What Are Our Eyes First Drawn to in Art

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Peer-reviewed

Research Article

When Art Moves the Eyes: A Behavioral and Heart-Tracking Report

• Davide Massaro,
• Federica Savazzi,
• Cinzia Di Dio,
• David Freedberg,
• Vittorio Gallese,
• Gabriella Gilli,
• Antonella Marchetti

ten

• Published: May xviii, 2012
• https://doi.org/10.1371/journal.pone.0037285

Figures

Abstruse

The aim of this report was to investigate, using heart-tracking technique, the influence of lesser-upward and top-down processes on visual behavior while subjects, naïve to art criticism, were presented with representational paintings. Forty-ii subjects viewed color and black and white paintings (Color) categorized as dynamic or static (Dynamism) (bottom-up processes). Half of the images represented natural environments and half human subjects (Content); all stimuli were displayed under artful and movement judgment conditions (Job) (top-down processes). Results on gazing beliefs showed that content-related superlative-downwardly processes prevailed over low-level visually-driven bottom-up processes when a human discipline is represented in the painting. On the contrary, bottom-upwardly processes, mediated by low-level visual features, particularly affected gazing behavior when looking at nature-content images. Nosotros discuss our results proposing a reconsideration of the definition of content-related top-down processes in accordance with the concept of embodied simulation in art perception.

Citation: Massaro D, Savazzi F, Di Dio C, Freedberg D, Gallese V, Gilli G, et al. (2012) When Art Moves the Eyes: A Behavioral and Heart-Tracking Written report. PLoS ONE 7(5): e37285. https://doi.org/x.1371/journal.pone.0037285

Editor: Manos Tsakiris, Royal Holloway, University of London, Great britain

Received: Nov ten, 2011; Accepted: April 17, 2012; Published: May 18, 2012

Copyright: © 2012 Massaro et al. This is an open-access article distributed under the terms of the Creative Eatables Attribution License, which permits unrestricted apply, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the European union grant TESIS-264828 to VG. This research was also made possible by a D1-2008 research grant from the Università Cattolica del Sacro Cuore to GG. The funders had no role in report pattern, data collection and analysis, decision to publish, or grooming of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The debate on the definition of processes that contribute to the surfacing of an artful feel is very controversial, partly because of the unlike weights assigned to the elements in the contest betwixt "bottom-up" and "elevation-downwardly" processes. Unlike theoretical frames emphasize 1 or the other process in the building up of an aesthetic experience. Notwithstanding it is most likely that, in looking at an artwork, an observer enters into a dialogue in which aesthetic feel emerges from the interaction betwixt the 2 processes that operate at dissimilar levels of the viewer'southward experience [ane]–[3]. In particular, tiptop-down processes, classically recognized in factors such as content, cultural groundwork and teaching, may interact and therefore affect bottom-up processes, generated past sensory-driven coding of external stimuli.

Given that aesthetic experience begins with a visual scan of the artwork, the multi-level interaction between sensory-driven bottom-upwards and top-downward processes in aesthetic experience has been also studied exploring center movement behavior [4], [v]. Pioneering investigations into visual exploratory behavior of paintings [6], [vii] and subsequent studies on the informative details of an epitome [8], [9] revealed that observers focus their gaze on specific areas of the image, rather than in a random way. The areas receiving loftier densities of fixations were interpreted as cueing the observer's interest in informative elements of the paradigm [10]. In fact, attention studies revealed that eye movements are an index of overt selection and, as a upshot, they are the expression of the relation between what is observed and its relevance to the viewer's involvement [11]. In this respect, the analysis of the viewer'due south exploratory pattern and selection of salient visual aspects of the artwork tin assistance shed light on the respective contribution of bottom-up and pinnacle-down processes in the first stages of aesthetic experience in the beholder.

The study of bottom-up processes involved in aesthetic experience has mainly focused on the analysis of image composition, i.e. the relation among visual features of an artwork [12]. In this respect, aesthetic experience appears to exist influenced past factors such as contrast [13], balance [fourteen], [15], maximum effects with a minimum of ways [14] and symmetry [sixteen]–[18]. Computational bottom-up models of visual exploration, using eye-tracking technique, have further identified the depression-level properties responsible for drawing attention to specific areas of interest (salient regions of an paradigm) [nineteen]. Thus far, the identified contributors to visual saliency are contrast of luminance, curves, corners and occlusions every bit well as color, edges, lines and orientation [20].

There is evidence that low-level saliency measures, derived from a computational model (information theory), are also effective in capturing attention during aesthetic feel [21], [22]. For example, it has been shown that color may contribute to one's aesthetic feel [23] by enhancing the number of perceived elements within a limerick, ultimately increasing image complexity. In fact, there is testify that a moderate degree of complication increases the aesthetic appeal of visual stimuli [24], [25]. Another factor that may contribute to visual saliency within a painting is dynamism. According to Arnheim [26] the recognition of some dynamic qualities of the image is one of the well-nigh important elements of the aesthetic experience. The style in which motion in art is represented was explored past a study showing that one of the few graphic invariants in Western visual art is that representing move in garments. In these examples, motility perception is evoked past the adoption of specific features such equally orientation, curvature and convergence of lines, which represent robust graphic elements that have survived, in the Western civilization, across countries and centuries. The same effect can be gained also independently of contextual cues [27].

