Haiku poetry, a short poem with clear form, can potentially elucidate many unknown aspects of verbal art. Previous studies on haiku appreciation reported emotional and cognitive effects on aesthetic evaluation. Since both change during art appreciation, clarifying the processes of these change during haiku evaluation is beneficial. This study examined the temporal effects of emotional and cognitive changes on aesthetic evaluation from multiple perspectives and measured the ratings of each section of a haiku, continuous emotional reports, and physiological changes in pupil diameter. First, 112 participants rated a haiku at three time-points on various items, such as beauty, valence, and ambiguity. Subsequently, they rated the same haiku continuously for 20 seconds via a joystick for valence and arousal, during which their pupil diameter was measured. Emotional changes, such as increased positive emotions, arousal, and emotional ambiguity, and cognitive changes, such as decreased cognitive ambiguity, affected the haiku’s aesthetic evaluation. Regarding continuous emotion reports, positive and negative emotions gradually increased for positive and negative haikus, respectively, while arousal gradually increased for both. Additionally, increase in pupil diameter also explained the beauty. This study advances our understanding of verbal arts by empirically exploring conscious and unconscious emotional and cognitive responses to haikus.
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/v3
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Haiku poetry is the world’s shortest poetic form and includes 5-7-5 morae and a seasonal word (Iida, 2008). In a haiku, one or two images are composed and a breakpoint, called a kire (cut), is efficiently used to integrate them (Blasko & Merski, 1998). While the Japanese are naturally comfortable with the 5-7-5 rhythm (Niikuni et al., 2022), rhymes are not common in haikus. Owing to its controlled format and clear rules, haiku poetry can potentially explore the frontier of verbal art, which has many unexplored elements compared with accumulated research in visual and auditory arts (cf., micropoems) (Hugentobler & Lüdtke, 2021; Jacobs, 2015). Empirical studies have examined how poetry has been aesthetically appreciated, both regarding cognition and emotion (Hitsuwari & Nomura, 2024; Johnson-Laird & Oatley, 2022; Obermeier et al., 2016; Pițur & Miu, 2022; Wassiliwizky et al., 2017). Similarly, haiku poets have suggested a relationship between the emotions and cognition evoked by a haiku’s beauty (Kishimoto, 2008). Some recent empirical studies have examined this relationship. Regarding emotions, positive (Belfi et al., 2018; Hitsuwari & Nomura, 2022a) and mixed emotions, such as awe and nostalgia, (Hitsuwari & Nomura, 2022b) explain the beauty. Regarding cognition, imagery vividness explains the aesthetic appeal of haiku (Belfi et al., 2018; Mehl et al., 2023). Furthermore, cognitive ambiguity reduces their appeal (Hitsuwari & Nomura, 2022b). However, these results contradict the high evaluation of grief haiku (Imase et al., 2010) and importance of ambiguity in haiku and poetry (Nitta, 2016; Wallot & Menninghaus, 2018). These findings are insufficient since negative emotions (Menninghaus et al., 2017) and resolution of ambiguity (Muth & Carbon, 2013) contribute to aesthetic experiences in visual and auditory arts. Consideration of the processes involved in alleviating negative emotions, as well as increment of positive emotions and resolution of ambiguity that explain a haiku’s uniqueness and its commonality with previous findings, is required. Therefore, this study aimed to provide a deeper understanding of the beauty of haikus and focused on the process of emotional and cognitive changes during appreciation.
