Effects of liking on visual attention in faces and paintings

Acta Psychologica 197 (2019) 115–123

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Effects of liking on visual attention in faces and paintings☆,☆☆ ⁎

T

Juergen Goller , Aleksandra Mitrovic, Helmut Leder Department of Basic Psychological Research and Research Methods, University of Vienna, Vienna, Austria

A R T I C LE I N FO

A B S T R A C T

Keywords: Liking Visual attention Eye tracking Facial attractiveness Artworks Aesthetics

The visual aesthetics of an object increases visual attention towards the object. It is argued that this relation between liking and attention is an evolutionary adaptation in sexual and natural selection. If this is the case, we would expect this relation to be domain specific, and thus, stronger for biological than for non-biological objects. To test this hypothesis, we conducted two eye-tracking studies, in which we compared the relation between liking and gaze patterns in images of biological (faces) and non-biological (paintings) stimuli. In Study 1, we presented randomly combined image pairs for 20 s in a free-viewing paradigm. Participants then selected the image they liked more in a 2-AFC task and rated the liking of each image on a Likert-scale. In Study 2, we employed the same paradigm but this time, images were combined based on pre-rated liking to ensure that images in each pair were clearly different. In both studies, we found a strong relation between liking and visual attention. Against our expectations, these effects were of similar magnitude for faces as for paintings. We conclude that the relation between liking and visual attention is not limited to biological objects but that its effects are domain general. The evolutionary function of the relation between liking and visual attention might stem from evolutionary adaptations, nonetheless, this link seems to be a rather basic phenomenon that applies across domains.

1. Introduction

Turano, 2011; Mitschke, Goller, & Leder, 2017), everyday items, architecture, detergents, or simple shapes (Holmes & Zanker, 2012). A special object domain in visual perception is food, as its consumption is essential for survival and natural selection. It is therefore not surprising that for images of food, clear associations have been found between preference ratings and visual attention (Doolan, Breslin, Hanna, Murphy, & Gallagher, 2014; Werthmann et al., 2011). However, it is important to stress that especially for food, preference ratings might mostly reflect associations with taste, past experiences, and certain expectations rather than merely aesthetic and visual evaluations. In most of the studies investigating the relation between liking and visual attention, the visual preference was not part of the experimental task itself. Because the aesthetic quality of the stimuli is not part of the procedure, these studies show a relatively unbiased relation between preference and visual attention. In a related line of research, where participants indicated the preferred items or images in a preference task, a similar relation was found between preference ratings and visual attention. It was shown that with the progression of the decision, visual attention is shifting towards the preferred item in a systematic way. For this preference tasks, systematic patterns have been shown for various object domains, such as faces (Shimojo, Simion, Shimojo, & Scheier,

For many objects in everyday visual perception, we can tell whether we like them or not, even if we have very little information about the object (Leder, Belke, Oeberst, & Augustin, 2004; Olson & Marshuetz, 2005; Russell & George, 1990; Willis & Todorov, 2006). This includes objects and domains that are typically associated with aesthetics, like artworks, landscapes, or faces, but also includes everyday objects, like chairs, water bottles, or even the font of this manuscript. This inherent evaluative component in visual perception influences visual attention. In early face-to-face free-viewing studies, it was shown that more attractive faces where looked at longer compared to less attractive faces (Fugita, Agle, Newman, & Walfish, 1977; Kleck & Rubenstein, 1975). This relation between facial attractiveness and visual attention has been repeatedly replicated in eye tracking studies (Langlois, Ritter, Roggman, & Vaughn, 1991; Leder, Mitrovic, & Goller, 2016; Leder, Tinio, Fuchs, & Bohrn, 2010; Maner et al., 2003; Maner, Gailliot, Rouby, & Miller, 2007; Mitrovic, Tinio, & Leder, 2016; Slater et al., 1998; Valuch, Pflüger, Wallner, Laeng, & Ansorge, 2015). The same relation has been found for other object categories, such as images of artworks (Brieber, Nadal, Leder, & Rosenberg, 2014; Heidenreich & ☆

We thank Eva Specker for her help and Imani Rameses and Marleen Schönfeld for their help with data collection. This research was supported by a grant of the Austrian Science Fund (P27355). ⁎ Corresponding author. E-mail address: [email protected] (J. Goller). ☆☆

https://doi.org/10.1016/j.actpsy.2019.05.008 Received 25 June 2018; Received in revised form 10 May 2019; Accepted 13 May 2019 Available online 27 May 2019 0001-6918/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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subsequent blocks. To test our main hypothesis, we analyzed the interaction between liking and the domain on the eye-tracking parameters. In addition, we also explored the temporal course and linearity of the effects. Taste differs, and people do not necessarily like the same faces or paintings (Cupchik & Gebotys, 1988; Furnham & Walker, 2001; Hönekopp, 2006; Vessel & Rubin, 2010). For example, it has been shown that individual taste as compared to shared taste accounts for about 40% in faces and for about 75% in paintings (Leder, Goller, Rigotti, & Forster, 2016). To account for this individual taste, we did not average the data, but analyzed the individual ratings and the individual gaze patterns of the participants. In Study 2 we used pre-assigned image pairs to address the issue of causality. Previous studies have shown that effects of sex are likely in evaluating faces (Leder, Mitrovic, & Goller, 2016; Maner et al., 2007; Mitrovic et al., 2016) and paintings (Polzella, 2000; Rawlings, 2003). To avoid such effects and to keep the study design straightforward, we only tested women and only used male faces in our studies.