While the visual features that make up the structural composition of a representational artwork enhance the perceptual weight of the primal elements within it (bottom-up processes), the goal of the visual exploration (task) may determine their informativeness for the viewer (top-down processes). As indicated above, acme-down processes are influenced by a person's cultural background, education, degree of preparation in the arts, familiarity to and interest in a specific piece of work of art [16], every bit well as past inter-individual differences [28]. Heart-movement studies have too indicated motivation and job requirement as height-downward factors affecting aesthetic feel when viewing a painting [7], [29]. Platt and Glimcher [30] have shown that the reward macaque monkeys can expect from heart-move responses modulates the activity of neurons inside the oculomotor parietal area LIP. Rothkopf, Ballard, and Hayhoe [31] claimed that task requirements may be considered a good top-down predictor of gaze behavior. In fact they found that people involved in naturalistic virtual reality environments directed their gaze toward regions of the visual scene primarily on the basis of the chore requirements. The prove that eye motion patterns are affected past the cerebral job comes from studies in humans on high-level scene perception [ten] equally well as from visual aesthetic studies [5], [32]. Locher and colleagues [33], for example, showed that asking participants to appraise either complexity or pleasantness of abstract dot patterns affected their visual exploratory behavior. Zangemeister and colleagues [32] as well plant that exploration design of the same abstruse and realistic artworks changed as office of job requirements (no instruction, remember content features for a retrieve chore or concentrate on artistic aspects of the artworks). In some other instances, investigations found only a moderate contribution of task-related top-down processes on gaze beliefs during painting viewing. Wallraven et al. [21], for example, found that the scan paths of twenty participants, who looked at 275 artworks from different artistic styles under two dissimilar conditions (judging painting complexity, making artful judgments), did not substantially change as a function of task-type. In fact, both tasks favored a global search strategy, although the spatial distribution of fixations was broader in the artful judgment condition.

Additionally, the content of an artwork (for example a human portrait or the representation of a landscape) appears to influence human visual beliefs in a pinnacle-down fashion. Although the structural composition of a painting may bear on the perceptual weights of the most meaningful elements [5], [34], information technology has been also suggested that aesthetic feel associated with human content may operate in a specific manner different from the mere structural features that characterize visual patterns lacking human forms. In this respect, semantic factors are shown to play an important office in preference ascription. In fact, image content appears to atomic number 82 to greater divergence betwixt factors, such every bit similarity and preference ratings, in representational works, and specially in portraits, compared to artworks with poorer semantic values, such as abstract works [22]. One possible hypothesis of caption of the relevance of semantic factors is the embodied theory of perception, which introduces a new element of artful evaluation, namely, a multimodal notion of vision. Our visual perception of objects in the real world implies a lot more than the mere activation of our visual brain. Vision is ever a multimodal enterprise, encompassing the activation of sensori-motor, viscero-motor and bear on-related brain circuits. The discovery of mirror neurons [35], [36] and of a multifariousness of mirroring mechanisms in our brain (for review, see [37]) demonstrated that the same neural structures activated by the actual execution of actions or by the subjective feel of emotions and sensations are also active when we see others acting or expressing the aforementioned emotions and sensations. These mirroring mechanisms have been interpreted equally constituting a basic functional machinery in social cognition, defined equally embodied simulation [38], [39]. Embodied simulation is engaged as well when actions, emotions and sensations are displayed every bit static images, as in the case of art works [40]. Mirroring mechanisms and embodied simulation, as suggested past Freedberg and Gallese [40] might empirically ground the fundamental function of empathy in artful experience.

In the present study we used eye-tracking technique in the first stages of prototype scanning to investigate the contributions of lesser-upwards and top-down processes in the evaluation of aesthetic feel. The bottom-up processes under investigation were evoked by low-level features, namely colour and dynamism; top-down processes were represented by job blazon and content of paintings. Eye movement behavior was studied while participants, naïve to fine art criticism, observed representational paintings in two experimental conditions: aesthetic judgment and movement judgment.

Methods

Participants

40-two Italian undergraduate students naïve to art criticism (22 female, 20 male person, mean historic period = 22 South.D. = 3.95, range = nineteen–44) took role in this report. They gave their written informed consent to the experimental procedure. They did non present vision disorders that could interfere with the eye-tracking technique. Their participation was rewarded with a shopping voucher worth xx euros. The study was approved by the Local Ethic Commission (Università Cattolica del Sacro Cuore, Milan).

