Haiku poetry, a short poem with a clear form, has the potential to elucidate many unknown aspects of linguistic art. Previous studies on haiku appreciation have shown that negative emotion and cognitive ambiguity reduce aesthetic evaluation. Considering the importance of negative emotions and ambiguity in art, it is beneficial to clarify the process of emotional and cognitive changes during aesthetic evaluation. This study examined the temporal effects of emotional and cognitive changes on aesthetic evaluation from multiple perspectives by measuring the ratings of each section of haiku, continuous emotional reports, and physiological changes in pupil diameter. The 112 students first rated the haiku at three time points on items such as beauty, valence, and ambiguity. Next, they rated the same haiku continuously for 20 seconds using a joystick for valence and arousal during which the pupil diameter was measured. The results showed that a decrease in negative emotions and ambiguity explained the beauty of the haiku. In the continuous emotion reports, positive emotions gradually increased for positive haiku and negative emotions gradually increased for negative haiku, while arousal decreased once and then gradually increased for both forms of haiku. Additionally, an increase in pupil diameter also explained the beauty. The roles of negative emotions and ambiguity were revealed by focusing on both subjective and physiological indicators of emotional and cognitive changes during haiku appreciation. This study has contributed to the advancement of our understanding of linguistic art forms by empirically exploring conscious and unconscious emotional and cognitive responses to haiku.
Research Article
Effects of Emotional and Cognitive Changes on Aesthetic Evaluation of Poetry Based on Subjective and Physiological Continuous Responses with Pupil Diameter Measurement
https://doi.org/10.21203/rs.3.rs-2713826/v1
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Beauty of Haiku Poetry
Haiku poetry is the world’s shortest poetic form with 5-7-5 morae and includes a seasonal word (Iida, 2008). In haiku, one or two images are composed, and a breakpoint called a kire (cut)[1] is efficiently used to integrate the two images into a work (Blasko & Merski, 1998). While the Japanese are naturally comfortable with the 5-7-5 rhythm (Niikuni et al., 2022), rhymes are not common in haiku. With its controlled format and clear rules, haiku poetry has the potential to explore the frontier of linguistic art, which has many unexplored elements compared to the accumulated research in visual and auditory arts (cf., micropoem (Hugentobler & Lüdtke, 2021; Jacobs, 2015). Haiku poets have suggested a relationship between the emotions and cognition evoked by the beauty of haiku (Kishimoto, 2008), with many empirical studies conducted in this regard in recent years. In terms of emotions, positive emotions (Belfi et al., 2018; Hitsuwari & Nomura, 2022a) and mixed emotions, such as awe and nostalgia (Hitsuwari & Nomura, 2022b) explain the beauty of haiku. In terms of cognition, imagery vividness explains the aesthetic appeal of haiku (Belfi et al., 2018; Hitsuwari & Nomura, 2022a), and cognitive ambiguity has been found to reduce the appeal of haiku (Hitsuwari & Nomura, 2022b). However, these results appear to contradict the high evaluation of grief haiku (Imase et al., 2010) and the importance of ambiguity in haiku and poetry (Nitta, 2016; Obermeier et al., 2016). Indeed, these findings are insufficient, given that negative emotions (Menninghaus et al., 2017) and the resolution of ambiguity (Muth & Carbon, 2013) have repeatedly been shown to contribute to aesthetic experiences in visual and auditory arts. Consideration of the processes involved in alleviating negative emotions, as well as the increment of positive emotions and resolution of ambiguity that explain the uniqueness of the haiku and its commonality with previous findings, are required. Therefore, this study aimed to provide a deeper understanding of the beauty of haiku by focusing on the process of emotional and cognitive changes during appreciation.
Emotional and Cognitive Changes During Haiku Appreciation
Several process models of emotion and cognition during art appreciation have recently been developed (Leder & Nadal, 2014; Leder et al., 2004; Menninghaus et al., 2017; Pelowski et al., 2017). For example, in Menninghaus et al.’s (2017) model, negative art appreciation can be enhanced by the distancing element, which creates a foundation for enjoying negative emotions by forming psychological distance from art, and the embracing element, which actively integrates, assimilates, and adopts the power of negative emotions to improve art appreciation. Embracing elements emphasize the interplay between positive and negative emotions and the role of mixed positive/negative emotions, suggesting gradual alleviation of strong negative emotions. Emotions of sadness when experiencing music have been demonstrated to lead to enjoyment, a relationship mediated by being moved, which is a mixed positive-negative emotion (Vuoskoski & Eerola, 2017). This suggests that changes in negative and positive emotions are important in the art experience. Another process model (Pelowski et al., 2017) states that viewers can make several interpretations and thoughts when there is low relevance to the self and low schema consistency in the top-down cognitive process. However, as they continue to view the artwork, they encounter information that resolves the discrepancy and may lead to insight with aesthetic evaluation. In support of this process, experiments have captured the association between ambiguity and beauty at more than one point in time. Muth and Carbon (2013) showed ambiguous images repeatedly for 500 ms each and asked participants to repeat their ratings of liking and clarity. They found that the largest increase in liking occurred immediately after the resolution of ambiguity (i.e., insight) occurred. Thus, it is important to pay attention to the emotional and cognitive processes of art appreciation. By evaluating haiku poems with a clear 5-7-5 structure, it is possible to examine the impact of emotional and cognitive changes on beauty. Haiku is an ideal subject for examining this process of change due to its controlled shortness, but the expected results could be extended to other poetic forms and art genres.