2003), simple patterns (Isham & Geng, 2013), photographic art (Glaholt & Reingold, 2009), computer-generated fractals (Foulsham & Lock, 2015), food items (Krajbich & Rangel, 2011), and as a comparison between faces and brand logos (Glaholt & Reingold, 2009). However, it seems that the attention shift reflects general decision making rather than the visual preference itself (Glaholt & Reingold, 2009). In this paradigm, it is difficult to distinguish between visual attention attributed to the process of decision making and visual attention attributed to the visual appeal or aesthetics of an image. We therefore limit ourselves to the free-viewing paradigm, in which—across several studies—a positive relation between visual aesthetics and visual attention was found: The more people like an object, the more they look at it. This was repeatedly shown for faces, and although the few free-viewing attempts that have been made with object domains other than faces show the same pattern, its effects are less consistent. Although different domains have been used in previous studies, no study directly compared different object categories. The paradigms and research designs differ between studies, which makes hypothesis testing across two different studies hard to justify. We argue that testing the hypothesis that there is a difference between object domains, requires both object domains to be presented in the same study design and analyzed in one statistical model. Previous research was mainly concerned with faces, presumably because facial attractiveness is directly connected to evolutionary theory, which allows for the testing of specific hypotheses based on a well-founded theory (Darwin, 1871). Faces are a basic biological domain and face processing evolved over a long period (Leder, Goller, Forster, Schlageter, & Paul, 2017; Little, 2014). Facial attractiveness is partially seen as the result of evolutionary adaptions over the phylogenetic past of human ancestors (Grammer, Fink, Moller, & Thornhill, 2003; Little, Jones, & DeBruine, 2011; Rhodes, 2006). Being evaluated as attractive is beneficial for being selected as a mate, in that highly attractive faces motivate sexual behavior and indicate genetic fitness (cf. sexual selection). Thus, the attention-binding effect of facial attractiveness can be interpreted as a function of an evolutionary derived motivational drive (Leder, Mitrovic, & Goller, 2016). The relation between liking and attention is evolutionary adaptive because it is beneficial to sexual selection. As opposed to faces, human artefacts and non-biological objects have evolved over a much shorter period. Visual arts are the prototype of a non-biological domain, where aesthetic qualities and liking play a major role (Leder & Nadal, 2014). Although for example early cave paintings date back at least 35,000 years in humans (White et al., 2012) or even 65,000 years in Neanderthals (Hoffmann et al., 2018), it is unlikely that evolution would impact on perceptual mechanisms under these short timeframes (Dissanayake, 2007; Zaidel, Nadal, Flexas, & Munar, 2013). It is more likely that effects of liking on visual attention for non-biological objects are not the direct product of evolutionary adaptations, but generalized effects derived from the perception of biological object categories like faces. Paintings can depict any content often including biological and natural elements. This includes humans and human faces, animals, landscapes, food, and many others. The content depicted by paintings therefore could also be often categorized as biologically relevant, at least in a more abstract way than in photographs. It was therefore important to select paintings that most likely do not fall into the category of biological stimuli. We selected paintings that are clearly recognizable as artificial stimuli and do not depict single faces (e.g., portraits), food (e.g., fruit still life), or landscapes. We hypothesize that the relation between liking and visual attention should be domain specific and stronger for biological objects than for non-biological objects. To test this hypothesis, we compared the correlation between liking and visual attention, as measured by the total fixation duration (TFD), fixation count (FC), and mean fixation duration (MFD). In two studies, we presented pairs of faces and pairs of paintings in an eye-tracking free-viewing paradigm. To measure liking, we asked people to rate the images on a Likert-scale and make 2-AFC choices in