Visual Stimuli selection

One hundred stimuli were initially selected. They consisted of high-resolution digital versions of art paintings downloaded from different website collections. The stimuli were identified choosing artworks representing two main semantic categories: 50 human full-figure representations and l landscapes. Stimuli of these two groups were further categorized co-ordinate to the level of represented movement for a total number of 4 sub-categories: 25 dynamic man images, 25 static man images, 25 dynamic nature images, and 25 static nature images. Three independent judges performed the categorization. The doubtful cases were collegially resolved. A second set of stimuli was obtained by digitally converting the colored paintings into black and white images. The color modification was performed using a photo editing estimator program (Microsoft Role picture director) past means of the standard tool incorporated in the software parcel. The consequence of the decoloration of images is of smashing involvement in the research on epitome digital manipulation. Information technology is worth noting that several algorithms that aim to preserve the visual characteristics of colour images have been adult (meet for example, [41], [42]). Notwithstanding, at that place is not yet a strategy uniquely recognized as improve than the others. The limits potentially linked to the decoloration strategy used should be kept in mind in evaluating any differences about the Colour variable.

The attribute ratio of the paintings was preserved. Image sizes ranged from 448×880 to 519×797 pixels.

In order to select the 40 images considered being less familiar (not previously known), thirty-8 Italian volunteers (32 females, six males; age range = 20–61, mean historic period = 27.nineteen, SD = seven.49), naïve to art criticism, were randomly assigned to the color or the black and white painting presentation and asked to express two different judgments about perceived familiarity and level of movement of each painting. Results confirmed our prior categorization of images into static and dynamic. Then, on the basis of familiarity judgment only, nosotros selected the forty images that obtained the everyman familiarity evaluation, equally distributed among the iv sub-categories. Therefore, the post-obit groups of images – both in color and blackness and white versions – were used for this study: 10 dynamic homo images, 10 static human images, ten dynamic nature images, and 10 static nature images (for a total detailed description of this procedure of selection and for more information about the paintings see Material S1 and Tables S1, S2, S3, S4). The size of these selected images ranged from 495×812 to 788×524 pixels.

Procedure and Tasks

The stimuli were presented in two experimental tasks: artful judgment (AJ) and movement judgment (MJ). The order of these two tasks was counterbalanced across participants. Eye-movements were recorded using an eye-tracking technique during both tasks.

Lxxx stimuli (twoscore in the original color version and xl in the modified blackness and white version) were presented on a calculator screen in a randomized order. The presentation of the heart-tracking stimuli was created using the Tobii Studio 1.3 software (Tobii Technology AB). Participants were seated at a desk-bound in a placidity room, at a altitude of approximately 70 cm from the monitor. They were told that they would be shown a series of paintings on the calculator monitor while their heart-position was recorded. Each trial began with the presentation of a central blackness cantankerous on a white background for 1 2d, followed past the presentation of the stimulus that lasted 3 seconds. Then, a task-related question about the aesthetic appreciation of the painting (AJ) or the movement perception (MJ) appeared. Participants were instructed to respond to the question on a 7-point Likert scale by using the PC mouse. The question was presented both at the beginning of each task and each fourth dimension the respond was to be given. When the reply was given, the new trial started.

Each eye-tracker registration session lasted approximately ten minutes. An initial calibration pattern was displayed to participants before running both the centre-tracker sessions (AJ and MJ tasks).

Eye-Tracking data acquisition and model assay

Eye position was recorded using a Tobii Centre-Tracker X120 set on the desk in front end of the subject, betwixt the discipline and the monitor. The X120 Eye-Tracker is a stand-lonely eye tracking unit of measurement that uses an infra-red based system for capturing reflections of the pupil and cornea in club to sample centre-position every one/120 of a second. The organisation is accurate to less than 0.5 degrees.

Data were processed by the software through progressive aggregation levels in order to obtain a pattern of clusters, namely portions of the prototype with a high concentration of gaze information points. Clusters were automatically created by the software on the basis of the distance threshold that was set to fifty pixels as minimum distance between 2 different clusters (see figure 1). Tobii software uses the robust clustering algorithm suggested by Santella and DeCarlo [43] for eye motility information analysis. The cluster number represents the temporal guild in which clusters were generated by the aggregation of fixations from each trial. Data were normalized with respect to the total area of images and of the size of clusters. The eye-movement indicators candy past the software (Tobii Studio 1.iii) were fixations and observations. Fixations occur when a target feature of interest is positioned on the fovea for a variable period of time (averaging about 300 ms per fixation); observations occur each fourth dimension a specific cluster is entered and exited. The information on these two eye-movements indicators were collected both in terms of number and duration. For the analysis of between-furnishings univariate GLM was used. As for the analysis of within- and between-furnishings fixed-effects ANOVA model was used. This model was called because robust and, therefore, able to provide very reliable results even with small-scale sample sizes. Information technology was preferred to a random-effects model (which would have immune a stronger generalization of the results) since the latter, given the nature of our sample, would have increased the risk of biases in the computation of the model [44]–[46]. For the multiple comparisons the Sidak correction was practical.

Global pattern analysis.

Analyses of heart-tracking data were firstly carried out inside the total number of clusters formed in the paintings, respective to the sum of all amassed areas. For this purpose ii indexes were created: i) the total number of fixations per image, obtained by summing the number of fixations recorded for each cluster; 2) the mean duration of a fixation, obtained by dividing the full duration of fixations by the total number of fixations.

Cluster analysis.

