this correlation. First, alcohol overuse might have impaired cognitive ability in general, including emotion processing. This scenario seems consistent with previous clinical studies [92] reporting the correlation between alcohol use disorders and inaccuracy in facial emotion perception, because one feature of polarized emotion responses is the increased deviations from normative (average) response—i.e., inaccuracy in emotion response. As an alternative scenario, it is possible that individuals who are socially active [93] or under stressful situations [94] are prone to alcohol consumption. In line with the latter scenario, the pairwise correlation analysis on our data showed that the alcohol measure was positively correlated both with the frequency of negative experiences (r = 0.3, p < 0.01) and with the severity of negative experiences (r = 0.26, p = 0.02).

No significant relationship of personality measures with emotion measures

Previous studies reported that a few psychological-characteristics measures of personality traits are significantly correlated with emotion responses. For example, it has been reported that valence responses to static images are biased positively and negatively in individuals with extraversion and neuroticism traits, respectively [31, 95]. These previous reports suggest at least some significant pairwise correlations of those trait measures with some of valence measures in our data. However, we could not find such correlations at all, needless to mention no involvement of those traits in the between-domain population covariation. In that regard, none of the remaining measures of personality show significant correlations with any of the emotion response measures or the population covariation either (only one TCI measure (harm avoidance) showed a significant correlation with the bias measure of valence but failed to survive the correction of multiple comparisons). We conjecture that the difference in emotion-inducing stimuli between the previous work (simple static images) and the current work (complex unfolding-over-time narratives) might have resulted in different results. Alternatively, the visual narrative stimuli used in the current work might not have created a sufficient degree of variability in valence responses, as hinted by the standard deviations in the valence measures being somewhat smaller than those in the arousal measures (Table 2).

Other contributions of the current work to emotion research

Apart from identifying the robust mode of population covariation between the emotion-response and psychological-characteristics domains, the current work makes several useful contributions to the scientific investigation of individual differences in emotion. First, we found that there are substantive individual differences in ‘arousal’ responses to the same stimuli and that those differences can be predicted by profiling individuals for the psychological-characteristics measures, particularly the psychosocial measures. This warns against the possibility that even the same experimental manipulation of emotion using laboratory stimuli may end up with inducing substantially different degrees of ‘subjective (or effective) arousal’ responses across individuals. This, in turn, calls for the attention to the necessity of controlling for such individual differences in emotion induction effects by taking into account the tight linkage found in our study between the polarized arousal-response style and the rich profile of psychosocial assets. Second, for the purpose of promoting fine-grained individual differences in emotion responses, we developed a large number (N = 144) of film excerpts that induce a wide range of affective states by visually unfolding stories over time. This library of visual narratives can be used to tap into individual differences in contextual effects on emotion [96, 97] or subtle and nuanced emotion processing, such as emotion granularity [81]. No significant interindividual association between emotion responses and clinical problems was found in our non-patient cohort of subjects. However, such association might be found in clinical populations. In this regard, our library of visual narratives can be considered as a natural—thus ecologically valid and unobtrusive—means of detecting the emotional symptoms specific to certain psychiatric disorders, such as schizophrenia, which tend to be accompanied by emotion impairment [26, 98]. Lastly, CCA, as one of the popular multivariate analyses, efficiently explores the association between two multivariate collections of variables by finding linear combinations of each collection that maximize a linear correlation coefficient between the two collections. The current work demonstrated the power of CCA in discovering latent structures of covariation hidden in high-dimensional data sets such as psychological-characteristics measures and diverse aspects of emotion responses. Furthermore, as demonstrated previously [78], the current work showed that CCA becomes even more powerful when it is used in conjunction with another multivariate analysis that compresses high dimensional data into a low dimensional space such as PCA, which allowed us to back-project the CCA mode onto raw—thus interpretable—measures.

Future work

In what follows, we considered a few issues that should be dealt with in future work. Firstly, we considered the possibility that ‘polarized arousal responses’ might not reflect participants’ actual emotion experiences but rather their overall tendency to choose extreme alternatives whatever questionnaire they work on. To address this concern, we conducted control tests by regressing out the “extreme response style” factor and obtained outcomes that are nearly identical to the original results (S5 Fig). Furthermore, the ‘extreme response style’ hypothesis seems unlikely because if that hypothesis is true, the ‘polarized emotion responses’ should have been observed not just for the arousal measures but also for the valence measures, which is inconsistent with our findings (S4 Fig). As a future means of ensuring that the ‘polarized response style’ indeed reflects participants’ actual emotion experiences, we consider adding physiological measurements that are tightly linked with ‘arousal’ states, such as cardiac and skin-conductance responses. Secondly, we considered how generalizable our finding is to other populations, different cultures in particular. We intentionally recruited participants to form a culturally homogenous population to minimize the individual differences in emotion responses due to cultural differences. It would be important to check whether our findings can be replicated especially in different-age groups or non-Asian cultures. If replicated, the canonical mode of population covariation found in the current work can be considered a highly generic feature of the relationship between psychological characteristics and emotion responses. Even if not replicated, such differences between cultures will provide us with valuable information about cultural differences in emotion processing. Thirdly, we considered possible ways of extending the methods of the current work to translational research on emotion in clinical populations such as schizophrenia or mood and anxiety disorder patients. For example, given the suggested linkage between emotion recognition and social functioning in schizophrenia patients [99] or between negative valence bias and maladaptive social functioning in mood and anxiety disorder patients [100, 101], the CCA analysis may help reveal a robust and refined covariation structure relating certain aspects of emotion responses to the disorder subtypes, symptoms, spectra, or stages. In addition, given the suggested linkage between schizophrenia and difficulty in integrating emotion perception with context [102], the VN stimuli used in the current work seem more suitable for further specifying the exact nature of emotional deficit in people with schizophrenia. Lastly, we considered extending the current work to include brain-imaging measures such as anatomical structure or functional connectivity as a third domain of the multi-domain CCA. Such extensions will help us elucidate the neural basis for the canonical mode of population covariation between the emotion-response and psychological characteristic domains.