2. Study 1 2.1. Method 2.1.1. Participants A sample of 57 female psychology students (Mage = 21.26 years, SDage = 3.45, Mdnage = 20, age range: 18–36 years) gave written consent before they participated for course credit. All participants had normal or corrected to normal vision. Both studies were approved by the university's ethics committee. 2.1.2. Stimuli We used 60 images of paintings and 60 images of faces, selected from a lager stimulus set based on pre-ratings. The original set of paintings consisted of 196 representational paintings from various Western art periods between the 16th and 21st century (see Table A1 in the appendix for a complete list of the paintings). These paintings are part of a larger set of our own database of images of paintings which was arranged to conduct empirical studies. All paintings selected for this study belong to a category labeled “scenes”, precluding other categories labeled as “portraits”, “still lifes”, or “landscapes”. The scenes significantly vary in style and content, depicting all forms of representational and abstract visual elements. Whereas for example some of the older Renaissance paintings show groups of people in a scenery in a relatively representational way, most of the paintings show human or facial elements at most in an alienated and unrealistic way. In a prestudy, 120 participants rated the 196 paintings for liking and familiarity on seven-point Likert scales. We took the 102 paintings that showed a familiarity score below 2.00 to minimize potential familiarity effects and randomly selected 60 paintings for the study. The original set of faces consisted of 143 images of white male faces used in previous studies (Leder, Goller, et al., 2016; Schacht, Werheid, & Sommer, 2008). We took the attractiveness ratings from Leder, Goller, et al. (2016) and matched the distribution of liking ratings for faces as closely as possible to the distribution of liking ratings for paintings (faces: M = 3.69, SD = 0.92, range: 2.16–5.47; paintings: M = 3.79, SD = 0.87, range: 2.10–5.40). All stimuli were shown on a gray background. Faces were shown in portrait format with a size of 413 × 537 pixels. Paintings were shown in landscape format and rescaled to 850 pixels in width, while preserving the original aspect ratio, resulting in heights ranging between 367 pixels and 847 pixels. 2.1.3. Apparatus The study was conducted in a dimly lit room using a desktopmounted eye tracker (1000 Hz, 9-point calibration and validation, EyeLink 1000, Experiment Builder Version 1.10.1630, SR Research Ltd., Ottawa, ON, Canada). Only the dominant eye was tracked. The images were presented in a distance of 70 cm (Samsung SyncMaster 116

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2443BW, 24″, 16:9; 1920 × 1200 pixels, 60 Hz, Samsung Group, Seoul, South Korea). To minimize head movements, participants' heads were stabilized using a chin rest. Visual acuity, color vision, and oculomotor dominance were assessed before the study.

Table 1 Descriptive statistics of the eye tracking parameters. Faces

2.1.4. Procedure The study consisted of four blocks. In the first block (free-viewing), pairs of faces or pairs of paintings were presented side by side on a light gray background. Pairs of faces and pairs of paintings were randomly combined, irrespective of the pre-ratings. Thus, each pair differed on a range from insignificant to pronounced differences in liking. Participants were randomly assigned to the faces (n = 28) or paintings (n = 29) condition and instructed to view the images freely without any required decision or task. The image pairs were randomly created on a trial-by-trial base and each image only appeared once in each block. Each image pair was presented for 20 s, sufficient time to inspect both images (gf. Locher, Smith, & Smith, 1999). In between, a fixation cross appeared in the center of the screen, which participants were required to fixate within five seconds to proceed to the next trial. If this fixation failed because of a gaze drift, the eye tracker was re-calibrated. Eye movements were only recorded during this first block. In a second block (2-AFC block), the same 30 image pairs were presented again in random order. During the presentation, participants indicated via key press which of the two images they liked more without time constraints. In a third block (rating block), the same 30 image pairs again were presented in random order, but this time, participants rated each image for liking on a 7-point Likert scale ranging from 1 (not at all) to 7 (very much). Whether the left or the right image was to be rated first, randomly changed from trial to trial and was indicated by a small dot above the according image. We used the term liking (German original: Gefallen) to refer to the aesthetic component in visual perception, because it is applicable to both, faces and paintings. In a fourth and final block (familiarity block), all 60 images were individually presented, and the participants indicated whether they already knew the face or the painting before the study (yes or no). Afterwards, all participants indicated their age. Participants in the face group were additionally asked for their sexual orientation (Mitrovic et al., 2016) and participants in the painting group were asked questions about their art interest (self-developed scale; see Jakesch, Goller, & Leder, 2017). Participants were further asked in an open-answer-format, on which aspects they were mainly focusing in making their decisions. These post-study questions were collected for different studies and were not analyzed here.

TFD (ms) TFD Δ (ms) FC FC Δ MFD (ms) MFD Δ (ms)

Paintings

M

SD

Range

M

SD

Range

8451 3431 28.28 10.25 299 37.65

2700 3672 9.38 11.07 191 43.27

222–17,209 0–16,694 1–61 0–59 100–5684 0–265,89

8312 4438 29.32 14.00 284 43.46

3275 3956 11.04 12.71 188 47.24

106–18,601 0–16,958 1–69 0–66 100–6371 0–342.68

Note. Δ (Delta) refers to the difference between each image in each trial.