Gazing beliefs inside each cluster was analyzed. Since the minimum number of clusters built across all images was 4 (range 4–20), only the commencement 4 clusters (Regions of Interest, ROI) formed in temporal society of exploration were considered for the cluster analysis. The variables measured in this analysis are described in Table ane.

Latent Class Analysis.

Latent class assay (LCA) models containing ane through four classes were fitted to the data using the iii.0 version of the Latent GOLD software [47]. LCA aims to ascertain groups of subjects on the ground of the probability that each subject belongs to a specific group, investigating associations among a set of variables. This statistical method is item useful and powerful because it does not rely on the traditional modeling assumptions and therefore it is less discipline to biases associated with non-parametric data. The rationale for LCA is that the observed distance between subjects with respect to a specific fix of variables is reduced by the identification of due north classes, which maximize the internal homogeneity as well as the inter-class heterogeneity. Furthermore, dissimilar other techniques (for example K-means clustering), LCA provides various diagnostic tools in social club to decide the optimal number of clusters. One of these is the Bayesan Information Criterion (BIC), based on the maximum likelihood function that allows selecting the all-time model amidst a finite prepare of models [48].

Experimental aims

The present study aimed at answering the following research questions:

1. How do dynamism and color bear upon paradigm exploration pattern (dynamic vs. static; colour vs. black and white)?
2. Is there a specific exploration pattern associated with image content (human vs. nature)?
3. How practise sensory-driven lesser-up and content-related acme-down processes interact affecting the exploration design?
4. Is there a difference in exploration blueprint between the types of task (aesthetic judgment vs. motility judgment) and is it correlated with the blazon of judgment expressed?

Results

Behavioral assay

A 2×ii×2 General Linear Model (GLM) analysis on the behavioral ratings with two levels of stimulus Content (human [H] vs. nature [N]), 2 levels of stimulus Dynamism (dynamic [D] vs. static [Southward]) and 2 levels of stimulus Colour (color [C] vs. black & white [BW]) was carried out within the tasks of aesthetic judgment (AJ) and motility judgment (MJ) separately (see Table two and Table iii for hateful values and model statistical notations).

As far equally AJ task is concerned, results revealed a master outcome of Dynamism (D>S) and a main outcome of Colour (C>BW). A pregnant interaction between Dynamism and Color was also plant indicating that the meaning deviation between dynamic and static prototype ratings persisted just in the color status (DC>SC). Additionally a 3 levels interaction was observed between Content, Color and Dynamism. More specifically, in the color status (Figure 2a), human and nature images received a higher AJ in the dynamic condition than in the static condition (HDC>HSC; NDC>NSC). In the black and white condition (Figure 2b) only human dynamic images were preferred over human static images (HDBW>HSBW). What seems to emerge is a higher aesthetic appreciation for dynamic images than static ones. This appreciation seems to be influenced past the content of the motion picture. In the example of paintings representing nature, it remains high only in the presence of color that is a low-level characteristic; in the case of human figures, aesthetic appreciation may depend more on factors related to the content, namely high-level characteristics.

With reference to MJ chore, results showed a main event of Content (H<N), a main effect of Dynamism (D>S) -confirming our prior stimulus selection– likewise equally an interaction between these ii factors (Figure 3). Post-hoc analyses revealed that the magnitude of the difference betwixt human being and nature in static images (ΔM = .877; HS<NS) was greater than the magnitude of the departure between human and nature in dynamic images (ΔM = .388; HD<ND), although both of them were significant. The images representing nature are, on average, perceived as more dynamic than those representing man beings (Figure 4). This effect could be explained with reference to a specific allure exerted by the content of the paintings. Bodily driven mechanisms would mainly affect the exploration of human images, supporting a more precise and modulated perception of motion, whereas nature images would be by and large influenced by visual characteristics of the paintings.

Eye-tracking global design analysis

Number of clusters.

A univariate GLM analysis was conducted on the number of eye-fixation clusters equally dependent variable with Content (human being [H] vs. nature [N]), Dynamism (dynamic [D] vs. static [S]), Color (color [C] vs. blackness and white [BW]) and Judgment Job (aesthetic judgment [AJ] vs. movement judgment [MJ]) as independent variables (see Table 4 and Table v for hateful values and model statistical notations). The movement and artful ratings were not introduced in the present and subsequent models of eye tracking data considering such ratings do non correlate with data of the exploration pattern, every bit shown and discussed beneath.

Results revealed a primary effect of Content (H<N) and a main upshot of Dynamism (D<S). More specifically, the number of clusters was smaller in human than in nature images and in dynamic than in static images.