Several process models of emotion and cognition during art appreciation have been developed (Leder & Nadal, 2014; Leder et al., 2004; Menninghaus et al., 2017; Pelowski et al., 2017). In Menninghaus et al.’s (2017) model, negative art appreciation could be enhanced by an ''embracing'' component that enhanced art appreciation via positive integration and incorporation of the power of the negative emotions. Therefore, the interactions between positive and negative emotions and role of mixed emotions were highlighted. This suggests that changes in negative and positive emotions are important in an art experience. Another process model (Pelowski et al., 2017) stated that viewers could make several interpretations and thoughts when there was low relevance to the self and low schema consistency in the top-down cognitive process. As individuals continue to view an artwork, they encounter information that resolves discrepancy and may lead to insight with aesthetic evaluation. Supporting this process, experiments have captured the association between ambiguity and beauty at more than one time-point. Muth and Carbon (2013) presented ambiguous images repeatedly for 500 ms each and asked participants to repeat their liking and clarity ratings. They found that the largest increase in liking occurred immediately after the resolution of ambiguity (i.e., insight). Thus, paying attention to the emotional and cognitive changes in art appreciation is important. The impact of these changes on beauty can be examined by evaluating haiku poems with a clear 5-7-5 structure. Haiku is ideal to examine this process owing its controlled shortness; however, expected results could be extended to other poetic forms and art genres.
We examined the abovementioned emotional and cognitive change processes and asked participants to evaluate the haiku in lines 1, 2, and 3 one-by-one while a more precise method to measure the change processes was introduced. Emotional processes, such as the perception and expression of emotional states and social signaling, could be acquired by manipulating joysticks (Girard & Wright, 2018; Sharma et al., 2020). Although a flourishing approach in studies on the aesthetic appreciation of temporal art, especially music (Bachorik et al., 2009; Grewe et al., 2007; Nagel et al., 2007), the continuous self-rating of spoken poetry does not affect physiological responses or post-appreciation ratings. Hence, this type of continuous rating during appreciation is also possible in poetry (Wagner et al., 2021). In this study, participants reported their emotions on two dimensions, valence and arousal, via a joystick as the haiku was gradually presented. These dimensions were also used in previous studies (Bliss-Moreau et al., 2020; Russell, 1980; Sharma et al., 2020 for practical examples using joysticks).
Additionally, eye movements were compatible with continuous response, such as joysticks, and included features of temporality and unconsciousness. This methodology was also accumulated in verbal art fields, such as poetry (Blohm et al., 2022; Menninghaus & Wallot, 2021; Scheepers et al., 2013). In particular, studies used haiku and eye-movement measurements and revealed that people appreciated haiku differently based on the presence and position of the kire (cut) and type of haiku (Geyer et al., 2020; Hermann et al., 2017). A study compared haikus and slogans, with the same 5-7-5 syllable structure, and found that the pupil diameter was larger when haiku was viewed as aesthetic poetry compared with viewing slogans (Niikuni et al., 2022). Although pupil diameter was positively related to aesthetic evaluation in other art genres, such as painting and music (Kuchinke et al., 2009; Laeng et al., 2016), the relationship is more complex in haiku. One study reported no correlation (Spee et al., 2024). Therefore, this study measured pupil diameter as a supplementary measure, in addition to gradual haiku rating and joysticks.
Extending on previous studies that only captured cognition and emotion at a single timepoint during poetry appreciation, this study aimed to examine the multidimensional effects of changes in cognition and emotion on haiku evaluation and combined haiku ratings in steps, continuous ratings via a joystick, and pupil diameter measurements. In particular, increased positive emotion and arousal and decreased negative emotion and emotional/cognitive ambiguity during haiku appreciation could lead to beauty. Furthermore, dynamic changes in emotions during haiku appreciation could be captured via joysticks for the first time. Simultaneously, changes in pupil diameter, as an exploratory measure of aesthetic evaluation, would be positively related to beauty.
Participants
The sample size was 113 students, based on previous research on sample sizes for hierarchical and repeated measures data (Arend & Schäfer, 2019). In the sample size estimation method, direct effects of levels 1 and 2 and intra-class correlation coefficients among linear mixed models were incorporated into the model and calculated via the simr package (Green & Macleod, 2016) of R (R scripts used for the estimation were stored on the OSF project page). We recruited 113 undergraduate and graduate students from Kyoto University, whose native language was Japanese, via an electronic notice board. Owing to 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 owing to missing data, and ultimately, data from 112 participants (Mage = 20.92 years, SDage = 2.55, 53 men and 59 women) were analyzed. In addition, pupil diameter data could not be analyzed for 17 and two participants owing to a programming error (lack of time in the measurement program) and too many missing values, respectively. Therefore, pupil diameter data were analyzed for only 93 participants.