Team, 2008) applying Satterthwaite approximation for p-values (lmerTest, version 2.0–32; Kuznetsova, Brockhoff, & Christensen, 2016). For the LMMs, we aggregated the data to a Participant × image structure. We included a contrast for domain (faces minus paintings) as a fixed between-subjects factor and the liking ratings as a centered, continuous fixed factor. We also included an interaction between domain and liking to test whether the relation between liking and visual attention differs for faces and paintings. Because each image is presented together with another image, the eye-tracking measures for one image depend on the measures for one specific other image. This could be potentially problematic because the assumption of independence of observations could be violated. The best way to overcome this dependency would be to include random by-trial intercepts. However, the inclusion of random by-trial intercepts did not add any explanatory power to the model. We therefore only included random by-image and by-participant intercepts to account for the dependency of observations. As the paintings varied in size, we additionally included image size in number of pixels as a random intercept. We also included a random slope for liking for all random effects but no random slopes for domain, as otherwise the models failed to converge.1 By plotting and inspecting the model residuals, we detected no violations of linearity, homoscedasticity, or normality. We detected a small collinearity between liking and the Liking × Domain interaction, r = 0.44; all other correlations were negligible, |r| ≤ 0.11. In all the following, Est. (estimate) refers to the estimated means of the fixed-effects parameters from the fitted model as given by the lme4 package (Version 1.1–8; Bates et al., 2015; Bates et al., 2013). For TFD (intercept: Est. = 8393.95, SE = 338.00), we found a significant effect of liking, Est. = 531.72, SE = 89.75, t(4.90) = 5.92, p = .002, no effect of domain, Est. = 82.99, SE = 675.99, t (14.70) = 0.12, p = .904, and no Liking × Domain interaction, Est. = 10.44, SE = 179.50, t(4.90) = 0.06, p = .956. We found the same pattern for FC (intercept: Est. = 28.87, SE = 1.45), with an effect of liking, Est. = 2.71, SE = 0.39, t(3.20) = 6.93, p = .005, no effect of domain, Est. = −1.21, SE = 2.90, t(24.46) = −0.42, p = .679, and no interaction, Est. = −0.22, SE = 0.46, t(3.20) = −0.48, p = .662. For MFD (intercept: Est. = 28.87, SE = 1.45), we found no significant effects of liking, Est. = 5.91, SE = 13.22, t(33.81) = 0.45, p = .658, or of domain, Est. = 18.76, SE = 15.46, t(34.95) = 1.21, p = .233, and no significant interaction, Est. = −3.12, SE = 26.44, t(33.81) = −0.12, p = .907. Fig. 1 shows the effect of liking on TFD for faces and paintings. We repeated the three LMMs, but instead of the liking ratings, we entered a contrast for preference from the 2-AFC block (preferred minus non-preferred) as fixed factor. We found the same pattern for all effects and interactions as for the liking ratings: For TFD (intercept: Est. = 8381.31, SE = 266.10), we found a significant effect of preference, Est. = 2227.76, SE = 435.33, t(13.78) = 5.12, p < .001, no

2.2. Results 2.2.1. Eye-tracking data We started with the fixation report provided by Data Viewer software (SR Research Ltd., Ottawa, ON, Canada). We only analyzed fixations, which fell on one of the two images and did not include saccades or blinks in our analyses. We omitted all fixations that were shorter than 100 ms and all trials, in which participants indicated that they were familiar (68 out of 3420 images) with at least one of the two images (6336 fixations in total). This resulted in 94,674 fixations that were included in the analysis. As dependent variables, we analyzed the total fixation duration (TFD), which is the dwell time for each image in milliseconds, the fixation count (FC) for each image, and the mean fixation duration in milliseconds (MFD). Table 1 shows the descriptive statistics of the three eye tracking parameters for each image independently and the difference between the images in each trial. 2.2.2. Liking and visual attention For each eye-tracking parameter (TFD, FC, and MFD), serving as a dependent variable, we ran a linear mixed model (LMM) using the lme4 package (Version 1.1–8; Bates, Machler, Bolker, & Walker, 2015; Bates, Maechler, & Bolker, 2013) in R (Version 3.1.0; R Development Core

1 R code: LMM < - lmer(TFD/FC/MFD ~ king|pixels) + (liking|subject) + (liking|image), control = lmerControl(optimizer = “bobyqa”))

117

liking * domain + (lina.action = na.omit,

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smaller for the LMM (AIC: 24939, BIC: 24977) than for the NLMM (AIC: 24977, BIC: 25015), indicating a slightly better fit for the linear model. Fig. 2 shows the (linear) relation between the differences in the eyetracking parameters (TFD, FC, and MFD) and in the liking ratings. 2.2.4. Time course Time can be an important and crucial factor in aesthetic experiences (Leder & Nadal, 2014). However, whether time has a systematic influence on aesthetic judgments is still debated. Whereas some studies showed little or no effect of viewing time on aesthetic judgments (McWhinnie, 1993; Smith, Bousquet, Chang, & Smith, 2006), others showed positive or negative effects (Berlyne, 1970; Bornstein, 1989; Leder, Gerger, Brieber, & Schwarz, 2014; Maslow, 1937; Mita, Dermer, & Knight, 1977; Zajonc, 1968). Therefore, we additionally explored the temporal aspects. To do this, we cut the 20 s presentation time into onesecond segments and calculated the percentage of the fixation duration for the preferred image for each trial (Fig. 3). We plotted the mean values separately for faces and for paintings, which showed a similar and consistent time course: the tendency for preferred images to be looked at longer starts early after stimulus onset. The first 15 s resemble a flat inverted u-shaped curve. For the last five seconds, the fixation duration for the preferred image increases again, especially for paintings. 3. Study 2