An interaction between these 2 factors (Content and Dynamism) was also constitute (Figure 5). Post-hoc analyses revealed that dynamic images presented significantly fewer clusters than static images only in nature-content stimuli (DN<SN), whereas no significant differences in the number of clusters were constitute betwixt dynamic and static images in the homo-content stimuli. Furthermore, the effect of Content persisted only in the static status (HS<NS). In fact, results did not bear witness any significant difference in the number of clusters betwixt human being and nature condition in the dynamic images. No interaction furnishings were observed betwixt any of the variables and Judgment Chore-type. These data advise a consistent influence of content-related processes on the overall exploratory pattern in terms of number of clusters. Images depicting a man content seem to hold divers elements of attraction (attractors) compared with nature images, in which attention appeared to be directed towards a greater and more than variable number of potential attractors. The number of attractors in human-content paintings did not alter every bit a function of dynamism; in these stimuli, in fact, attractors seem to be common in dynamic and static images, possibly sharing similar relevant features.

Full number of fixations and fixation mean elapsing.

A 2×2×two×2 GLM was carried out on total number of fixations and mean duration of a fixation with 2 levels of stimulus Content (human [H] vs. nature [N]), 2 levels of stimulus Dynamism (dynamic [D] vs. static [S]), ii levels of stimulus Color (color [C] vs. black and white [BW]) and 2 levels of Judgment Chore (aesthetic judgment [AJ] vs. move judgment [MJ]) (meet Table six, Table vii and Tabular array 8 for mean values and model statistical notations).

Results relative to the total number of heart-fixations revealed a master effect of Content (H<N) and a main upshot of Dynamism (S<D). Nosotros institute a lower number of fixations in the human being-content besides every bit in static images than in nature and dynamic stimuli.

Additionally, a significant interaction between Content and Dynamism was found. In human-content stimuli, static images counted a total number of fixations significantly lower than dynamic images (HS<HD; Figure 6a). Likewise, in nature-content stimuli, static images counted a total number of fixations significantly lower than dynamic images, which remained always higher than the respective values in the homo-content condition (NS<ND). A meaning interaction between Dynamism and Colour was further found. The difference in the number of fixations betwixt color and blackness and white images was observed only for dynamic stimuli, disappearing for static images (CD>BWD; Figure 6b).

Finally a pregnant interaction between Judgment Task and Color was found. During AJ task the number of fixations was significantly higher for the color images than for the black and white images (CAJ>BWAJ), whereas no divergence was plant in the number of fixations betwixt colour and black and white images during MJ task.

Considering the mean elapsing of a single-middle-fixation per image, results were complementary to those described above on the total number of fixations.

These start results about fixations approve the idea that man content guides the viewer'due south attention on a more limited number of attractors than nature content; however, human attractors are fixed for longer than nature attractors. Moreover, results show that dynamism and color have an enriching part of perceived details, supporting the fulfillment of the job.

Eye-tracking cluster assay

As specified in Methods session, analyses were carried out considering simply the get-go 4 clusters (ROIs) formed in temporal order of exploration, which corresponded to the minimum number of clusters nowadays in all images.

ROI analysis was carried out on the four get-go clusters using ii×2×ii×two GLM models with 2 levels of stimulus Content (human [H] vs. nature [N]), 2 levels of stimulus Dynamism (dynamic [D] vs. static [S]), 2 levels of stimulus Color (color [C] vs. black and white [BW]) and 2 levels of Judgment Task (aesthetic judgment [AJ] vs. move judgment [MJ]).

Cluster size.

Table 9 shows clusters size equally a function of the percentage of surface area covered with respect to the full area of the paradigm.

Results showed a principal effect of Content (F _{(four 141)} = 14.773; p<.001, η² = .30, δ = 1.00; H<North): ROIs extension was significantly smaller in human-content than in nature-content images, supporting the over mentioned idea that paintings representing human being figures present highly meaningful and specific attractors.

Fixations and observations.

ROI analysis was carried out within each of the 4 start ROIs because the post-obit indexes: time to starting time fixation, fixation number and duration, observation number and duration (Tabular array 10).

As far as the time-to-first-fixation is concerned, in ROI one results showed a master effect of Content: the fourth dimension necessary to enter into the first cluster was longer in human-content than in nature-content stimuli (H>N).

With regards to fixations and observations indexes in ROI one, ii and 3, results showed a primary effect of Content: in all the iii ROIs the fixations number and duration as well equally the observations number were always college in man-content than in nature-content images (H>N). Additionally, a main consequence of Dynamism was too found for the first iii ROIs. Still, while in ROI 1 fixations and observations number and elapsing were higher in static images than in dynamic images (D<Southward), these effects reversed in ROI ii and 3 (D>S). A like tendency was observed for the factor Color in ROI 1 and 3 only with respect to fixation and ascertainment durations. In fact, in ROI one we plant a longer duration of fixations and observations in black and white images than in color images (C<BW); this result reversed in ROI 3 (C>BW). A college number of fixations in black and white images than in color images was besides found in ROI 1.

Finally, results revealed that in the considered clusters, Judgment Task affected observation number simply not fixation indexes. Specifically, results showed a chief outcome in the observations number in ROIs 1 and 3 (AJ>MJ).

These primary furnishings confirm the bonny power of human-content images and highlight their informative force, with a specific focus on the start iii clusters. Furthermore, data show that, in the lack of the enriching effect of dynamism and color, attending focuses on ROI 1, probably considering of its semantic value. Moreover, these meaningful portions of the image need to exist re-explored for the ascription of an aesthetic evaluation.