Materials
Stimuli comprised 172 haikus used in previous studies (Hitsuwari & Nomura, 2022a; 2022b). In these studies, the valence of each haiku was measured on a visual analog scale (VAS) scale from 0 to 100. Of the 172 haikus, 30 each were selected as being more positive (Mvalence = 69.27, SD = 4.20, Range = 64.82–79.06) and more negative (Mvalence = 35.95, SD = 5.01, Range = 26.59–44.92). Hence, 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 haikus (24 positive and 24 negative) were used in the main experiment. These were randomly divided into two lists that included half positive and half negative haikus. Participants rated only one list.
Procedure
This study was approved by the Ethics Committee of Kyoto University (CPE-429). All data, materials, and scripts are available online at (https://osf.io/x64hu/?view_only=2420185c367f41d6a10c1a6137021ce9https://osf.io/).
The experiment was conducted in a laboratory at the Faculty of Education, Kyoto University. Informed consent was obtained from all the students prior to their participation. After the experiment was explained, participants were fitted with an ET-mono (Gazo, Co., Ltd; Nakamura et al., 2019), a wearable device that measured pupil diameter, and instructed to not move their heads significantly during the experiment. A practice session was conducted first, followed by the main experiment that was divided into two main components. First, participants rated each haiku at three time points (TPs): lines 1, 2, and 3. Participants completed a 0–100 VAS rating on six items: positive emotion, negative emotion, arousal, emotional ambiguity, cognitive ambiguity, and beauty (eight items, which included awe and nostalgia, were measured only at the third time point; Fig. 1a). Second, the same haiku was rated continuously via a joystick. A two-dimensional plot of valence and arousal on the X-and Y axis, respectively, was displayed on the screen. Participants continuously reported their own emotions for 20 seconds. Only line 1 was displayed for the first five seconds, followed by lines 2 and 3 after five second intervals each (Fig. 1b). During continuous reporting, participants were instructed to focus their evaluation on their own emotions, not on the haiku’s valence. Participants rated one haiku via the VAS, and subsequently switched to a joystick and performed a 20-second continuous emotion report for the same haiku. After they rated one haiku, they had a 30-second break before the next haiku was presented to prevent emotional carryover. A maximum 3-minute break was allowed after half of the haikus (12 haiku) were evaluated. Pupil diameters were recorded continuously from the beginning to the end and analyzed primarily during the continuous emotion report with the joystick. Finally, after the participants left the laboratory, they completed an online questionnaire that included demographic information, haiku familiarity, haiku/art experience, and individual traits. Although individual traits were not studied variables, Supplemental Table 1 presents the descriptive statistics and correlations with beauty TP3. The experimental program was created and executed using Psychopy version 3 (2020.2.3; Peirce et al., 2019), and the questionnaire was created via Qualtrics.