Fig. 1. Relation between liking ratings and total fixation duration (TFD), plotted separately for faces and paintings. The graph shows a linear relation between liking and TFD for faces and paintings alike. Even though we used individual ratings for the analysis, the figure shows averaged data for illustrative purposes. Error bars show standard errors.

We hypothesized that liking influences viewing time, but it is also possible that viewing time influences liking ratings (Bornstein, 1989; Glaholt & Reingold, 2009; Shimojo et al., 2003). According to the gazecascade-effect, liking and gaze form an interacting feedback loop: We tend to look longer at what we like—this increased visual attention leads to an increase in liking, which again increases our visual attention, and so on (Shimojo et al., 2003). In Study 1, we paired two images in random fashion, irrespective of their pre-ratings. This random allocation allows correlational analyses but does not allow any inferences about causality. To make a more direct approach towards causality, we conducted an experiment where we paired images based on their preratings. This produced a clear difference in liking for each image pair, in which one image was clearly liked more than the other. We used only paintings for Study 2, because for faces several similar pre-assigned liking studies have already been conducted (Leder et al., 2010; Maner et al., 2003; Mitrovic et al., 2016; Shimojo et al., 2003).

effect of domain, Est. = 140.00, SE = 532.21, t(8.066) = 0.26, p = .799, and no Preference × Domain interaction, Est. = −95.06, SE = 870.65, t(13.78) = −0.11, p = .915. For FC (intercept: Est. = 28.86, SE = 1.27), we found an effect of preference, Est. = 6.78, SE = 1.32, t(13.36) = 5.13, p < .001, no effect of domain, Est. = −1.16, SE = 2.54, t(17.69) = −0.46, p = .652, and no interaction, Est. = −0.21, SE = 2.64, t(13.36) = −0.08, p = .939. For MFD (intercept: Est. = 298.16, SE = 11.43), we found no significant effects of liking, Est. = 13.65, SE = 16.38, t(9.84) = 0.83, p = .424, or of domain, Est. = 20.35, SE = 22.87, t(24.03) = 0.89, p = .382, and no interaction, Est. = −5.79, SE = 32.76, t(9.84) = −0.18, p = .863. 2.2.3. Relative differences In addition to the main hypothesis, we also tested whether the differences in TFD and the differences in liking show a linear or nonlinear relation. In doing so, we calculated the differences for TFD and liking for each image pair. We ran a LMM2 and a non-linear mixed model (NLMM),3 both with the difference in TFD as a dependent variable. In the LMM, the difference in liking was modelled as a linear fixed factor and in the NLMM, the difference in liking squared was modelled as a quadratic fixed factor. Both models included a contrast for domain as fixed factor, and random by-participant and random bytrial effects. For both models, we found an effect of difference in liking (LMM: Est. = 1097.35, SE = 87.53, t(1618.8) = 12.54, p < .001; NLMM: Est. = 192.17, SE = 17.86, t(1637.70) = 10.763, p < .001), but no effect of domain (p ≥ .940) and no interaction (p ≥ .361). We compared the models using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Both values were marginally

3.1. Method A different sample of 30 female psychology students (Mage = 21.26 years, SDage = 3.45, Mdnage = 20, age range: 18–36 years) gave written consent before they participated for course credit. All participants had normal or corrected to normal vision. The apparatus, the design, and the procedure were the same as in Study 1. The only difference was the assignment of the image pairs: We started with the same 102 unfamiliar paintings as in Study 1 but selected the 30 least liked and the 30 most liked paintings based on the ratings of same pre-study. We then combined the least liked image from the least-liked pool with the least-liked image from the most-liked pool, the second-toleast-liked image with the second-to-least-liked image, and so on. That gave us rather similar differences for each image pair, ranging from 1.71 to 2.20 (M = 1.88; on a 7-point Likert scale). Each participant saw the same 30 image pairs. 3.2. Results

2 R code: LMM < - lmer(TFD/FC/MFD ~ liking.difference * domain + (1|subject) + (1|trial), na.action = na.omit, control = lmerControl (optimizer = “bobyqa”)) 3 R code: NLMM < - lmer(TFD/FC/MFD ~ I(liking.difference^2) * domain + (1|subject) + (1|trial), na.action = na.omit, control = lmerControl (optimizer = “bobyqa”))