Interaction analyses for each considered ROI and relative statistic values are summarized in Table 11. Amid others, they bear witness a significant interaction between Content and Dynamism. In ROI 1 the number and duration of fixations was higher in human static images than in homo dynamic images (HS>HD), while in ROIs 2 and 3 these indexes were higher in human dynamic images than in homo static images (HS<Hd). A significant interaction was also found between Content and Color. Specifically, results revealed that, in ROI 1, black and white images received a higher number of fixations than colour paintings merely in nature-content (NC<NBW) and not in human being-content images. Conversely, in ROI two, the number of fixations and the duration of fixations were higher in color images than in black and white images only in human being-content (HC>HBW) and non in nature-content stimuli. These results substantially confirmed the evidences emerged from principal furnishings, stressing further, on one side, the peculiarity of how images representing homo figures drive the exploration pattern on specific portions of the epitome, on the other side, the role of colour and dynamism every bit possible enhancer of paintings details.

Content Assay and Latent Form Assay (LCA)

Focusing only on man-content paintings, an analysis was carried out on the content of each ROI which was defined on the basis of a qualitative description of the portion of the body bounded past the ROI considered (face, limbs, trunk or mixed content – face+limbs or face up+torso –, not on homo body). Results showed that the face surface area was the starting time clustered surface area (ROI 1) in the 61,3% of the cases; this value rose to 92.6% if also considering the content of ROI 2. Additionally, results revealed that the content mostly portrayed in the remaining 3 ROIs represented the limbs, on boilerplate, in 46% of the cases. See Table 12 for the percentage of fixations landed on these specific body parts.

We carried out a latent grade analysis (run into Methods for details) based on the variables Dynamism (static vs. dynamic) and Judgment Task (aesthetic vs. movement) to identify the presence of content-driven exploration patterns because the first four ROIs on human being-content paintings. In other words, we intended to verify the presence of unlike explorative approaches focusing attending on the specific contents of the homo body portrayed in the offset iv ROIs. In particular, LCA was fitted to the first four ROIs contents, which could vary betwixt face, limbs, torso and mixed contents (face up+limbs or face+body).

In the first LCA the independent variable Dynamism (dynamic vs. static) was used as active covariate. Active covariates are predictors of the probability to belong to the latent classes. Considering the unexplained corporeality of the association amid the variables (50²) and the explanative parsimony as choice criteria of the model, the best model was given by the 2-grade model (L² = 213.539 p<.01, Npar = 34, BIC = 850,96). The R^two values indicated that only the variance of the outset 2 indicators (prototype clusters) was significantly explained by this 2-class model. In item the model explained 22% and 31% of the variance respectively of the first and the 2nd ROI. The covariate Dynamism significantly predicted the 2-form distinction. In fact, 73% of static images showed the predominance of face equally content of the ROI 1, with a provisional probability (CP) equal to .71. This was followed past limbs as content of the ROI two (CP = .75). Lxxx percent of dynamic images showed an homogeneous distribution of choice among limbs (CP = .28), torso (CP = .31) and mixed content (CP = .29) for the ROI 1, and a predominant choice of mixed content (CP = .61) for the ROI 2. A LCA with the independent variable Judgment Task (aesthetic vs. motility judgment) as active covariate did not show whatsoever significant effect of this predictor.

LCA results show that – specifically for the exploration of human contents – in static images the semantic value of ROI ane is consistently conveyed by face, whereas, in dynamic paintings, it is more equally represented past unlike portions of the torso.

Correlation Analysis

Correlations were carried out between artful or movement behavioral ratings and middle-tracking variables. Significant correlations were constitute only with respect to clusters covering the confront area in man images. In particular, correlations were observed between motility rating and number and duration of fixations (r = .309, p<.05; r = .324, p<.05, respectively) and between movement rating and duration of observation (r = .415, p<.01). The higher these indexes, the greater the motility evaluation.

Discussion

The principal aim of this written report was to investigate the relationship between bottom-up and top-downwardly processes while looking at representational paintings. Within this theoretical frame we specified variables pertaining to one or the other procedure. More than specifically, we investigated exploration patterns during the observation of artworks presented in a color and in a black and white version (Colour) and categorized as dynamic or static (Dynamism) (bottom-up processes). Images of paintings represented natural environments or homo subjects (Content); they were displayed under artful and motility judgment conditions (Task) (top-downwardly processes). Our data are discussed against the classical approach to bottom-up and top-down processes and also advise alternative interpretations in the lite of the results obtained. For simplicity, the furnishings of lesser-upwardly processes (sensory-driven) on eye gazing behavior in relation to the top-downwardly variables (content and task-type) are discussed in split sections.