Continuous Response
In this study, we examined the emotional and cognitive change processes described above by asking participants to evaluate haiku in line 1, line 2, and line 3[2] one by one while introducing a method to measure the change processes more precisely. Emotional processes, such as the perception and expression of emotional states and social signaling, can be acquired live by manipulating joysticks (e.g., Girard & Wright, 2018; Sharma et al., 2020). Although this has been a flourishing approach in the study of aesthetic appreciation of temporal art, especially music (e.g., Bachorik et al., 2009; Grewe et al., 2007; Nagel et al., 2007), it also appears that continuous self-rating of spoken poetry[3] does not affect physiological responses or post-appreciation ratings, indicating that this kind of continuous rating during appreciation is also possible in poetry (Wagner et al., 2021). In this study, participants reported their emotions using a joystick as the haiku was gradually presented on the two dimensions of valence and arousal used in previous studies (Bliss-Moreau et al., 2020; Russell, 1980; Sharma et al., 2020 for practical examples using joysticks).
Pupil Diameter
Eye movement has been measured in poetry appreciation studies to capture temporal and unconscious responses (Blohm et al., 2022; Menninghaus & Wallot, 2021). In particular, studies using haiku, eye-movement measurements revealed that people appreciate haiku differently depending on the presence and position of the kire (cut)[4] and the type of haiku (Geyer et al., 2020; Hermann et al., 2017). Research measuring unconscious cognitive responses using pupil diameter is common, with the technique used in the psychology of aesthetics for painting and music (e.g., Kuchinke et al., 2009; Laeng et al., 2016). For example, Kuchinke et al. (2009) found a positive relationship between pupil size and liking ratings of cubist paintings. As such, this technique can be used as an emotional indicator or, as shown below, a cognitive indicator. That is, pupil diameter broadly reflects cognitive load and processing effort (Piquado et al., 2010). For example, pupil diameter has also been employed to appreciate linguistic art. Scheepers et al. (2013) found that pupils respond strongly to rhyme violations in limericks, suggesting that emotions elicited by rhyme violations were one possible factor for such a reaction. Other studies have demonstrated that the more ambiguity one perceives, the larger the pupil diameter (Graves et al., 2021; Kadem et al., 2020) or that pupil diameters greatly expand just before insight (Salvi et al., 2020). However, the role of pupil diameter is unclear during aesthetic and ambiguity evaluations of haiku. By examining time-series changes in pupil diameter in conjunction with subjective reports from joysticks, we could explore changes in emotion and cognition in haiku appreciation.
Aims
Given the limitations of previous studies that only captured cognition and emotion during poetry appreciation at a single time point, this study examined the multidimensional effects of changes in cognition and emotion on haiku evaluation by combining haiku ratings in steps, continuous ratings using a joystick and pupil diameter measurements. In particular, the role of negative emotion, positive emotion, and the resolution of ambiguity in haiku had not been clarified previously, but it is expected that these factors will increase the aesthetic evaluation of haiku. In addition, because pupil diameter positively correlates with a higher liking of art (Kuchinke et al., 2009) and with ambiguity (Graves et al., 2021), we exploratorily investigated whether pupil size may predict the evaluation of beauty or reflect an ambiguity resolution mechanism.
[1] “a break point or gap between two (at first glance) often seemingly disparate images, or parts, of the haiku.” (Geyer et al., 2020, p. 4)
[2] In English, haiku are three-line poems, each part of which is described as line 1, line 2, and line 3, whereas in Japanese, haiku are often written as one-line poems, with each part being described as kamigo (top five), nakashichi (middle seven), and shimogo (bottom five).
[3] While Wagner et al. (2021) used spoken poetry, this study used written poetry. Considering the findings in neuroscience that language comprehension and semantic processing in reading and listening are independent of the form of input (Booth et al., 2002; Buchweitz et al., 2009), it is unlikely that more interference is occurring in the present paradigm than in previous studies (Wagner et al., 2021).
Ethics Approval
This study was approved by the Ethics Committee of Kyoto University (CPE-429). All data, materials, and scripts are available online (https://osf.io/x64hu/?view_only=2420185c367f41d6a10c1a6137021ce9https://osf.io/).
Participants
The sample size was 113 students, based on previous research on sample sizes for hierarchical and repeated measures data (Arend & Schäfer, 2019). In this sample size estimation method, direct effects of level 1 and level 2 and intra-class correlation coefficients among linear mixed models are incorporated into the model and calculated using the SIMR package (Green & Macleod, 2016) of R (the R scripts used for the estimation are stored on the OSF project page). One hundred and thirteen undergraduate and graduate students from Kyoto University, whose native language is Japanese, were recruited via an electronic notice board. Given the nature of the pupil measurement device and joystick operation, only students who were right-handed and did not wear glasses were included. One student was excluded entirely due to missing data, and ultimately the data of 112 students (Mage = 20.92 SDage = 2.55, 53 men and 59 women) were used in the analysis. In addition, pupil diameter data could not be analyzed for 17 participants due to a programming error (lack of time in the measurement program). Therefore, pupil diameter data were analyzed only for 95 students.