Data Analysis
First, descriptive statistics were calculated separately for negative and positive haikus for each variable. Scores were analyzed via t-tests, and Cohen's d was also calculated. Additionally, multi-level correlations were calculated via the HAD software (version 17.20; Shimizu, 2016). Second, a hierarchical Bayesian regression model, in which changes in TP12 and TP23 for all rating variables (within-participant centered) explained beauty TP3, was conducted via sbrms package (Bürkner, 2017), with participants and haiku for random intercepts:
Beauty TP 3 was used as the dependent variable as it was the form of haiku completed at time point 3, and one that people usually appreciated. Convergence was confirmed (< 1.10) (Brooks & Gelman, 1998), and significance of the effect of each variable was determined by 95% posterior credible intervals (CIs) not containing zero. Third, for the joystick time-series data, 20 seconds of data at 60 frames per second (fps) was used. These were compressed (downsampled) to one-tenth of their original size. Following previous studies that used joysticks and pupil diameter (Takano & Nomura, 2023), we used smoothed trend models with second-order differencing for time-series analysis. This model was a state-space model of Bayesian updating applied to time-series analyses and was suitable for capturing gradual changes and fluctuations between time points. It assumed the existence of invisible states and comprised (1) a state equation that represented the change in state and (2) an observation equation that represented the process by which the observed values were obtained (Durbin & Koopman, 2012). The smoothed trend model was represented by the following two equations:
Difference-in-differences was 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 parameters of fluctuation in the amount of change (magnitude of change in the drift component). Settings of model estimation were: four chains, each with iteration = 10000, warmup = 2000, thin = 10, post-warmup draws per chain = 800, and total post-warmup draws = 3200. Fourth, similar to the second analysis, the hierarchical Bayesian regression model that explained beauty, TP3, was run via the amount of change in pupil diameter. Participants and haiku were put into random effects. Regarding pupil diameter, data with missing values of 20% or more were removed. After missing value completion with the zoo package in R (Zeileis & Grothendieck, 2005), baseline correction was performed. Data were differenced from the baseline (average of the first five data in every haiku trial). Furthermore, since the data were acquired at 120 fps, they were downsampled by a factor of 20. Fifth, similar to the third analysis, the smoothed trend model of the state-space model was also applied for pupil diameter. In addition, analyses that used the conventional linear mixed model and time-series plots of the observed data were performed to confirm no significant differences with the Bayesian results (see supplementary file). Analyses were conducted using R software (version 4.1.0; R Core Team, 2021) and Rstan (version 2.21.3; Stan Development Team, 2021).
Table 1 presents the descriptive statistics of positive and negative haiku for each time point, t-test results, and multi-level correlations (haiku and participant level) with beauty TP3 (See Supplementary Table 2 for all multi-level correlation matrices). Differences in valence were observed between negative and positive haikus. Beauty scores increased gradually, with the highest scores at TP3, where the haiku was completed.