Behavioral data

Behavioral results obtained in aesthetic judgment status revealed that dynamic images were preferred to static images; likewise, color images were preferred to blackness and white images. However, interaction analyses showed that, when rating nature-content paintings only, aesthetic evaluation of dynamic images dropped appreciably in the absenteeism of information well-nigh color. These results suggest that color might potentiate the aesthetic effect of dynamic images by perhaps enriching the picture with perceptual details (increased image complexity). This idea is in line with Zellner et al. [25], who suggested that colour –as a low-level saliency chemical element– could increase the complexity of visual stimuli by enhancing the number of perceived elements, ultimately contributing to artful experience [21]. This effect was not observed for man-content stimuli. In fact, preference for dynamic human images was not affected by information conveyed by color, suggesting that aesthetic evaluation of images depicting man subjects may be guided by content-related factors, which cannot be fully explained by low-level visual perceptual data only, equally in the instance of nature-content stimuli.

Additionally, the assay of rating in move judgment condition showed that nature images were on average recognized as more dynamic than human images. According to a classical perspective on motion perception, this consequence could exist explained in terms of a more meaning presence of depression-level features in nature images that in human being images. This visual information would elicit bottom-up processing of motility perception, highly affecting the formulation of a judgment [1]–[iii]. Nevertheless, this difference tin can be too explained in terms of content-related attractiveness to dissimilar aspects of the images. In fact, in nature-content paintings the dynamic character of the images was most likely affected by attending to depression-level visually-driven lesser-upwardly processes (e.thousand., colour enhancing visual complexity); whereas, in human-content paintings, move rating may have been affected by attraction to elements most possibly identified past bodily-driven simulation processes, that is, by the variety of sensory-motor resonance mechanisms induced by the observation of homo bodies [twoscore]. This mechanism would modulate the perception of movement in human images making information technology more detailed than that of nature images. This greater modulation would affect the rating variance, determining a lower average scoring for human movement than nature i.

This interpretation based on the concept of embodied simulation [38], [39] appears to be corroborated by data obtained from heart-tracking, as described in detail in the section to follow.

Centre tracking information

Effect of bottom-up and content-related top-down processes.

Eye-tracking results showed that static human-content images, on average, guided visual exploration on fewer precise areas than static nature images. The allure exerted by man-content images was independent of dynamism, while nature-content stimuli attracted attention to few specific areas only in the case of dynamic images.

The lack of influence of dynamism while observing human-content paintings likely betrays the fact that a human torso might imply an intrinsic and natural dynamism, evoking motor resonance in its beholder and causing, as earlier suggested, a more accurate perception of human than nature motion. This observation supports the hypothesis put forward in Graham et al. [22] where it is suggested that artful experience, associated with human being content, may rely on specific qualities of the artwork that are different from the structural features characterizing visual patterns lacking human forms. This effect also supports the thought that, in the absenteeism of a human being figure, low-level visual features predominantly bear on the visual scan path.

In fact, in static nature images, the greater number of clusters observed suggests that participants continued to explore the images in search of attractors. These latter, on the other hand, were more readily institute in dynamic nature images, because of the specific depression-level features employed in arts to represent motion [26].

What emerges from our data is that color and dynamism, at least for the paintings considered, appear to play an enriching function inside lesser-up processes; whereas, within summit-downward bodily-driven processes, homo content may prove a stronger power than purely natural content. This interpretative frame finds further support if nosotros consider the number of total fixations beyond paintings too every bit cluster size. Dynamic and color images revealed a greater amount of perceived details than static and black and white paintings, equally shown by a higher number of fixations. The smaller mean cluster size observed for man than nature paintings, on the other hand, indicates that attractors in man images captured attending on specific narrower areas than nature images and suggests, once more, that human images contained presumably more than meaningful and informative bodily content elements than nature images. In fact, analysis of the get-go three clusters revealed more and longer fixations, every bit well as greater returns to these areas, in man than in nature paintings. They also confirmed that, in the lack of information about dynamism and color (static and black and white images), observer's attention was focused on the most salient part of the painting, namely cluster 1.

According to our hypothesis of embodied simulation, the human frame seems to automatically orient participants toward predetermined attractors, namely the presence of a human figure in the picture drives the search for parts of the body. This trend may bear upon the fourth dimension necessary to spatially identify the expected element. In fact, results revealed that the time used to make the first fixation into the beginning cluster was, on average, longer in human images, where the expected content is defined and framed, than in nature images, where the potential attractors may vary into a wider range of undefined elements. In other terms, in a moving-picture show depicting natural environments, any element may represent a potential attractor that requires inspection.

The interpretative framework arising from our results, thus far, gives a specific role to the human content – not found for the nature i – in the fashion it affects the aesthetic perception of paintings. This framework is further corroborated and extended past findings from Latent Grade Analysis. Focusing only on man content images, it shows that in static images a strong attractor was face, while in dynamic images attention was equally spread out beyond unlike body parts. In the first example, the exploration pattern would be guided past the embodied simulation of sensations and emotions; in the second example it would be greatly influenced past the simulation of deportment (see effigy vii).

Figure 7. Heat map visualization of the gaze beliefs for man color images.