Reliability of emotion measures over visual narrative stimuli.

(DOCX)

Click here for additional data file.

Invariance of emotion measures to different ways of defining normative emotion responses.

(DOCX)

Click here for additional data file.

The detailed contributions of the psychological characteristics and emotion-response measures to the CCA mode.

(DOCX)

Click here for additional data file.

Distribution analysis on the valence responses.

(DOCX)

Click here for additional data file.

The results of the CCA analysis in which ‘extreme response style’ was regressed out.

(DOCX)

Click here for additional data file.

Specifications of psychological-characteristic measures: Psychosocial factors category.

(DOCX)

Click here for additional data file.

Specifications of psychological-characteristic measures: Clinical problems category.

(DOCX)

Click here for additional data file.

Specifications of psychological-characteristic measures: Personality category.

(DOCX)

Click here for additional data file.

List of visual narratives.

(XLSX)

Click here for additional data file.

Power analysis and sample size justification.

(DOCX)

Click here for additional data file.

De-confounding the CCA for the overall tendency of making extreme reports.

(DOCX)

Click here for additional data file.

23 Sep 2021

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Reviewer #1: The article proposes a method to analyze individual differences in emotion response and explore the systematic structures underlying such response. The authors introduce justification of the existence of such differences or variation and explain the parameters of their analysis and the limitations of previous analyses. In turn, they propose a data-driven approach based on multivariate analyses allowing to identify significant across-individual covariations between the domain of emotion response and that of psychological characteristics. However, in its current form, the paper still has some weaknesses that should be addressed before publication. Firstly, the English needs to be revised to improve intelligibility and eliminate some errors mostly found in the second part of the paper as compared with the first part (see below for specific examples). I would also recommend the authors to avoid criticisms that may sound like overgeneralizations and simplifications (see below for specific examples). One of their major claims is that their method is more powerful than previous analyses based on pairwise comparisons. Everyone who has worked with pairwise comparisons is probably aware of the fact that they can vary depending on the number of factors analyzed. However, even if the multivariate method proposed is certainly more powerful, results also depend on the number of factors introduced. There is, therefore, no need to be too negative about previous approaches; the authors could still emphasize the advantages of their method and recognize the value of previous ones together with their shortcomings. Secondly, even if research meets all applicable standards for the ethics of experimentation and the statistical analysis seems to be described in sufficient detail, there are still details from the experiment that should be provided to clarify the protocol, especially those referring to the criteria to select stimuli and materials (specific examples are also provided below). Thirdly –and this is actually one of my major concerns– the discussion of results somehow lacks rigor and theoretical justification. Even if I understand that the data-driven approach adopted aims at avoiding theoretical bias, plausible explanations for the findings should be soundly justified and grounded in the literature; or alternatively, they could be provided just as hypotheses to be verified in future studies. Finally, despite its interest and value, the method proposed seems too complex and time consuming when comparing effort vs results. The benefits of using the method could be further outlined to make it worthwhile.

Here is a list of some of the changes required at specific points in the paper:

LANGUAGE AND STYLE

-Lines 105-108. The first part of the sentence requires a verb: “The variability of emotion refers to how inconsistent an individual’s emotion responses to the same event or highly similar events and…”

-Lines 118-120. I would recommend the authors to modulate and soften the criticism against “all the previous studies”, unless they positively know that they all tested “predictions of interest”. Their assumption that testing particular theories or hypotheses is necessarily negative because it leads authors to focus on “a few pre-selected measures” (lines 150-152) seems an oversimplification that may not be always true. I would also extend my disagreement to their assumption that all previous studies use pairwise correlations between single emotion measures and single psychological characteristics measures (lines 162-165). I think that the authors could make their point and avoid this type of overstatements.

-Lines 505 and 600. The expression “As results” is not clear in this context. Please, rephrase to “As a result” or a near synonym.

-Line 539. The correct form of the verb “remain” in “only the first CCA mode remain significant” should be either “remains” in the present or “remained” in the past.