After deleting all fixations outside the images, fixations shorter than 100 ms, and fixations for familiar image pairs, 51,702 fixations remained for analyses. We ran three LMMs4 with TFD, FC, and MFD as dependent variables and included a contrast for pre-rated liking (liked 118

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Fig. 2. Relation between the differences in TFD, FC, and MFD for each image pair and their difference in liking. Each dot represents the interaction of one participant with one image pair. Four extreme values in MFD (4173, 917, −635, & −659) are not depicted by this figure.

more minus liked less) as a fixed between-subjects factor. We also included random by-image, by-participant, and by-image size intercepts with no slopes for pre-rated liking. We observed no violations of linearity, homoscedasticity, or normality. We found effects of pre-rated liking for TFD (intercept: Est. = 8371, SE = 183), Est. = 2436, SE = 351.20, t(57.99) = 6.94, p < .001, for FC (intercept: Est. = 28.76, SE = 6.97), Est. = 6.59, SE = 1.22, t(58) = 5.39, p < .001, and MFD (intercept: Est. = 293.36, SE = 6.97), Est. = 21.94, SE = 4.37, t(48.47) = 5.02, p < .001. Table 2 shows all mean values, their differences, and effect sizes (Cohen's d). 4. Discussion We tested the hypothesis that liking of an object influences how long we look at that object and the hypothesis that this effect would be stronger for biological than non-biological objects. To this aims we presented pairs of faces and pairs of paintings in a free-viewing paradigm and measured participants' liking evaluations and gaze patterns. We found a strong positive correlation between liking ratings and the total fixation duration (TFD). This main effect of liking is a further replication of previous findings for faces (Fugita et al., 1977; Kleck & Rubenstein, 1975; Leder, Mitrovic, & Goller, 2016; Leder et al., 2010; Mitrovic et al., 2016) and paintings (Brieber et al., 2014; Heidenreich & Turano, 2011; Mitschke et al., 2017). The effect of liking on the total fixation duration is strong: an increase of one point in liking on the seven-point Likert scale increased the gaze duration by > 500 ms. The effect was even stronger when we presented images with a pre-rated difference in liking (Study 2). Images that where liked more were looked at for 2400 ms longer during the 20 s presentation time than images that were liked less, which translates to a Cohen's d of 1.79. This effect size clearly exceeds the effect sizes usually found for the opposite effect, the mere exposure effect, which usually is below 0.5 (Bornstein, 1989). This large effect size suggests that liking somehow influences visual attention and the relation found in Study 1 is not exclusively a byproduct of increased visual attention. However, it does not contradict the role of the mere-exposure effect (Glaholt & Reingold, 2009; Zajonc, 1968) or the gaze-cascade model (cf. Shimojo et al., 2003). We hypothesized that the relation between liking and visual attention would be stronger for faces than for paintings. Our data does not support this hypothesis: we found no interaction between liking and the two object domains. Thus, the liking of paintings guided visual attention to the same extent as the liking of faces. We expected the relation to be stronger for faces because faces are a biological domain highly relevant for sexual selection and facial attractiveness is the product of evolutionary adaptation (Grammer et al., 2003; Little et al., 2011;

Fig. 3. Relative likelihood between the preferred and non-preferred image to be looked at over the 20 s presentation time, plotted separately for faces and paintings. Error bars show standard errors and the horizontal line shows the 50% chance level. Table 2 Mean values for Study 2. Liked less

TFD FC MFD

Liked more

Difference

M

SD

M

SD

7153 25.46 282.67

1357 4.88 13.68

9589 32.04 304.06

1364 4.58 20.07

Cohen's d 2436 6.58 21.39

1.79 1.39 1.25

Note. Difference computes as liked more minus liked less. Cohen's d is calculated using the pooled SD as denominator.

4 LMM < lmer(TFD/FC/MFD ~ liking.pre + (1|pixels) + (1|subject) + (1|image), na.action = na.omit, control = lmerControl(optimizer = “bobyqa”))