| Negative Haiku | Positive Haiku | Haiku Level | Participant Level | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TP | Mean | SD | Mean | SD | t value | p value | Cohen's d | r | p value | r | p value | |||||
| Beauty | ||||||||||||||||
| 1 | 40.33 | 22.48 | 43.33 | 23.55 | -3.38 | < .01 | − .13 | .27 | < .01 | .72 | < .01 | |||||
| 2 | 50.34 | 21.45 | 50.47 | 22.86 | − .16 | .87 | − .01 | .50 | < .01 | .88 | < .01 | |||||
| 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 | < .01 | − .41 | .06 | < .01 | .34 | < .01 | |||||
| 2 | 39.30 | 21.41 | 54.89 | 22.13 | -18.55 | < .01 | − .72 | .22 | < .01 | .39 | < .01 | |||||
| 3 | 37.17 | 23.70 | 62.50 | 23.51 | -27.78 | < .01 | -1.07 | .32 | < .01 | .52 | < .01 | |||||
| Negative Emotion | ||||||||||||||||
| 1 | 41.11 | 22.54 | 30.62 | 20.35 | 12.65 | < .01 | .49 | − .04 | .04 | .24 | .03 | |||||
| 2 | 49.15 | 23.82 | 31.52 | 21.52 | 20.11 | < .01 | .78 | − .09 | < .01 | .31 | < .01 | |||||
| 3 | 53.28 | 26.00 | 27.97 | 21.86 | 27.29 | < .01 | 1.05 | − .10 | < .01 | .32 | < .01 | |||||
| Arousal | ||||||||||||||||
| 1 | 42.38 | 22.66 | 42.52 | 22.46 | − .16 | .87 | − .01 | .05 | .02 | .33 | < .01 | |||||
| 2 | 52.62 | 20.54 | 50.54 | 21.13 | 2.59 | .01 | .10 | .21 | < .01 | .43 | < .01 | |||||
| 3 | 55.89 | 22.64 | 56.36 | 22.53 | − .54 | .59 | − .02 | .39 | < .01 | .62 | < .01 | |||||
| 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 | < .01 | .14 | .23 | |||||
| 3 | 47.77 | 28.48 | 41.99 | 28.80 | 5.21 | < .01 | .20 | − .16 | < .01 | .02 | .84 | |||||
| Cognitive Ambiguity | ||||||||||||||||
| 1 | 49.18 | 26.95 | 45.02 | 26.84 | 4.00 | < .01 | .15 | − .05 | .01 | .28 | < .01 | |||||
| 2 | 46.04 | 25.51 | 42.02 | 25.63 | 4.07 | < .01 | .16 | − .18 | < .01 | .32 | < .01 | |||||
| 3 | 44.25 | 29.45 | 36.76 | 28.54 | 6.69 | < .01 | .26 | − .22 | < .01 | .24 | .05 | |||||
| Awe | ||||||||||||||||
| 3 | 39.49 | 25.95 | 35.13 | 25.68 | 4.37 | < .01 | .17 | .40 | < .01 | .64 | < .01 | |||||
| Nostalgia | ||||||||||||||||
| 3 | 43.24 | 27.39 | 47.69 | 27.05 | -4.24 | < .01 | − .16 | .36 | < .01 | .53 | < .01 | |||||
| Valence (Joystick) | ||||||||||||||||
| 1 | − .01 | .09 | .05 | .09 | -15.33 | < .01 | − .60 | .09 | < .01 | .25 | .10 | |||||
| 2 | − .05 | .13 | .07 | .13 | -23.39 | < .01 | − .91 | .16 | < .01 | .15 | .35 | |||||
| 3 | − .08 | .16 | .12 | .15 | -32.12 | < .01 | -1.25 | .25 | < .01 | .22 | .16 | |||||
| Arousal (Joystick) | ||||||||||||||||
| 1 | − .06 | .16 | − .06 | .16 | .78 | .43 | .03 | .04 | .03 | .00 | .99 | |||||
| 2 | − .03 | .23 | − .05 | .24 | 2.28 | .02 | .09 | .13 | < .01 | .21 | .06 | |||||
| 3 | .10 | .29 | .08 | .29 | 1.65 | .10 | .06 | .37 | < .01 | .42 | < .01 | |||||
| Pupil Diameter (before correction) | ||||||||||||||||
| 1 | 36.81 | 5.63 | 36.94 | 5.77 | − .54 | .59 | − .02 | .03 | .14 | − .12 | .32 | |||||
| 2 | 36.36 | 5.62 | 36.32 | 5.70 | .16 | .87 | .01 | .03 | .13 | − .11 | .37 | |||||
| 3 | 35.99 | 5.58 | 35.79 | 5.58 | .83 | .41 | .04 | .07 | < .01 | − .10 | .40 | |||||
| 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). t-tests were conducted to see if there were significant differences between each score of negative and positive haiku, and the results are shown as t value and p value. | ||||||||||||||||
A hierarchical Bayesian regression 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 the 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 were used as fixed effects (Table 2; Supplementary Table 3 for the same result in a linear mixed model).