-Line 586. A comma is missing before “have higher degrees of overall self-esteem”

-Lines 635-637. The sentence “we considered the possibility that the individuals with high variate scores tend to show ‘polarized arousal responses’ than those with low variate scores do” is missing a “more” or a “less”. Besides, the “do” at the end is not necessary

-Lines 639-641. In the sentence “Thus, if a given individual’s responses are more polarized than the normative responses, her or his responses will be not just more deviant from the normative responses but also be more exaggerated than the normative responses” the “be” in the second part can be omitted.

-Lines 668-669. The verb “is” should be deleted from the sentence: “This difference in kurtosis is resulted mainly”

-Line 793. “with those who shows” should be replaced with “with those who show”

-Lines 817-819. I would recommend to rephrase the whole sentence to enhance comprehension

-Lines 828-829. I would also recommend to rephrase the sentence “were found to make people better categorize emotions”

-Lines 845-847. The whole sentence needs to be rephrased, since either a whole clause seems to be missing or “that” should be deleted: “Initially, we found that many significant pairwise correlations including the clinical-problem measures from the questionnaires such as SCID-II, SSI Beck, BAI, TEMPS, and STAI.”

-Lines 851-857. The English should be revised here and in the whole section: e.g. “our findings indicate that the clinical-problem measures do not as strongly participate as the psychosocial measures in the canonical mode that governs the population covariation”

-Lines 916-919. The sentence needs to be rephrased, since the last part does not make any sense. It probably needs a “which” after “disorders”: “our library of visual narratives can be considered as a natural—thus ecologically valid and unobtrusive—means of detecting the emotional symptoms specific to certain psychiatric disorders, such as schizophrenia, tend to be accompanied by emotion impairment”. Besides, no sound arguments are provided for their proposal to use their library of visual narratives to detect emotional symptoms specific to psychiatric disorders, especially considering the lack of significant results provided in their study for these variables.

-I would recommend the authors to avoid the use of expressions such as “a previous study” when referring to previous results. Without introducing the specific work, the sentence sounds somewhat clumsy. The number reference to the work in the reference list can be inserted without resorting to these expressions.

-Lines 919-923. I would recommend the authors to avoid advising against the use of the type of methods they are advocating for in the present study, at least in the way they have phrased the warning: “despite the power of discovering latent structures of covariation hidden in high dimensional data sets, multivariate analysis methods have not to be exercised frequently due to the curse-of-dimensionality problem and the difficulty in interpretation”.

-Lines 929-930. I would delete “before concluding the current work” from the sentence since it impairs comprehension.

-Line 947. I would avoid referring to another age group as a different culture.

INFORMATION

-Lines 126-130. Authors could maybe provide specific references to some meta-analyses of the kind they point to.

-Lines 230-235. Further details about the protocol would be appreciated. Did participants take the 19 questionnaires home to complete them without any specific instructions on how to do it? Were they advised to fill them in at different times to avoid exhaustion? Also, the criteria to select them seem rather loose, since the explanation provided only specifies that they “were developed with different taxonomies that capture individual differences in relatively enduring behavioral tendencies from diverse theoretical and practical perspectives” (231-33).

-In relation to the questionnaires used, no information is provided as to whether the authors used the Korean version or the English one. Reference to the use of the Korean version is found in S1_Table (although not for every questionnaire), but it should also be clarified in the text.

-Lines 262-263. I cannot see the logic of including 4 music videos and 4 TV commercials in the corpus, since the numbers are hugely unbalanced in comparison with the 116 motion pictures. This unbalance should be further justified.

-Lines 271-273. I also have problems with the relevance of selecting a “coherent piece of storytelling, so that it could be readily narrated with a few sentences”, considering that all stimuli were made soundless (line 276).

-Lines 302-303. The authors affirm that they confirmed that the “stimuli covered the affective space in a representative manner”. However, no data are provided of how they did so.

JUSTIFICATION

Lines 783-831. I appreciate and value the authors’ effort to provide explanations for the link between polarized arousal responses and psychosocial factors, but their arguments lack strength and theoretical support. They refer to previous studies in the literature and also to a particular theoretical view, but the justification and links should be strengthened. For example, on lines 798-800 the authors state to consider “one influential view” based on the impact of emotion on social interaction “as a possible explanation for the tight linkage between psychosocial factors and polarized arousal responses”. However, on lines 817-819 they acknowledge that an explanation in the opposite direction is also possible: “allowing for an interpretation based on the impact of social interactions on emotion expression in the opposite direction of influence posited by the view introduced above”. Furthermore, on line 823 they mention another result that cannot be easily interpretable based on these explanations and briefly mention another line of previous work. Further than the plausibility of the explanations provided, the final impression is that possibilities are suggested as they are found, but no theoretical background or standpoint is used to justify them.

Reviewer #2: The author's work is interesting and methodologically well-done. The investigation of individual covariations between psychological and emotional (arousal and valence) constructs in visual narratives is fascinating, and a needed step in the field of human function dimensions. Moreover, this understanding can be applied to many populations, such as mood and anxiety disorders. While the manuscript is intelligible, my main comments are larger, broad suggestions to improve the formatting (some focus) and readability of the manuscript.