119

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We also analyzed whether the relation between liking and visual attention is rather linear or non-linear in regressing the difference in liking for each image pair towards the difference in TFD. Our analysis shows no additional benefit of a non-linear model, which suggests that a linear model reflects the pattern of the data: The more you like an object, the longer you look at it. Our explorative analysis of the time course is relatively new in this line of research and revealed a systematic pattern for both object categories. The shift towards the preferred image starts quickly after stimulus onset. The ratio between the preferred image and visual attention follows a flat inverted u-shape over the first 15 s of presentation time. For the last five seconds, visual attention again seems to shift towards the preferred image. We did not include time in the statistical models, because we had no specific hypotheses about interactions between any of the factors and time. However, these findings, summarized in the graphs shown in Fig. 3, could inspire future research to test temporal aspects in more detail, formulate specific hypotheses, and include time into statistical analyses, presumably as a non-linear factor. Such dynamic eye-tracking measures could in future research also be compared with other online measures allowing temporal analyses (for example fEMG; Jakesch et al., 2017). Our studies have some limitations. Second, we presented two isolated images on a screen in the laboratory. This raises the question, if our findings can be generalized to everyday life. On the one hand, real world visual perception is much more complex and complicated than the experimental paradigm. For example, it has been argued that the real experience of a paining in the museum cannot simply be reproduced on a screen in the laboratory (Augustin & Wagemans, 2012; Brieber, Nadal, & Leder, 2015; Locher et al., 1999). The same applies to faces, where systematic differences between real faces and photographs of faces exist on several dimensions (Risko, Laidlaw, Freeth, Foulsham, & Kingstone, 2012). Future research should investigate whether the relation between liking and visual attention systematically differs in the real world compared to the laboratory. On the other hand, the importance of social media increases every day. In that sense, the putatively artificial lab environment might not be that artificial after all. Third, we only tested women and showed them male faces but women and men systematically differ in their liking-related response in many ways (e.g., Buss, 1995; Buss & Schmitt, 1993; Feingold, 1990; Furnham & Walker, 2001; Kret & De Gelder, 2012). Investigating interactions with sex could be an interesting avenue for future evolution-based research questions. Fourth, we only used participants considered as lay people in art, but there are differences regarding the expertise and the interest of perceivers in judging paintings (Antes & Kristjanson, 1991; Leder et al., 2014; Locher, Smith, & Smith, 2001; Vogt & Magnussen, 2007; Vogt & Magnussenô, 2007). It could be a critical test for functional evolutionary aspects, whether art experts show reduced sensitivity for paintings, but not for faces. To sum up, we found a strong relation between individual liking preferences and visual attention for faces as well as for paintings. This relation is mostly driven by an increased number of fixations (supported by both studies), but also by an increased mean fixation duration (supported only by Study 2). For the first time, we showed systematic temporal patterns over 20 s presentation time for both object categories. We did not find systematic differences between faces (biological domain) and paintings (non-biological domain): The functions of aesthetics in guiding visual attention might stem from evolutionary adaptations, nonetheless, this relation seems to be a rather basic phenomenon that applies across domains.

Rhodes, 2006). For human artefacts like paintings on the other hand, such evolutionary function is less likely, which makes liking for nonbiological objects a by-product of liking for biological objects (Dissanayake, 2007; Zaidel et al., 2013). Paintings in general and the paintings used in these studies are a versatile object domain. Some paintings show abstract but also representational elements that contain human or animal faces and face-like elements. It is therefore reasonable that the same basic mechanisms driving visual attention in human faces apply to the biological objects depicted in the paintings. Although this cannot fully be tested by the current study design, there are at least three reasons why such mechanism is unlikely: First, the paintings are clearly recognizable as artificial stimuli as they introduce obvious style, brushstrokes, or abstraction elements. Only some Renaissance paintings might be taken as realistic depiction, but even they are clearly categorized as artificial stimuli. Thus, although the paintings depict biological elements, the paintings are presumably being not considered as a ‘biological’ human category by the participants. Second, most paintings that depict faces or biological elements show them in a highly distorted, alienated, and abstract way. Only a few paintings depict faces of people in a relatively realistic way. This makes it unlikely that these depictions are a biological object domain as compared to the photographs of real faces. Third, all paintings that contain facial or human elements, contain more than one single element. This makes it highly unlikely that the liking ratings for the paintings as a whole are systematically driven by single facial or human elements. Although these reasons make it unlikely that direct biological factors caused the relation between liking and visual attention and paintings, future studies could further test this assumption by using completely abstract images showing no representational elements. This would rule out the possibility that human-like elements as a semantic distinct category drive the likingattention-relation. However, it would not rule out that the same basic visual features driving liking for biological objects like faces also apply to abstract paintings or images. For example, symmetry is not only considered attractive in faces, where it might indicate low fluctuating asymmetry in potential mates (e.g., Schaefer, Fink, Grammer, & Mitteroecker, 2006), it is also an important factor in liking of nonbiological stimuli (e.g., McManus, 2005). Conceivably, such basic lowlevel features—deliberately or not—might influence the creation of paintings and consequently work on a domain general level. First, we did not control the images for any low-level visual features like complexity, saliency, balance, et cetera. Such low-level features can play an important role in visual attention and could be confounded with liking (e.g., Berlyne, 1970; Gartus & Leder, 2013; Leder, Gerger, Dressler, & Schabmann, 2012; Locher, Gray, & Nodine, 1996; Nodine, Locher, & Krupinski, 1993). It would be valuable to investigate how low-level features interact with liking in influencing visual attention. Based on the effect sizes in both studies, the effect of liking on TFD was largely due to an increased number of fixations (FC). Mean fixation duration (MFD) played a smaller role. The effect of liking on MFD was only significant in Study 2, with a difference of 21.39 ms and a mean difference in liking of 1.88 between both images. In Study 1, the effect did not reach significance, but showed a slope of 5.91 ms per one point of liking, which corresponds to a difference of 11.11 ms compared to Study 2. Thus, the effect of liking on MFD was stronger in Study 2. Our results do not challenge that the function of liking on visual attention in faces is the product of sexual selection. However, the effects of liking on attention seem to be a rather basic and domain general phenomenon. Its effects are not limited to biological objects like faces or bodies, but to the same extent can also apply to non-biological objects like paintings. Appendix A Table A1