Materials
The stimuli consisted of 172 haiku poems used in previous studies (Hitsuwari & Nomura, 2022a; 2022b). In those two previous studies, the valence of each haiku was measured on a visual analog scale (VAS) scale of 0 to 100. Out of the pool of 172 haiku poems, 30 were selected as being more positive (Mvalence = 69.27, SD = 4.20, Range = 64.82–79.06) and 30 as more negative (Mvalence = 35.95, SD = 5.01, Range = 26.59–44.92). A total of 60 haiku were evaluated by 11 participants who did not participate in the main experiment. Haiku that were too cryptic or evoked heightened negativity or abhorrence were eliminated; therefore, 48 haiku (24 positives and 24 negatives) were used in the main experiment. The haiku were randomly divided into two lists that included half of the positive haiku and half of the negative haiku. Participants only rated one list.
Procedure
The experiment was conducted in a laboratory at the Faculty of Education, Kyoto University. Informed consent from participants was obtained prior to participation in the experiment. After explaining the experiment, participants were fitted with the ET-mono (Gazo, Co., Ltd; Nakamura et al., 2019), a wearable device that measures pupil diameter, and instructed not to move their heads significantly during the experiment. A practice session was followed by the main experiment, divided into two main components. First, participants rated each haiku at three time points (TPs)[5]: line 1, line 2, and line 3. The participants completed a 0-100 VAS rating on six items: positive emotion, negative emotion, arousal, emotional ambiguity, cognitive ambiguity, and beauty (eight items, including awe and nostalgia, were only measured at the third time poin[6]t; Figure 1A). Second, the same haiku was rated continuously using a joystick. A two-dimensional plot of valence on the X-axis and arousal on the Y-axis was displayed on the screen, and participants continuously reported their own emotions for 20 seconds. For the first five seconds, only line 1 was displayed. Five seconds later, line 2 was displayed, and again five seconds later, line 3 was displayed (Figure 1B). During continuous reporting, participants were instructed to focus their evaluation on their own emotions, not on the valence of the haiku. Participants first performed a VAS rating for one haiku, then switched to a joystick and performed a 20-second continuous emotion report for the same haiku. After participants rated one haiku, they had a 30-second break before the next haiku was presented to prevent emotional carryover. A maximum of a 3-minute break was also allowed after the evaluation of half of the haiku (12 haiku). Pupil diameters were recorded continuously from the beginning to the end of the experiment and were analyzed primarily during the continuous emotion report with the joystick. Finally, after leaving the laboratory, participants completed an online questionnaire that included demographic information, haiku familiarity (haiku_fam), haiku/art experience (haiku_ex / art_ex), and individual traits[7]. Although individual traits were not the subject of this study, Supplemental Tables 1 and 2 shows the correlations between these descriptive statistics and beauty. The experimental program was created and executed using Psychopy ver. 3 (2020.2.3; Peirce et al., 2019), and the questionnaire was created in Qualtrics.
Data Analysis
Descriptive statistics and multilevel correlation analysis of the measured variables were first performed. In particular, we focused on the correlation between each variable and beauty and the correlation between cognitive ambiguity and pupil diameter. Second, the hierarchical Bayesian model explaining beauty TP3 was conducted using the Bayesian regression models using stan (brms) package (Bürkner, 2017) with participants, the haiku, and their respective independent variables for random intercepts and slopes[8]. For example, in the case where the change in positive emotion is the independent variable:
I used the beauty TP 3 as the dependent variable because it is the form of haiku that is completed at time point 3, and the one that we usually appreciate. Convergence was confirmed as it was less than 1.10 (Brooks & Gelman, 1998), and the significance of the effect of each variable was determined by 95% posterior credible intervals (CIs) not containing zero. For the joystick and pupil diameter time-series data, 20 s of data at 60 and 120 frames per second (fps), respectively, were used. These were compressed (downsampled) to one-tenth and one-twentieth of their original size, respectively, and aligned to a timestamp of 120. [9]Following previous studies that used joysticks and pupil diameter (Takano & Nomura, 2022), we used smoothed trend models with second-order differencing for time-series analysis (See also Taoka & Kusumi, 2021 for a psychological experiment using the similar analysis). This model is one of the state-space models of Bayesian updating applied to time-series analyses suitable for capturing gradual changes and fluctuations between time points. The state-space model assumes the existence of invisible states and consists of: (1) a state equation representing the change in state and (2) an observation equation representing the process by which the observed values are obtained (Durbin & Koopman, 2012). The smoothed trend model is represented by the following two equations[10].
The differences of differences are assumed to follow the normal white noise ζt (with an expected value of 0 and variance of σ2). This study mainly focused on visual observation of the time-series plots estimated by the model and the parameter of fluctuation in the amount of change (magnitude of change in the drift component). In addition, analyses using the conventional linear mixed model and time-series plots of observed data were also performed to confirm no significant differences in the results (see supplementary file). Analyses were mainly conducted using R software (ver. 4.1.0; R Core Team, 2021) and Rstan (ver. 2.21.3; Stan Development Team, 2021). For multilevel correlation, HAD (ver. 17.20; Shimizu, 2016) was used.
[5] In the following Methods and Results sections, the time point is denoted as TP. For example, beauty TP3 means the beauty score of haiku at the third time point.
[6] There are two reasons for this; one is the time constraints of the experiment. The other is that awe and nostalgia are higher-order emotions that are not a simple but complex mixture of positive or negative emotions. It was difficult to appreciate them by appreciating only one part of the haiku.