Below I have outlined points that would improve the manuscript:

1) The abstract needs to be rewritten and model the formatting of typical abstracts. It lacks details from the methods (participant information) and more specific details of the results, and their interpretation. The abstract is purpose or "introduction" heavy. I finished reading it unsure of what the project actually did and found.

2) The manuscript is generally well written. My concern is that the manuscript reads, and is formatted, closer to that of a thesis or dissertation paper, rather than a targeted journal article (e.g., how the introduction starts; many details in the methods could be supplemental – like power and sample size).

3) The Figure captions being included within the body of the manuscript might be allowed for the journal submission, but they would be better suited in a "Figure Caption" section if able.

4) As for the figures, reconsider what items are necessary and those that are not (e.g., Fig 1 – A, B, C, D and F do not seem necessary to me; Fig 5 - skewness, kurtosis etc is not needed here). Demographics can be tabled, and would be easier to understand. There should be more room dedicated to visualizing your data/results.

5) One quick statement in the discussion about how the work could be utilized in populations like schizophrenia opens the door for a very important application that the current work should highlight more. Performing this work in mood and anxiety disorders is a natural next step that is not discussed in the future work section at the end of the discussion. It should be, and projected findings even suggested.

**********

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Reviewer #1: Yes: Ana María Rojo López

Reviewer #2: No

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10 Nov 2021

We thank both reviewers for the highly constructive comments, which provided us with an opportunity to improve our manuscript's intelligibility and readability. In below, we briefly summarize the gist of our revision.

Reviewer #1 systematically sorted a total of 26 comments into four different issues, “language and style (18 comments)”, “information (6 comments)”, “justification (1 comment)”, and “benefits of the multivariate approach (1 comment)”. Since we found that all the comments were written clearly and had relevant points, we have incorporated all of them into the revised manuscript. To list major revision points, we (i) substantially revised INTRODUCTION to avoid “oversimplification”, “overgeneralization”, and unnecessary negative attitude toward previous studies as for “language and style”; (ii) added the further details of our protocol, such that our study can be replicated based on our description, as for “information”; (iii) substantially revised DISCUSSION to indicate explicitly and exactly what aspects of findings might be explained by which theoretical viewpoints respectively, as for “justification”; (iv) further specified the benefits of the multivariate approach based on what was demonstrated in the current work, as for the last comment.

Reviewer #2’s comments (a total of 5 comments) were mostly about the format and readability of the manuscript. We agree with all these comments and incorporated them into the revised manuscript except for one regarding the location of figure captions (we could not do that due to the PLOS ONE format guideline). Consequently, Abstract and Fig 1 were substantially revised, INTRODUCTION and DISCUSSION were modified, and two Methods sections were now presented as Supplementary Appendices.

For an effective presentation and cross-referencing of our replies to the two reviewers’ comments, we labeled each of them with a format [C#-#-#], where #s represent the identity of a reviewer, the category of the comment, and the order of the comment within that category. To distinguish between the comments and our replies, italic fonts were used for the comments.

Replies to Reviewer #1’s comments

[Overall comment]: “The article proposes a method to analyze individual differences in emotion response and explore the systematic structures underlying such response. The authors introduce justification of the existence of such differences or variation and explain the parameters of their analysis and the limitations of previous analyses. In turn, they propose a data-driven approach based on multivariate analyses allowing to identify significant across-individual covariations between the domain of emotion response and that of psychological characteristics. However, in its current form, the paper still has some weaknesses that should be addressed before publication.”

Our reply to [Overall comment]: We thank Reviewer #1 for correctly and succinctly summarizing the core of our work presented in the submitted manuscript. We agree with Reviewer #1 that the previous version of the manuscript “has some weaknesses that should be addressed” and did our best to address those weaknesses in the revised manuscript.

[C1-1] General comment on the issue of “language and style”: “Firstly, the English needs to be revised to improve intelligibility and eliminate some errors mostly found in the second part of the paper as compared with the first part (see below for specific examples). I would also recommend the authors to avoid criticisms that may sound like overgeneralizations and simplifications (see below for specific examples). One of their major claims is that their method is more powerful than previous analyses based on pairwise comparisons. Everyone who has worked with pairwise comparisons is probably aware of the fact that they can vary depending on the number of factors analyzed. However, even if the multivariate method proposed is certainly more powerful, results also depend on the number of factors introduced. There is, therefore, no need to be too negative about previous approaches; the authors could still emphasize the advantages of their method and recognize the value of previous ones together with their shortcomings.”

Our reply to [C1-1]: We admit that there were many errors in the previous manuscript. In the revised manuscript, we got rid of those grammatical errors and rewrote several sentences, including those pointed out by the reviewer, to improve readability. We greatly appreciate the reviewer’s time and effort to detect specific errors and make suggestions for improvement. Moreover, we also agree with the reviewer that we were “too negative about previous approaches” in the previous manuscript. We rewrote an entire paragraph of Introduction and several other sentences or phrases throughout the manuscript to incorporate the reviewer’s suggestion for addressing this problem (“… emphasize the advantages of their method and recognize the value of previous ones together with their shortcomings.”). For details, please see below for our replies to the reviewer’s specific comments and the corresponding revisions.