| Group-Level Effects | Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk ESS | Tail ESS |
|---|---|---|---|---|---|---|---|
| Haiku Level SD (Intercept) | 10.97 | 1.25 | 8.82 | 13.70 | 1.00 | 4098.00 | 7168.00 |
| Participant Level SD (Intercept) | 11.04 | .83 | 9.56 | 12.78 | 1.00 | 3879.00 | 5971.00 |
| Population-Level Effects | Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk ESS | Tail ESS |
| (Intercept) | 56.61 | 1.93 | 52.79 | 60.38 | 1.00 | 2856.00 | 5132.00 |
| Positive Emotion TP12 | .18 | .02 | .13 | .23 | 1.00 | 19135.00 | 12538.00 |
| Positive Emotion TP23 | .23 | .02 | .18 | .27 | 1.00 | 17979.00 | 11422.00 |
| Negative Emotion TP12 | .01 | .02 | − .04 | .06 | 1.00 | 20225.00 | 13033.00 |
| Negative Emotion TP23 | .00 | .02 | − .04 | .05 | 1.00 | 19476.00 | 13558.00 |
| Arousal TP12 | .13 | .02 | .09 | .17 | 1.00 | 18255.00 | 12349.00 |
| Arousal TP23 | .23 | .02 | .19 | .28 | 1.00 | 19016.00 | 13373.00 |
| Emotional Ambiguity TP12 | .00 | .02 | − .03 | .03 | 1.00 | 19363.00 | 13277.00 |
| Emotional Ambiguity TP23 | .05 | .02 | .01 | .08 | 1.00 | 21104.00 | 13328.00 |
| Cognitive Ambiguity TP12 | − .06 | .02 | − .10 | − .03 | 1.00 | 19031.00 | 11587.00 |
| Cognitive Ambiguity TP23 | − .06 | .02 | − .09 | − .02 | 1.00 | 19500.00 | 13209.00 |
| Haiku Valence | -1.85 | 3.23 | -8.35 | 4.52 | 1.00 | 3276.00 | 5496.00 |
| Haiku List | 1.46 | 3.95 | -6.20 | 9.32 | 1.00 | 2820.00 | 4795.00 |
| Note. TP12 represents the change from Timepoint 1 (0–5 s) to Timepoint 2 (5–10 s); TP23 represents the change from Timepoint 2 (5–10 s) to Timepoint 3 (10–20 s). ESS represents Effective Sample Size. | |||||||
Results showed that increased positive emotions, arousal, and emotional ambiguity and decreased cognitive ambiguity explained the haiku’s beauty (Fig. 2). Figure 3 illustrates a time-series plot of the joystick-reported valence and arousal, estimated via the state-space model (see Supplementary Fig. 1 for the time-series analysis of the observed data). Figure 3a illustrates that for a positive and negative haiku, positive and negative emotions gradually increased, respectively. The parameter that represented the fluctuation in the amount of change in valence was σ = 0.00 (SD = 0.00) for both positive and negative haiku, which indicated that the amount of change was constant (no fluctuation; Supplementary Table 4). In contrast, Fig. 3b illustrates that arousal tended to drop temporarily only during the presentation time of line 1 and gradually increase for both haikus in almost the same manner. We set both valence and arousal to start at 0; the initial drop in arousal could be caused by this setting. That is, participants would express low arousal in the early stages of haiku appreciation, with arousal gradually increasing (consistent with the descriptive statistics; see Table 1). The parameter that represented fluctuation in the amount of change in arousal was σ = 0.00 (SD = 0.00) for both positive and negative haiku, which indicated that the amount of change was constant (no fluctuation; Supplementary Table 4). Moreover, the variance of arousal was larger than that of valence (Table 1).
A hierarchical Bayesian regression model was used to examine how changes in pupil diameter were related to a haiku’s beauty (see Table 3; Supplementary Table 5 for the same result in the linear mixed model). Results revealed that increase in pupil diameter at both TPs 1 and 2 (b = .68, 95% CI [.18, 1.18]) and TPs 2 and 3 (b = .65, 95% CI [.22, 1.10]) explained the variance.