[7] Individual traits were assessed using the following: three subscales (discomfort with ambiguity (DA), moral absolutism (MA), and need for complexity and novelty (NC)) of the Multidimensional Ambiguity Attitude Scale (MAAS; Lauriola et al., 2016; Hitsuwari & Nomura, 2021 for Japanese); openness subscale of NEO Five-Factor Inventory (Costa & McCrae, 1992; Shimonaka et al., 1999 for Japanese); four subscales (personal distress (PD), empathic concern (EC), perspective taking (PT), and fantasy (FS)) of the Interpersonal Reactivity Index (IRI; Davis, 1980; Himichi et al., 2017 for Japanese); two subscales (self related (self) and others related (others)) of the Profile of Emotional Competence (PEC; Brasseur et al., 2013; Nozaki & Koyasu, 2015 for Japanese); the awe subscale of Dispositional Positive Emotion Scale (DPES_awe; Shiota et al., 2006; Nomura et al., 2021 for Japanese); the Southampton Nostalgia Scale (SNS; Routledge et al., 2008; Nagamine & Toyama, 2019 for Japanese); and for subscales )causality, attitude toward contradiction (contradiction), perception of change (change), locus of attention (attention)) of the Analysis-Holism Scale (AHS; Choi et al., 2007; Uchida, 2010 for Japanese).
[8] Since this study dealt with a wide range of emotional and cognitive variables, including them in a single model was considered to make theoretical explanations more difficult (e.g., Pedhazur, 1997; Yoshida & Murai, 2021). Therefore, the main hierarchical Bayesian model focused on the amount of change in each variable and the emotional valence of haiku, and the multiple regression results were added to Supplementary Table 5.
[9] There are three main reasons for this. First, this study measures and analyzes pupil diameter over a relatively long range of 20 seconds and does not focus much on microscopic changes of a few tenths of a second. Second, pupil diameter data is also affected by blinking and small head movements, so there is a lot of missing data. Downsampling could fill in this gap. Third, the smaller the data size, the more efficient the analysis.
[10]t , ζt , yt,i, and et represent time, normal white noise at time t, participant i's observed data at time t (valence, arousal level, and pupil diameter in this study), and observation error, respectively.
Haiku Ratings In Steps
The descriptive statistics of positive and negative haiku for each time point, the results of the t-test, and the multilevel correlations (haiku level and participant level) with beauty TP3 are shown in Table 1 (See Supplementary Table 2 for all multilevel correlation matrix). There seemed to be a difference in the change in valence between negative and positive haiku. Beauty scores increased gradually, with the highest scores at TP3, where the haiku were completed.
| Negative Haiku | Positive Haiku | Correlation with Beauty 3 (p) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Time | Mean | (SD) | Mean | (SD) | t value | p-value | d | Haiku Level | Participant Level | |||
| Beauty | ||||||||||||
| 1 | 40.33 | (22.48) | 43.33 | (23.55) | -3.38 | .00 | − .13 | .27 | (.00) | .72 | (.00) | |
| 2 | 50.34 | (21.45) | 50.47 | (22.86) | − .16 | .87 | − .01 | .50 | (.00) | .88 | (.00) | |
| 3 | 55.68 | (23.98) | 57.41 | (24.59) | -1.85 | .06 | − .07 | - | - | - | - | |
| Positive Emotion | ||||||||||||
| 1 | 42.01 | (21.55) | 51.06 | (22.08) | -10.75 | .00 | − .41 | .06 | (.01) | .34 | (.00) | |
| 2 | 39.30 | (21.41) | 54.89 | (22.13) | -18.55 | .00 | − .72 | .22 | (.00) | .39 | (.00) | |
| 3 | 37.17 | (23.70) | 62.50 | (23.51) | -27.78 | .00 | -1.07 | .32 | (.00) | .52 | (.00) | |
| Negative Emotion | ||||||||||||
| 1 | 41.11 | (22.54) | 30.62 | (20.35) | 12.65 | .00 | .49 | − .04 | (.04) | .24 | (.03) | |
| 2 | 49.15 | (23.82) | 31.52 | (21.52) | 20.11 | .00 | .78 | − .09 | (.00) | .31 | (.01) | |
| 3 | 53.28 | (26.00) | 27.97 | (21.86) | 26.82 | .00 | 1.05 | − .10 | (.00) | .32 | (.01) | |
| Arousal | ||||||||||||
| 1 | 42.38 | (22.66) | 42.52 | (22.46) | − .16 | .87 | − .01 | .05 | (.02) | .33 | (.00) | |
| 2 | 52.62 | (20.54) | 50.54 | (21.13) | 2.59 | .01 | .10 | .21 | (.00) | .43 | (.00) | |