[C1-1-1~18] Specific comments on the issue of “language and style”

[C1-1-1]: Lines 105-108. The first part of the sentence requires a verb: “The variability of emotion refers to how inconsistent an individual’s emotion responses to the same event or highly similar events and…”

Reply to [C1-1-1]: We revised the sentence accordingly (see lines 99-102 in the revised manuscript).

[C1-1-2]: Lines 118-120. I would recommend the authors to modulate and soften the criticism against “all the previous studies”, unless they positively know that they all tested “predictions of interest”. Their assumption that testing particular theories or hypotheses is necessarily negative because it leads authors to focus on “a few pre-selected measures” (lines 150-152) seems an oversimplification that may not be always true. I would also extend my disagreement to their assumption that all previous studies use pairwise correlations between single emotion measures and single psychological characteristics measures (lines 162-165). I think that the authors could make their point and avoid this type of overstatements.

Reply to [C1-1-2]: Although we did not intend to claim that “all the previous studies (that have studied the relationship between the two domains)” tested only “predictions of interest” in the previously submitted manuscript, we admit that the way we contrasted the “hypothesis-driven and regional” approach and the “data-driven and global” approach gave an unnecessary impression of “oversimplification” and “overstatements.” To address this problem, we substantially revised the 5th and 6th paragraphs of Introduction by precisely specifying what previous studies were referred to whenever we refer to “previous studies” (e.g., lines 107, 113) and by avoiding any unnecessary (oversimplified or overstated) contrast with “previous studies” (lines 107-130).

[C1-1-3]: 1-3 Lines 505 and 600. The expression “As results” is not clear in this context. Please, rephrase to “As a result” or a near synonym.

Reply to [C1-1-3]: We searched the entire manuscript for the wrong use of “as results”, including those detected by the reviewers, and replaced with “as a result” (lines 296, 377,

436, 531).

[C1-1-4]: Line 539. The correct form of the verb “remain” in “only the first CCA mode remain significant” should be either “remains” in the present or “remained” in the past.

Reply to [C1-1-4]: “remain” has been replaced with “remains” (line 470).

[C1-1-5]: Line 586. A comma is missing before “have higher degrees of overall self-esteem”.

Reply to [C1-1-5]: A comma has been added (lines 517-518).

[C1-1-6]: Lines 635-637. The sentence “we considered the possibility that the individuals with high variate scores tend to show ‘polarized arousal responses’ than those with low variate scores do” is missing a “more” or a “less”. Besides, the “do” at the end is not necessary

Reply to [C1-1-6]: The whole sentence has been corrected for those errors (lines 566-568).

[C1-1-7]: Lines 639-641. In the sentence “Thus, if a given individual’s responses are more polarized than the normative responses, her or his responses will be not just more deviant from the normative responses but also be more exaggerated than the normative responses” the “be” in the second part can be omitted.

Reply to [C1-1-7]: “be” has been deleted (lines 570-572).

[C1-1-8]: Lines 668-669. The verb “is” should be deleted from the sentence: “This difference in kurtosis is resulted mainly”

Reply to [C1-1-8]:]: “is” has been deleted (lines 599-601).

[C1-1-9]: Line 793. “with those who shows” should be replaced with “with those who show”

Reply to [C1-1-9]: “shows” has been replaced with “show” (line 722).

[C1-1-10]: Lines 817-819. I would recommend to rephrase the whole sentence to enhance comprehension

Reply to [C1-1-10]: This phrase (“… thus allowing for an interpretation based on the impact of social interactions on emotion expression in the opposite direction of influence posited by the view introduced above …”) has been deleted to address one of the reviewer’s other comments regarding the “justification” issue. For the details of revision, see our Reply to [C1-3] below.

[C1-1-11]: Lines 828-829. I would also recommend to rephrase the sentence “were found to make people better categorize emotions”

Reply to [C1-1-11]: The sentence has been rephrased as follows: “In line with this possibility, it has been reported that reading stressful stories tends to make people better categorize emotions” (lines 765-767).

[C1-1-12]: Lines 845-847. The whole sentence needs to be rephrased, since either a whole clause seems to be missing or “that” should be deleted: “Initially, we found that many significant pairwise correlations including the clinical-problem measures from the questionnaires such as SCID-II, SSI Beck, BAI, TEMPS, and STAI.”

Reply to [C1-1-12]: “that” has been deleted (lines 783-785).