| Group-Level Effects | Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk ESS | Tail ESS |
|---|---|---|---|---|---|---|---|
| Haiku Level SD (Intercept) | 11.54 | 1.34 | 9.24 | 14.51 | 1.00 | 2688.00 | 5257.00 |
| Participant Level SD (Intercept) | 10.73 | .93 | 9.05 | 12.72 | 1.00 | 3484.00 | 5768.00 |
| Population-Level Effects | Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk ESS | Tail ESS |
| (Intercept) | 56.07 | 2.02 | 52.06 | 60.05 | 1.00 | 1811.00 | 3815.00 |
| Pupil Diameter TP12 | .68 | .26 | .18 | 1.18 | 1.00 | 15439.00 | 12204.00 |
| Pupil Diameter TP23 | .65 | .23 | .22 | 1.10 | 1.00 | 15299.00 | 11760.00 |
| Haiku Valence | 1.72 | 3.46 | -5.10 | 8.45 | 1.00 | 1742.00 | 3665.00 |
| Haiku List | 3.37 | 4.17 | -4.74 | 11.56 | 1.00 | 1764.00 | 3777.00 |
| Note. TP12 represents the change from Timepoint 1 (0–5 s) to Timepoint 2 (5–10 s); TP23 represents the change from Timepoint 2 (5–10 s) to Timepoint 3 (10–20 s). ESS represents Effective Sample Size. | |||||||
Finally, the time-series analysis plot indicated that the pupil diameter increased with the appearance of each new line (Fig. 4; see Supplementary Fig. 2 for the time-series analysis of the observed data). Furthermore, an overall slowly decreasing trend was observed. The trend was almost the same for both positive and negative haikus; however, pupil diameter was slightly larger during the negative haiku after line 2 appeared. The parameter that represented the fluctuations in pupil diameter change were σ = .02 (SD = .00) for negative haiku and σ = .03 (SD = .01) for positive haiku, which indicated that the amount of change differed slightly (Supplementary Table 4).
This study examined the influence of emotional and cognitive changes on aesthetic evaluations during haiku appreciation. Results revealed that both emotional changes, such as increased positive emotions, arousal, and emotional ambiguity, and cognitive changes, such as decreased cognitive ambiguity, explained the beauty of the haiku. Continuous evaluation of emotions via a joystick revealed that valence and arousal drastically changed during haiku appreciation. Pupil diameter repeatedly increased and decreased, with greater pupil diameter observed during negative haiku (especially TP3). Increase in pupil diameter also explained the beauty.
First, focusing on the emotional aspect, increase in positive emotion, arousal, and emotional ambiguity explained the beauty. This was consistent with the results of previous studies that reported positive relationships between positive felt valence (Hitsuwari & Nomura, 2022a) or arousal (Frame et al., 2024) and aesthetic evaluations with one-shot ratings (not gradual ratings). Furthermore, our study demonstrated that increased emotional ambiguity contributed to beauty, which was a novel finding. However, the expected decrease in negative emotions was not observed, presumably as the effect of the increase in positive emotions was greater and the effect was cancelled out. Studies should examine division by valence and also by dividing negative emotions into sadness, anxiety, suspense, among others (Menninghaus et al., 2017). Haiku appreciation with the joystick revealed a gradual increase in arousal during the appreciation process (Fig. 3b), which could be an important factor to consider in the assessment of emotions during haiku appreciation. This was consistent with psychophysiological findings that demonstrated increased electrodermal activity, a measure of emotional arousal, during poetry appreciation (Wassiliwizky et al., 2017). Simultaneously, Menninghaus et al. (2019) argued that aesthetic emotions could not be defined regarding high or low arousal. Instead, art that successfully combined high- and low-arousal emotions, not just one or the other, was more valuable (c.f., Mehl et al., 2023).