| 3 | 55.89 | (22.64) | 56.36 | (22.53) | − .54 | .59 | − .02 | .39 | (.00) | .62 | (.00) | |
| Emotional Ambiguity | ||||||||||||
| 1 | 51.88 | (26.81) | 49.00 | (27.32) | 2.76 | .01 | .11 | − .05 | (.01) | .10 | (.38) | |
| 2 | 48.48 | (25.91) | 46.14 | (26.78) | 2.30 | .02 | .09 | − .15 | (.00) | .14 | (.23) | |
| 3 | 47.77 | (28.48) | 41.99 | (28.80) | 5.21 | .00 | .20 | − .16 | (.00) | .02 | (.84) | |
| Cognitive Ambiguity | ||||||||||||
| 1 | 49.18 | (26.95) | 45.02 | (26.84) | 4.00 | .00 | .15 | − .05 | (.01) | .28 | (.01) | |
| 2 | 46.04 | (25.51) | 42.02 | (25.63) | 4.07 | .00 | .16 | − .18 | (.00) | .32 | (.01) | |
| 3 | 44.25 | (29.45) | 36.76 | (28.54) | 6.69 | .00 | .26 | − .22 | (.00) | .24 | (.05) | |
| Awe | ||||||||||||
| 3 | 39.49 | (25.95) | 35.13 | (25.68) | 4.37 | .00 | .17 | .40 | (.00) | .64 | (.00) | |
| Nostalgia | ||||||||||||
| 3 | 43.24 | (27.39) | 47.69 | (27.05) | -4.24 | .00 | − .16 | .36 | (.00) | .53 | (.00) | |
| Joystick Valence | ||||||||||||
| 1 | − .01 | (.09) | .05 | (.09) | -15.39 | .00 | − .59 | .09 | (.00) | .23 | (.12) | |
| 2 | − .05 | (.13) | .07 | (.13) | -23.57 | .00 | − .91 | .17 | (.00) | .11 | (.45) | |
| 3 | − .08 | (.16) | .12 | (.15) | -32.36 | .00 | -1.25 | .25 | (.00) | .19 | (.21) | |
| Joystick Arousal | ||||||||||||
| 1 | − .06 | (.16) | − .06 | (.16) | .87 | .38 | .03 | .05 | (.01) | − .01 | (.94) | |
| 2 | − .03 | (.23) | − .05 | (.25) | 2.10 | .04 | .08 | .14 | (.00) | .20 | (.07) | |
| 3 | .11 | (.29) | .09 | (.30) | 1.67 | .10 | .06 | .38 | (.00) | .41 | (.00) | |
| Note: Valence and arousal of the joystick indicate the average values for each time point (5 seconds for TP1 and 2, 10 seconds for TP3). | ||||||||||||
Next, a hierarchical Bayesian analysis was conducted to examine the effects of changes in emotion and cognition on aesthetic evaluation, with beauty TP3 as the dependent variable. Participants and haiku were used as random effects and fixed effects for the amount of change between TPs 1 and 2 and TPs 2 and 3 for positive emotion, negative emotion, arousal, emotional ambiguity, and cognitive ambiguity, respectively.
| Fixed Effects | Estimate | Est. Error | l-95% CI | u-95% CI | R-hat | Bulk ESS | Tail ESS | |
|---|---|---|---|---|---|---|---|---|
| Positive Emotion | ||||||||
| Intercept | 55.82 | 1.99 | 51.88 | 59.69 | 1.00 | 1910 | 3856 | |
| TP 2 − 1 | .22 | .03 | .16 | .28 | 1.00 | 13470 | 12584 | |
| TP 3 − 2 | .27 | .03 | .21 | .34 | 1.00 | 9917 | 11649 | |
| Haiku Valence | -1.89 | 3.44 | -8.62 | 4.82 | 1.00 | 2263 | 3935 | |
| TP 2 − 1 * TP 3 − 2 | .00 | .00 | .00 | .00 | 1.00 | 17435 | 14278 | |
| TP 2 − 1 * Haiku Valence | .11 | .05 | .01 | .22 | 1.00 | 12729 | 12167 | |
| TP 3 − 2 * Haiku Valence | .14 | .05 | .03 | .24 | 1.00 | 10638 | 11808 | |
| TP 2 − 1 * TP 3 − 2 * Haiku Valence | .00 | .00 | .00 | .01 | 1.00 | 20717 | 14240 | |
| Negative Emotion | ||||||||
| Intercept | 55.99 | 1.97 | 52.10 | 59.85 | 1.00 | 2685 | 5211 | |
| TP 2 − 1 | − .08 | .03 | − .15 | − .02 | 1.00 | 10966 | 11329 | |
| TP 3 − 2 | − .09 | .03 | − .14 | − .03 | 1.00 | 12850 | 12109 | |
| Haiku Valence | .94 | 3.31 | -5.60 | 7.55 | 1.00 | 2487 | 4741 | |
| TP 2 − 1 * TP 3 − 2 | .00 | .00 | .00 | .00 | 1.00 | 17548 | 13680 | |
| TP 2 − 1 * Haiku Valence | − .11 | .06 | − .23 | .00 | 1.00 | 11430 | 12404 | |
| TP 3 − 2 * Haiku Valence | − .15 | .05 | − .25 | − .05 | 1.00 | 14996 | 11378 | |
| TP 2 − 1 * TP 3 − 2 * Haiku Valence | .00 | .00 | .00 | .01 | 1.00 | 20550 | 14711 | |
| Arousal | ||||||||
| Intercept | 56.63 | 1.89 | 52.92 | 60.36 | 1.00 | 3443 | 5638 | |
| TP 2 − 1 | .19 | .02 | .15 | .24 | 1.00 | 22391 | 13132 | |
| TP 3 − 2 | .29 | .03 | .24 | .35 | 1.00 | 15755 | 12330 | |
| Haiku Valence | 1.54 | 3.15 | -4.68 | 7.77 | 1.00 | 3239 | 6087 | |
| TP 2 − 1 * TP 3 − 2 | .00 | .00 | .00 | .00 | 1.00 | 17996 | 13905 | |
| TP 2 − 1 * Haiku Valence | .07 | .04 | − .02 | .16 | 1.00 | 26410 | 13016 | |
| TP 3 − 2 * Haiku Valence | .12 | .04 | .03 | .21 | 1.00 | 26358 | 12904 | |
| TP 2 − 1 * TP 3 − 2 * Haiku Valence | .00 | .00 | .00 | .01 | 1.00 | 17707 | 13008 | |