[C1-1-13]: Lines 851-857. The English should be revised here and in the whole section: e.g. “our findings indicate that the clinical-problem measures do not as strongly participate as the psychosocial measures in the canonical mode that governs the population covariation”

Reply to [C1-1-13]: The last three sentences of this section have been revised, as follows: “To be sure, we do not insist that those pairwise comparisons are inappropriate. They served the main purpose of the previous work, which was to verify specific hypothesis-driven predictions. Our findings suggest that the contribution of clinical-problem measures to the population covariation between the emotion-response and psychological-characteristics domains is not as strong as the psychosocial measures.” (lines 789-794)

[C1-1-14]: Lines 916-919. The sentence needs to be rephrased, since the last part does not make any sense. It probably needs a “which” after “disorders”: “our library of visual narratives can be considered as a natural—thus ecologically valid and unobtrusive—means of detecting the emotional symptoms specific to certain psychiatric disorders, such as schizophrenia, tend to be accompanied by emotion impairment”. Besides, no sound arguments are provided for their proposal to use their library of visual narratives to detect emotional symptoms specific to psychiatric disorders, especially considering the lack of significant results provided in their study for these variables.

Reply to [C1-1-14]: We understood the reviewer’s point and admitted that the sentences in the previous manuscript sounded conflicting with no significant contribution of clinical-problem measures to the CCA mode (, which was summarized in the Discussion section titled “A negligible contribution of clinical problems to the population covariation”). However, this result was obtained from non-patient people, and our VN stimuli can still be considered in translational research on emotion deficit in people with mental diseases. In the revised manuscript, we explicated pointed out this possibility (lines 851-857).

[C1-1-15]: I would recommend the authors to avoid the use of expressions such as “a previous study” when referring to previous results. Without introducing the specific work, the sentence sounds somewhat clumsy. The number reference to the work in the reference list can be inserted without resorting to these expressions.

Reply to [C1-1-15]: We agree with and appreciate the reviewer’s recommendation. Accordingly, we searched the entire manuscript for such “clumsy” uses of “a previous study” and revised them as recommended.

[C1-1-16]: Lines 919-923. I would recommend the authors to avoid advising against the use of the type of methods they are advocating for in the present study, at least in the way they have phrased the warning: “despite the power of discovering latent structures of covariation hidden in high dimensional data sets, multivariate analysis methods have not to be exercised frequently due to the curse-of-dimensionality problem and the difficulty in interpretation”.

Reply to [C1-1-16]: We agree with and appreciate the reviewer’s recommendation. Accordingly, the sentence was revised as follows: “… the current work showed that CCA becomes even more powerful when it is used in conjunction with another multivariate analysis that compresses high dimensional data into a low dimensional space such as PCA, which allowed us to back-project …” (lines 864-868).

[C1-1-17]: Lines 929-930. I would delete “before concluding the current work” from the sentence since it impairs comprehension.

Reply to [C1-1-17]: “before concluding the current work” has been deleted (lines 872-873).

[C1-1-18]: Line 947. I would avoid referring to another age group as a different culture.

Reply to [C1-1-18]: We agree with and appreciate the reviewer’s recommendation. Accordingly, the sentence has been revised as recommended (lines 888-889)

[C1-2] General comment on the issue of “information”: “ Secondly, even if research meets all applicable standards for the ethics of experimentation and the statistical analysis seems to be described in sufficient detail, there are still details from the experiment that should be provided to clarify the protocol, especially those referring to the criteria to select stimuli and materials (specific examples are also provided below).”

Reply to [C1-2]: Thanks to the reviewer’s comments, we now realize that the previous manuscript did not provide sufficient specific details regarding the experimental protocol, including those pointed out by the reviewer. In the revised manuscript, we added further details of our protocol such that our study can be replicated based on our description. For details, see our replies to the reviewer’s specific comments below.

[C1-2-1~6] Specific comments on the issue of “information”

[C1-2-1]: Lines 126-130. Authors could maybe provide specific references to some meta-analyses of the kind they point to.

Reply to [C1-2-1]: To address one of the reviewer’s comments on the issue of language and style ([C1-1-2]), we substantially revised the paragraphs in which the sentences dealing with “meta-analyses” were originally included (see Reply to [C1-1-2] for details). In doing so, we decided not to bring up the issue related to “meta-analyses.”

[C1-2-2]: Lines 230-235. Further details about the protocol would be appreciated. Did participants take the 19 questionnaires home to complete them without any specific instructions on how to do it? Were they advised to fill them in at different times to avoid exhaustion? Also, the criteria to select them seem rather loose, since the explanation provided only specifies that they “were developed with different taxonomies that capture individual differences in relatively enduring behavioral tendencies from diverse theoretical and practical perspectives” (231-33).

Reply to [C1-2-2]: As requested by the reviewer, we provided further details about the protocol of acquiring the psychological-characteristics measures (lines 182-206). Specifically, we detailed how participants were instructed to complete the questionnaires (lines 200-206) and how we selected the questionnaires by specifying the types and contents of the three taxonomies used in the current work (lines 185-196).

[C1-2-3]: In relation to the questionnaires used, no information is provided as to whether the authors used the Korean version or the English one. Reference to the use of the Korean version is found in S1_Table (although not for every questionnaire), but it should also be clarified in the text.

Reply to [C1-2-3]: As recommended by the reviewer, we’ve explicitly stated that the Korean version of the questionnaires was used (lines 198-199).

[C1-2-4]: Lines 262-263. I cannot see the logic of including 4 music videos and 4 TV commercials in the corpus, since the numbers are hugely unbalanced in comparison with the 116 motion pictures. This unbalance should be further justified.