Second, focusing on the cognitive aspects, a decrease in cognitive ambiguity explained the haiku’s beauty. While a previous study (Hitsuwari & Nomura, 2022b) found a negative relationship between cognitive ambiguity and aesthetic evaluation of haiku (the higher the cognitive ambiguity, the lower the haiku’s beauty), this study extended that relationship. Cognitive ambiguity should be eliminated eventually rather than be avoided from the beginning. This was consistent with the fact that cognitive ambiguity was expected and enjoyed in haiku and poetry (Nitta, 2016; Wallot & Menninghaus, 2018). Furthermore, it was also highly consistent with the phenomenon of a dramatic increase in liking image stimuli at the moment of resolution of ambiguity, also called the aesthetic aha (Muth & Carbon, 2013). Studies that considered the relationship between ambiguity and art evaluation reported an inverted U-shaped relationship (Jakesch & Leder, 2009), which indicated that neither too little nor too much ambiguity was important for an aesthetic experience. Moreover, such studies suggested that ambiguity at the level where resolution was expected was ideal (Muth et al., 2019). Hitsuwari and Nomura (2022b) proposed to assess ambiguity as a factor that comprised both emotional and cognitive ambiguity, rather than as a single factor. In our study, the correlation between both ambiguities revealed that only moderate correlation (haiku level correlation in TP3 r = .57; Supplementary Table 2), which indicated that they were different concepts. Therefore, categorizing ambiguity and examining each relationship with aesthetic evaluations would be beneficial.
Third, increase and decrease in pupil diameter during haiku appreciation could reflect changes in emotion and cognition. In particular, pupil diameter expansion explained the haiku’s beauty. This result was consistent with findings that revealed a positive correlation between pupil diameter dilation and aesthetics of arts, including haiku (Niikuni et al., 2022), painting (Kuchinke et al., 2009) and music (Laeng et al., 2016).
Limitations and Future Directions
This study focused on the processes of emotional and cognitive changes during haiku appreciation, which have not been adequately examined. Although we present new findings, several issues require clarification. First, this study examined the haiku as a special form of poetry. Conducting a paradigm that focuses on similar change processes in other styles of poetry and literature is necessary. Second, although this experiment was conducted with Japanese participants via Japanese haiku, its application in other languages and cultural groups should be examined. 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 haikus at only one time point. Specifically, emotional changes, such as increased positive emotions, arousal, and emotional ambiguity, and cognitive changes, such as decreased cognitive ambiguity, affected the aesthetic evaluation of the haiku. Continuous evaluation of emotions via a joystick revealed the gradual movement of emotions. Positive and negative haikus revealed a gradual increase in positive and negative emotions, respectively. Furthermore, both haikus showed a similar gradual increase in arousal. Additionally, pupil diameter was assessed to exploratorily examine unconscious responses to the haiku and explain its beauty.
Haiku, with its controlled shortness, was a suitable stimulus to capture these changes and could potentially advance the study of verbal art, which was a frontier compared with other art forms, such as visual art. Specifically, three lines (5-7-5 morae) can be displayed at fixed timings, and owing to their short length, numerous poems can be appreciated in a single experiment. These results also demonstrated the importance of participants’ evaluation of the process of change in a step-by-step manner and continuous measurement of their reactions during appreciation. The measurement of evaluation at one time point was 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.
Finally, this study sheds light on the psychometric and reproducibility issues in this area (Nosek et al., 2022). Specifically, a sample size derivation based on multi-level analyses and various analytical methods, including Bayesian analysis, was devised (Arend & Schäfer 2019; Etz & Vandekerckhove, 2018). Future research should conduct more psychometrically valid research on art appreciation and aesthetic evaluation.
Declaration of Interest
The authors declare no conflicts of interest.
Note
The example haiku in Fig. 1 means “Thinner and thinner grows the moon, and then it's gone. The night is colder.”
Funding
This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 19H01773, 23K22374, 22KJ1813, and by the Overseas Research Fellowship.
Acknowledgements
We would like to thank the supporters of the monthly academic crowdfunding platform academist (https://academist-cf.com/fanclubs/264) for their contributions. We also extend our gratitude to Editage (https://www.editage.com) for their assistance in English editing.
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Competing interests: The authors declare no competing interests.