| Emotional Ambiguity | ||||||||
| Intercept | 56.80 | 1.94 | 53.01 | 60.58 | 1.00 | 2216 | 4615 | |
| TP 2 − 1 | − .09 | .02 | − .13 | − .05 | 1.00 | 11018 | 11656 | |
| TP 3 − 2 | − .07 | .02 | − .12 | − .03 | 1.00 | 12206 | 12315 | |
| Haiku Valence | 1.73 | 3.30 | -4.72 | 8.38 | 1.00 | 2177 | 4096 | |
| TP 2 − 1 * TP 3 − 2 | .00 | .00 | .00 | .00 | 1.00 | 16302 | 11522 | |
| TP 2 − 1 * Haiku Valence | − .09 | .04 | − .17 | − .02 | 1.00 | 13554 | 11940 | |
| TP 3 − 2 * Haiku Valence | − .04 | .03 | − .11 | .03 | 1.00 | 21223 | 12113 | |
| TP 2 − 1 * TP 3 − 2 * Haiku Valence | .00 | .00 | .00 | .00 | 1.00 | 17955 | 13049 | |
| Cognitive Ambiguity | ||||||||
| Intercept | 57.13 | 1.96 | 53.27 | 60.87 | 1.00 | 2222 | 4150 | |
| TP 2 − 1 | − .11 | .02 | − .15 | − .06 | 1.00 | 11018 | 11596 | |
| TP 3 − 2 | − .11 | .03 | − .16 | − .06 | 1.00 | 10771 | 11824 | |
| Haiku Valence | 1.44 | 3.24 | -5.03 | 7.81 | 1.00 | 2213 | 4768 | |
| TP 2 − 1 * TP 3 − 2 | .00 | .00 | .00 | .00 | 1.00 | 17018 | 12166 | |
| TP 2 − 1 * Haiku Valence | − .01 | .05 | − .09 | .08 | 1.00 | 11663 | 11949 | |
| TP 3 − 2 * Haiku Valence | − .03 | .05 | − .12 | .06 | 1.00 | 11197 | 11677 | |
| TP 2 − 1 * TP 3 − 2 * Haiku Valence | .00 | .00 | .00 | .00 | 1.00 | 16951 | 14123 | |
Hierarchical Bayesian results showed that the increase in positive emotion, decrease in negative emotion, increase in arousal, decrease in emotional ambiguity, and decrease in cognitive ambiguity from both TP1 to TP2 and TP2 to TP3 explained the beauty of the haiku in each model. The interaction between the change in positive emotion (TP1 to TP2 and TP2 to TP3), negative emotion (TP2 to TP3), arousal (TP2 to TP3), emotional ambiguity (TP1 to TP2), and the valence of haiku (TP1 to TP2 and TP2 to TP3) was also significant. The effects of increased positive emotion, decreased negative emotion, increased arousal, and decreased emotional ambiguity on beauty were stronger for positive haiku than for negative haiku, while the effect of decreased cognitive ambiguity on beauty was not moderated by the valence of the haiku.
Continuous Evaluation With Joystick
A time-series plot of the joystick-reported valence and arousal estimated by the state-space model is shown in Fig. 2 (see Supplementary Fig. 1 for the time-series analysis of observed data). Figure 2A shows that for positive haiku, positive emotions increased gradually, and for negative haiku, negative emotions increased gradually. The parameter representing the fluctuation in the amount of change in valence was σ = 0.00 (SD = 0.00) for both positive and negative haiku, indicating that the amount of change was constant (no fluctuation in the amount of change; Supplementary Table 7). In contrast, Fig. 2B shows that arousal tends to drop temporarily only during the presentation time of line 1 and then gradually increases for both haiku in the same manner. In this study, both valence and arousal were set to start at 0; the initial drop in arousal may be caused by this setting. That is, participants would have reported expressing low arousal in the early stages of haiku appreciation, with arousal gradually increasing (consistent with descriptive statistics; Table 1). The parameter representing fluctuation in the amount of change in arousal was σ = 0.00 (SD = 0.00) for both positive and negative haiku, indicating that the amount of change was constant (no fluctuation in the amount of change; Supplementary Table 7). Moreover, the variance of arousal on the Y-axis was larger than the variance of valence on the X-axis (Table 1).
Pupil Diameter
The time-series analysis plot indicates that pupil diameter tended to decrease slightly (Fig. 3). Meanwhile, there was no significant change in the big picture, and the trend was almost the same for both positive and negative haikus (See also Supplementary Table 8 for descriptive statistics). The parameter representing the fluctuation in pupil diameter change was σ = 0.01 (SD = 0.01) for both positive and negative haiku, indicating that the amount of change was nearly constant (Supplementary Table 11).