Reply to [C1-2-4]: In the revised manuscript, we first further specified the numbers of VN stimuli for each type of source (130 clips from 124 different motion pictures, 7 clips from 4 different music videos, and 7 clips from 4 different TV commercials; lines, 221-225), explained why we ended up excerpting 14 clips by referring to these non-motion-picture sources (lines 222-225), and justified why we think it does not matter to include those clips for the purpose of the current work (lines 225-228), and carried out additional analysis to show that the emotion-response measures remained almost identical regardless of whether those non-motion-picture clips were included or not in the VN library (lines, 228-230; S2 Fig in the revised manuscript).

[C1-2-5]: Lines 271-273. I also have problems with the relevance of selecting a “coherent piece of storytelling, so that it could be readily narrated with a few sentences”, considering that all stimuli were made soundless (line 276).

Reply to [C1-2-5]: We regret that we used the phrase “it could be readily narrated” in the previous manuscript. Our intention was to specify one of the criteria for selecting clips for the VN stimuli, which was that each clip describes a story that can be understood without sound information. In the revised manuscript, we replaced “it could be readily narrated” with “could be readily described” and added a sentence describing our intention to use this criterion (lines 216-218).

[C1-2-6]: Lines 302-303. The authors affirm that they confirmed that the “stimuli covered the affective space in a representative manner”. However, no data are provided of how they did so.

Reply to [C1-2-6]: As recommended, we provided the ground for the statement (“stimuli covered the affective space in a representative manner”) in the revised manuscript. Specifically, we referred to two previous studies which show how affective states are typically distributed in the affective space and indicated that a similar pattern of distribution was observed in the current data by referring to Fig 1A, B in the revised manuscript (lines 250-254).

[C1-3] Comment on the issue of “justification”: “Thirdly –and this is actually one of my major concerns– the discussion of results somehow lacks rigor and theoretical justification. Even if I understand that the data-driven approach adopted aims at avoiding theoretical bias, plausible explanations for the findings should be soundly justified and grounded in the literature; or alternatively, they could be provided just as hypotheses to be verified in future studies.”

“Lines 783-831. I appreciate and value the authors’ effort to provide explanations for the link between polarized arousal responses and psychosocial factors, but their arguments lack strength and theoretical support. They refer to previous studies in the literature and also to a particular theoretical view, but the justification and links should be strengthened. For example, on lines 798-800 the authors state to consider “one influential view” based on the impact of emotion on social interaction “as a possible explanation for the tight linkage between psychosocial factors and polarized arousal responses”. However, on lines 817-819 they acknowledge that an explanation in the opposite direction is also possible: “allowing for an interpretation based on the impact of social interactions on emotion expression in the opposite direction of influence posited by the view introduced above”. Furthermore, on line 823 they mention another result that cannot be easily interpretable based on these explanations and briefly mention another line of previous work. Further than the plausibility of the explanations provided, the final impression is that possibilities are suggested as they are found, but no theoretical background or standpoint is used to justify them.”

Reply to [C1-3]: We thank the reviewer for appreciating our effort to offer some explanations for the results although the current work, as a data-driven study, was not designed to test specific theories or hypotheses. At the same time, we concur with the reviewer that we still need to offer a set of coherent explanations or, at least, do not seem to conflict with one another. We do not think that any single theoretical view can offer a unified account for the CCA mode observed in the current work. We believe that the across-individual covariation structure governing the relationship between the emotion-response and psychological-characteristics domains is likely to be complex in nature and entails diverse, multiple factors. Guided by this belief, we took the strategy of offering a primary explanation (the account based on “social sharing of emotions”) for the most prominent relationship (covariation structure between polarized arousal responses and psychosocial-asset groups of measures) and then offering a secondary explanation (the account based on “stressful life events”) for the (minor) relationship (covariation structure between polarized arousal responses and the negative-experience measures). We do not think that these two explanations conflict with each other because they account for two different aspects of the CCA mode found in the current work.

Having clarified our perspective on this issue, we regret that three different aspects of our findings (and possible accounts for those aspects) were provided under a single section titled - rather biasedly - “Association between psychosocial factors and polarized arousal response” in the previously submitted version of the manuscript. This contributed to the impression that several incoherent explanations are offered to one single aspect of the findings. Thus, to address this problem in our revised manuscript, we dissected the original section into three sections with respective titles indicating explicitly and exactly what aspects of findings are addressed and offered the corresponding accounts, respectively (lines 713-769).

[C1-4]: Comment on “benefits of using the multivariate approach”

“Finally, despite its interest and value, the method proposed seems too complex and time consuming when comparing effort vs results. The benefits of using the method could be further outlined to make it worthwhile.”

Reply to [C1-4]: We agree with and appreciate the reviewer’s suggestion. Accordingly, we further specified the benefits of the multivariate approach based on what was demonstrated in the current work (lines 857-868). (By the way, although our method (CCA+PCA) may look complicated, it can be simply considered as an extension of the Pearson correlation analysis. In addition, we believe one can readily apply our method to multivariate datasets using our MATLAB codes available in the OSF repository.)