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List of paintings. Title

Artist

(Untitled) Fallen Angel A Bar at the Folies-Bergère A Street in Paris in May 1871 Acker Alter Billardspieler- vor ihm sein vertrauter, persönlicher Gegner - aber hinter ihm - wer ist das? An Experiment on a Bird in the Air Pump Apollo and Marsyasa Apollo in the Forge of Vulcan Apple Picking at Eragny-sur-Epte Astonishment of the Mask Wouse (L'étonnement du masque Wouse) At Père Lathuille'sb At the light of a candle, three men study a small replica of the Borghese gladiatora Bathers Bathers of Beach Scenes Belshazzar's Feast (Gastmahl des Belsazar) Birthdayb Breton Women at a Wall Bridge of the Wind (Windsbraut) Charon Crossing the Styxb Christ and the Virgin in the House at Nazaretha Cupid and Psychea David Praised by the Israelite Women Death and the Woodcuttera Dustheadsb George Clive and his family with an Indian maidb Hospital Italian Comediansb Jawlensky and Werefkinb Manao tupapau (Spirit of the Dead Watching) Marriage A-la-Mode. 1, The Marriage Settlementa Martyrdom of Saint Philipb Mr and Mrs. Andrewsa Nightmareb Ohne Titel (mit Reinhard Stangl)b Orientala Pierrotb Place de la Concordb Regattasb Rendevouz der Freunde Repressionb Room in New York Rugby (Les Jouers de Rugby) Sabina von Steinbach Scene from the Destruction of Messinab Second Version of Triptych 1944 Skeleton Painter in his Studio Stag at Sharkey'sa Subway Sunday, Women Drying Their Hair2 Susanna and the Eldersb Tables for Ladiesa The Burial of Atala The Card Players (Barnes Foundation, Philadelphia) The City The Corn Siftersa The Delugeb The Denial of Saint Petera The Europe Bridge (Le Pont de l'Europe) The Glass of Winea The Intrigue The Laugh The Lifeboat is Taken through the Dunes The Meeting (Die Versammlung)a The mowers of Luzerneb The Newborn Childb The Night The Night-Hag Visiting Lapland Witchesa The Pirates The Potato-Eatersa The Rowerb The Suicidea The Tapestry Weavers, or The Fable of Arachnea The Visit The Wrestlersa There Is No Finished World

Basquiat, Jean-Michel Manet, Édouard Luce, Maximilien Jules Baselitz, Georg Hausner, Rudolf Wright of Derby, Joseph Ribera de, Jusepe Velázquez, Diego Pissarro, Camille Ensor, James Manet, Édouard Wright of Derby, Joseph Cézanne, Paul Rothko, Mark Rijn van, Rembrandt Chagall, Marc Bernard, Émile Kokoschka, Oskar Patinir, Joachim Zurbarán de, Francisco Reynolds, Joshua Brugghen ter, Hendrick Millet, Jean-François Basquiat, Jean-Michel Reynolds, Joshua Lassnig, Maria Watteau, Jean-Antoine Münter, Gabriele Gauguin, Paul Hogarth, William Ribera de, Jusepe Gainsborough, Thomas Janmot, Louis A.R. Penck Kandinsky, Wassily Macke, August Degas, Edgar Dufy, Raoul Ernst, Max Rivera, Diego Hopper, Edward Lothe, André Schwind von, Moritz Ludwig Beckmann, Max Bacon, Francis Ensor, James Bellows, Georges Tooker, George Sloan, John Honthorst van, Gerrit Hopper, Edward Girodet-Trioson, Anne Louis Cézanne, Paul Grosz, George Courbet, Gustave Comerre, Léon François Caravaggio da, Michelangelo Merisi Caillebotte, Gustave Vermeer, Jan Ensor, James Boccioni, Umberto Ancher, Michael Lindner, Richard Dupré, Julien Tour de la, Georges Beckmann, Max Füssli, Johann Heinrich Slevogt, Max Gogh van, Vincent Ensor, James Manet, Édouard Velázquez, Diego Vallotton, Félix Luks, George Benjamin Masson, André

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Table A1 (continued) Title

Artist

Three Ways of Being Triumph of Surrealism Vacances No.2 Visit at the Leasehold Farma Volga Boatman Wara

Lassnig, Maria Ernst, Max Lothe, André Brueghel d.Ä., Jan Repin, Ilja Jefimowitsch Rousseau, Henri

a b

Used only in Experiment 1. Used only in Experiment 2.

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