Next, a hierarchical Bayesian model was used to examine how changes in pupil diameter were related to the beauty of the haiku (Table 3; See also Supplementary Table 9 for multilevel correlation). The results showed that the increase in pupil diameter size at TPs 1 and 2 explained the variance of the beauty ratings for haiku (b = .68, 95% CI [.20, 1.15]). This is confusing when observing the decreasing time-series plot of pupil diameter, but since the time-series only represents the average of all haiku, some haiku may have resulted in larger pupils indicating a greater perception of beauty. Furthermore, pupil diameter showed either no significant correlation or a weak negative correlation with perceived cognitive ambiguity, which is in contrast to our hypothesis (TP1: r = − .02, p = .39; TP2: r = − .04, p = .04; TP3: r = − .02, p = .48; Supplementary Table 9).
| Fixed Effects | Estimate | Est. Error | l-95% CI | u-95% CI | R-hat | Bulk ESS | Tail ESS | |
|---|---|---|---|---|---|---|---|---|
| Pupil Diameter | ||||||||
| Intercept | 55.61 | 2.05 | 51.56 | 59.60 | 1.00 | 3015 | 6031 | |
| TP 2 − 1 | .68 | .24 | .20 | 1.15 | 1.00 | 20858 | 12881 | |
| TP 3 − 2 | .12 | .24 | − .38 | .58 | 1.00 | 12375 | 10928 | |
| Haiku Valence | 1.84 | 3.38 | -4.79 | 8.56 | 1.00 | 3088 | 5539 | |
| TP 2 − 1 * TP 3 − 2 | − .12 | .05 | − .22 | − .03 | 1.00 | 20586 | 12567 | |
| TP 2 − 1 * Haiku Valence | − .29 | .48 | -1.21 | .65 | 1.00 | 24286 | 12837 | |
| TP 3 − 2 * Haiku Valence | − .12 | .41 | − .92 | .68 | 1.00 | 19512 | 11530 | |
| TP 2 − 1 * TP 3 − 2 * Haiku Valence | .19 | .10 | .00 | .39 | 1.00 | 19462 | 12602 | |
| Note: Each value indicates the average values for each time point (5 seconds for TP1 and 2, 10 seconds for TP3). | ||||||||
This study examined the influence of emotional and cognitive changes during haiku appreciation on aesthetic evaluations. The results showed that both emotional changes, such as increased positive emotions, decreased negative emotions, increased arousal, and decreased emotional ambiguity, and cognitive changes, such as decreased cognitive ambiguity, explained the beauty of the haiku. The continuous evaluation of emotions by the joystick showed that valence and arousal indeed changed during haiku appreciation, and pupil diameter explained the beauty of the haiku.
The Impact Of Emotional Changes On The Beauty Of Haiku
First, focusing on the emotional aspect of this study, an increase in positive emotion, a decrease in negative emotion, and a rise in arousal explained the beauty of the haiku. While a previous study (Hitsuwari & Nomura, 2022a) found that the more positive the emotional valence, the higher the aesthetic evaluation of the haiku, this study sheds new light on whether and how a decrease in negative emotion is also involved. Beauty could be perceived initially by evoking negative emotions and then mitigating them, which is consistent with previous studies showing the importance of negative emotions in art appreciation (e.g., Menninghaus et al., 2017; Vuoskoski & Eerola, 2017). It was also found that negative emotions were positively correlated with beauty at the inter-individual level when negative emotions were rated separately from positive emotions on the VAS (Table 1). This represents a trend in which those who are more likely to feel negative emotions are more likely to score higher on beauty, indicating that susceptibility to negative emotions facilitates aesthetic experiences. The results of the multiple regression analysis (Supplementary Table 5) also show that the increase in positive emotions, rather than the decrease in negative emotions, explains the increase in beauty. It would be interesting to study not only the division by valence but also by dividing negative emotions into sadness, anxiety, suspense, etc. (Menninghaus et al., 2017).
Haiku appreciation with the joystick revealed a gradual increase in arousal during the appreciation process (Fig. 2B), which may be an important factor to consider when assessing emotions during haiku appreciation. In fact, Menninghaus et al. (2019) argue that aesthetic emotions cannot be defined in terms of high or low arousal. Rather, art that successfully combines high and low arousal emotions, not just one or the other, is more common.
The effect of changes in emotion on beauty was moderated by the valence of haiku. Positive haiku were more likely to be affected by increased positive emotion, decreased negative emotion, increased arousal, and decreased emotional ambiguity than negative haiku. The valence of the artwork and the valence actually felt by the viewer should be considered separately (Kallinen & Ravaja, 2006). To the best of our knowledge, this is the first time such an interaction has been revealed. The present results are reasonable, given the inherently positive nature of art and the heightened emotions of enjoyment that art elicits (Lomas, 2016). Meanwhile, artworks that elicit negative emotion are also enjoyed (Menninghaus et al., 2017), and the influence of factors other than valence may be stronger.
The Impact Of Cognitive Changes On The Beauty Of Haiku
A decrease in cognitive ambiguity explains the haiku’s beauty. While a previous study (Hitsuwari & Nomura, 2022b) found a negative relationship between cognitive ambiguity and aesthetic evaluation of haiku (that is, the higher the cognitive ambiguity, the lower the beauty of the haiku), this study extended that relationship. In other words, cognitive ambiguity should eventually be eliminated but not be avoided from the beginning. This is consistent with the fact that cognitive ambiguity is expected and enjoyed in haiku and poetry (Nitta, 2016; Obermeier et al., 2016), and is also highly consistent with the phenomenon of a dramatic increase in liking for image stimuli at the moment of resolution of ambiguity, also called the aesthetic aha (Muth & Carbon, 2013). Similarly, the decrease in emotional ambiguity explains the beauty of haiku, and new findings indicate that not only cognitive or perceptual ambiguity (Muth & Carbon, 2013) but also the resolution of emotional ambiguity contributes to the art experience.