Replies to Reviewer #2’s comments

[Overall comment]:“The author's work is interesting and methodologically well-done. The investigation of individual covariations between psychological and emotional (arousal and valence) constructs in visual narratives is fascinating, and a needed step in the field of human function dimensions. Moreover, this understanding can be applied to many populations, such as mood and anxiety disorders. While the manuscript is intelligible, my main comments are larger, broad suggestions to improve the formatting (some focus) and readability of the manuscript.”

Reply to [Overall comment]: We thank the reviewer for appreciating the importance and contribution of our work. We mostly concurred with the reviewer’s suggestions and incorporated them into the revised manuscript accordingly. For details, see our replies to the reviewer’s specific comments.

[C2-1]: Comment on “Rewriting the abstract”

“The abstract needs to be rewritten and model the formatting of typical abstracts. It lacks details from the methods (participant information) and more specific details of the results, and their interpretation. The abstract is purpose or "introduction" heavy. I finished reading it unsure of what the project actually did and found.”

Reply to [C2-1]: As correctly pointed by the reviewer, we admit that the previous abstract failed to hit the balance between the “introductory”, “methodological”, and “results-summarizing” parts. In the revised abstract, we substantially cut the introductory part and provided more details about Methods and Results to help readers readily understand what our work is trying to address, how it does so, and what we eventually found (lines 20-47).

[C2-2]: Comment on “Format”: “The manuscript is generally well written. My concern is that the manuscript reads, and is formatted, closer to that of a thesis or dissertation paper, rather than a targeted journal article (e.g., how the introduction starts; many details in the methods could be supplemental – like power and sample size).”

Reply to [C2-2]: We agree with the reviewer’s points about the style of the previously submitted manuscript. As suggested, we deleted the starting part of Introduction, which sounds too general (lines 50-57), and presented two Methods sections ("power and sample size" and "De-confounding the CCA from the overall tendency of making extreme reports") as Supplementary Appendices in the revised manuscript.

[C2-3]: Comment on “figure caption section”: “The Figure captions being included within the body of the manuscript might be allowed for the journal submission, but they would be better suited in a "Figure Caption" section if able.”

Reply to [C2-3]: We felt the same way with the reviewer that “The Figure captions being included within the body of the manuscript” is distractive. However, unfortunately, this is one of the format requirements imposed by PLOS ONE. We will appreciate your understanding on this matter.

[C2-4]: Comment on “the figures”: “As for the figures, reconsider what items are necessary and those that are not (e.g., Fig 1 – A, B, C, D and F do not seem necessary to me; Fig 5 - skewness, kurtosis etc is not needed here). Demographics can be tabled, and would be easier to understand. There should be more room dedicated to visualizing your data/results.”

Reply to [C2-4]: To incorporate the reviewer’s point, which we agree with, in the revised manuscript, we carefully reconsidered what figure panels are necessary for readers to understand the important points of our work and justified our decisions as follows. As for Fig 1, we decided to remove the old Fig 1A and present it as Table (Table 1 in the revised manuscript) because the demographic information can be more specific in a Table format. We also decided to remove Fig 1B, Fig 1C, and Fig 1F because the information depicted in those panels is sufficiently available in the text. However, we decided to keep the old Fig 1D (, which is Fig 1A in the revised manuscript) because we need this figure to illustrate how the average emotion responses to the VN stimuli were distributed in the affective space, which was necessary to address one of the comments made the other reviewer (see [C1-2-6] in above and our reply to that). As for Fig 5, we decided to keep the table of skewness and kurtosis values. This decision was made because we think those values would help readers appreciate how the emotion responses are distributed differently between the two groups, which is crucial for characterizing the CCA mode of variation (Polarized arousal responses in the EM-high group), by providing quantitative indices of distribution shapes. Another reason to juxtapose those values with their corresponding bins of arousal values is to allow readers to appreciate the fact that “Polarized arousal responses” are expressed in terms of skewness for the extreme-value bins (e.g., nr<-2, 2

[C2-5]: Comment on “application of the current work”: “One quick statement in the discussion about how the work could be utilized in populations like schizophrenia opens the door for a very important application that the current work should highlight more. Performing this work in mood and anxiety disorders is a natural next step that is not discussed in the future work section at the end of the discussion. It should be, and projected findings even suggested.”

Reply to [C2-5]: We thank the reviewer for bringing up an important point. To address this point, we reviewed the translational research on emotional deficits in schizophrenia and anxiety-and-mood disorders and attempted to suggest possible ways of applying our approach to those clinical populations in the revised manuscript (lines 893-904).

Submitted filename: KimEtal_Revised_Response to Reviewers.docx

Click here for additional data file.

28 Jan 2022

A robust multivariate structure of interindividual covariation between psychosocial characteristics and arousal responses to visual narratives

PONE-D-21-14011R1

Dear Dr. Lee,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: No

7 Feb 2022

PONE-D-21-14011R1

A robust multivariate structure of interindividual covariation between psychosocial characteristics and arousal responses to visual narratives

Dear Dr. Lee:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

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