Studies considering the relationship between ambiguity and art evaluation have also reported an inverted U-shaped relationship between ambiguity and evaluation (Jakesch & Leder, 2009), indicating that neither too little nor too much ambiguity is important for the aesthetic experience. Moreover, such studies suggest that ambiguity at the level where the resolution of ambiguity is expected is ideal (Muth et al., 2019). Notably, Hitsuwari and Nomura (2022b) proposed to assess ambiguity not as a single factor but as comprising both emotional and cognitive ambiguity. In our study, where the participants were asked to report the two ambiguities directly, the resolution of both ambiguities was associated with beauty. However, an examination of the correlation between both ambiguities shows that they are indeed only moderately correlated (r = .57), indicating that they are different concepts. Therefore, it would be useful to continue categorizing ambiguity and examining its relationship with aesthetic evaluation.
Pupil Diameter And The Relation Of Beauty In Haiku
The pupil diameter expansion between TP1 1 and TP2 explains the beauty of the haiku. This result is consistent with findings showing a positive correlation between pupil diameter dilation and the evaluation of arts such as painting and music (Kuchinke et al., 2009; Laeng et al., 2016). In other words, the results suggest that pupil diameter is an indicator of the beauty of the haiku and linguistic art. However, the time-series change in pupil diameter did not change significantly, although it decreased slightly (Fig. 3). Pupil diameter was found to be largely unrelated to cognitive ambiguity, and in this study, pupil diameter was associated with emotional processes of beauty rather than cognitive processes such as the resolution of ambiguity. A recent study measuring pupil diameter compared haiku, an aesthetic poem, to senryu, a more comical poem, finding a more gradual pupil size enlargement in haiku (Niikuni et al., 2022). Because haiku requires more context and relevance judgments than senryu, the researchers interpreted pupil enlargement as not being explained by cognitive load (if cognitive load causes pupil dilation, the enlargement will occur more quickly in haiku) but by heightened emotion.
Hoshi and Menninghaus (2018) examined pupil diameter size while studying the linguistic stimulus of proverbs, concluding that pupil diameter reflects emotional aspects, such as beauty, and cognitive aspects, such as familiarity and brevity, in an antagonistic manner. In other words, pupil diameter reflects the beauty and various emotional and cognitive psychological processes. As discussed, emotional and cognitive changes continue to occur during haiku appreciation, and it would be difficult to completely isolate these effects on pupil diameter in this experiment. It is expected that future experiments will be conducted with more rigorous control of these variables.
The difference in the degree of compression of the data may be one possible technical reason why we did not observe an association between pupil diameter and cognitive ambiguity in this study. That is, the evaluation of ambiguity is a one-time evaluation over a period of 5 or 10 seconds, whereas the pupil diameter is data compressed to 1/600th or 1/1200th of the continuously acquired data for that time period. These points could be resolved by continuous reporting of ambiguity using a joystick.
Limitations And Future Directions
This study focused on the process of emotional and cognitive change during haiku appreciation, which had not been adequately addressed in previous studies. It introduced new findings; however, several issues still need to be clarified. First, this study examined the haiku as a special form of poetry, and it is necessary to conduct a paradigm that focuses on similar change processes in other styles of poetry and literature. Indeed, haiku, with its fixed forms of five, seven, and five morae, is ideal for this paradigm, but it is highly likely that negative emotions and the resolution of ambiguity also occur in other language arts. Although the experiment was conducted with Japanese participants using a Japanese haiku, it would be interesting to apply this experiment to other languages and cultural groups. Hitsuwari and Nomura (2022b) examined cultural differences in haiku and found that the shortness and image-dependent characteristics of haiku make them suitable for cultural comparison; therefore, further studies are expected.
This study revealed new insights into the role of emotion and cognition, which were previously evident in the evaluation of haiku at only one time point. Specifically, both emotional changes, such as increased positive emotions, decreased negative emotions and increased arousal, and cognitive changes, such as decreased cognitive ambiguity, affected the aesthetic evaluation of the haiku. The continuous evaluation of emotions using a joystick revealed the movement of emotions over time. Positive haiku revealed a gradual increase in positive emotions, and negative haiku revealed a gradual increase in negative emotions. Furthermore, both forms of haiku showed a similar gradual increase in arousal. Additionally, pupil diameter was introduced to exploratorily examine unconscious responses to the haiku and to explain the beauty of the haiku.
Haiku, with its controlled shortness, is a suitable stimulus for capturing these changes and has the potential to advance the study of linguistic art, which is a frontier compared to other art forms, such as visual art. Specifically, three lines (5-7-5 morae) can be displayed at fixed timings, and because of their short length, a large number of poems can be appreciated in a single experiment. These results also demonstrate the importance of having the participants evaluate the process of change in a step-by-step manner and continuously measuring their reactions during appreciation, as a measurement of evaluation at one time point is insufficient in any art appreciation research. During art appreciation, the emotions and cognition toward an artwork constantly change. The core of beauty lies in this process of change.
Finally, this study also sheds light on psychometric and reproducibility issues in this area (Nosek et al., 2022). Specifically, a sample size derivation based on multilevel analyses and various analytical methods, including Bayesian analysis, was devised (Arend & Schäfer 2019; Etz & Vandekerckhove, 2018). For future research on art appreciation and aesthetic evaluation, it is hoped that more valid research will be conducted, not only on haiku and poetry but also on painting, music, and other art forms.
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Competing interests: The authors declare no competing interests.