On the left is a dynamic image, on the right is a static image. The cherry gradient indicates portions of the image observed by the totality of the sample.

https://doi.org/x.1371/journal.pone.0037285.g007

As for the face content, several studies showed that information technology is generally the beginning part of the torso that is scanned in portraits [21] activating a configural visual encoding, instead of the more common analysis of private features [22], [49], [50]. The importance of face was as well shown in a study where eye-movements were recorded during the viewing of geometrical patterns that, in some instances, presented embedded faces. Results showed a variation in oculomotor behavior associated with the presence of face up [51]. Attraction to confront is particularly relevant because it represents an extremely of import cue about a person's identity, health state, emotional state, mental attitude and gender, which are factors playing a crucial role when socially interacting with conspecifics [52]–[54]. It is interesting to observe that allure to face, as highlighted by our findings, goes beyond the existent social frame, it being triggered also when viewing humans represented in artworks.

Equally for the rest of the body, several studies suggested that also the human body might be a salient and powerful stimulus [55], [56]. For example, Calvo-Merino et al. [57] institute that the perception of human bodies in trip the light fantastic postures, merely not the vision of objects, activates specific motor areas. Body-sensitive areas contributed to aesthetic experience of trip the light fantastic perception every bit far as early on belittling visual processing of body stimuli has a pregnant role in afterwards aesthetic responses.

Effect of lesser-upwards and chore-related tiptop-downwards processes.

Results relating to the number of clusters formed inside the images showed that judgment tasks did not significantly affect the participants' behavior. Similarly, LCA showed that the attended areas (with respect to human being-content only) were the aforementioned independently of whether the participants were assessing the aesthetics or the movement-expression of the paintings. Task-related top-downward processes did not seem to have exerted a pregnant effect on overall exploration pattern.

Still, results about more than analytic heart-tracking indexes, indicated that the get-go clusters of the paradigm, which were among the nigh salient in terms of represented content (come across above), needed to be re-explored for the ascription of an aesthetic evaluation. In other terms, the identification of cues revealing motion were more readily recognized and processed during movement task than during aesthetic task, in which the identification of elements useful for an aesthetic assessment involved more explicit and evaluative processes.

In this respect, it should be added that nosotros accept an important component of aesthetic experience to be the response to perceptual objects consisting of the embodied simulation of emotions, sensations and actions, that the content of the object evokes in the beholder. Such feel is not necessarily confined to the appreciation of artworks, although this is grounded on it. In contrast, we excogitate of aesthetic judgment as the explicit artful rating of an object co-ordinate to culturally and socially adamant aesthetic canons. Aesthetic judgment represents the most cognitive aspect of the relation established with works of art and it answers to the question: "Is it beautiful?" [58], [59].

Deepening the interaction between lesser-up and job-related pinnacle-down processes, nosotros constitute that color images were more explored than blackness and white images in aesthetic judgment task only. The capability of color to enrich the image of details, equally already stressed in our give-and-take in a higher place, probably influenced participants' need for more fixations to evaluate the images aesthetically. Additionally, exploration was on average longer for dynamic images during movement judgment than during aesthetic judgment job, indicating, not surprisingly, that dynamic images were more significant in terms of task fulfillment. Correlation analyses betwixt the various heart-tracking measures and the participants' behavioral ratings hardly produced whatever association. In other words, centre-gazing patterns were non predictive of either aesthetic or movement assessment of the observed stimuli. This lack of correlation is coherent with the results by Heidenreich and Turano [lx] that did non show any significant link betwixt participants' aesthetic judgments of the paintings and fixation durations or viewing time. On the whole, these data suggest that task-related top-down processes affected some specific components of the exploration pattern and that attraction exerted past sensory-driven bottom-up processes was functional to the fulfillment of the job.

Overall, our findings are subject area to some limitations. Despite a considerable number of stimuli was used, it covers a express portion of the artistic production available. Although the content categories analyzed (human and nature) are highly representative of what is commonly painted, they do not encompass all categories of creative content (for example, still life, human artifacts, etc.). Furthermore, some differences, although statistically significant, evidence a moderate magnitude and the type of analysis selected co-ordinate to the characteristics of our sample, although robust, did non control for random furnishings.

Concluding remarks

The relationship between peak-downwards and bottom-up processes seems to stem from the salience of the content represented in the painting. We found that when represented content includes human subjects, content-related elevation-down processes prevail over low-level visually-driven bottom-up processes in guiding the observers' explorative pattern. On the other manus, when nature-content is represented, bottom-up processes, mediated by elements such as colour, complication and visual dynamism, announced to preferentially bear upon gazing behavior.

More specifically, when a human being is portrayed in a painting, gazing behavior is mostly focused on the human effigy, independently of contextual elements also depicted in the epitome. In particular, attention is given to the face up area, specially when ascribing an aesthetic judgment whereas dynamism ascription appears to be strongly guided by attending to features portraying actions. This testify allow usa hypothesize that semantic content of artworks representing man body might evoke processes in the beholder that cannot exist univocally explained with reference to classical socio-cultural factors (such as cultural background and education, come across for example [1]–[3]), only that they likewise encompass the expression of embodiment, or, more than specifically, of the feed-back signals fed by parieto-premotor sensory-motor circuits to oculo-motor and visual cortical areas.

In this respect, our results suggest an estimation of the already described manner of focusing attention in terms of embodied simulation: the face would arm-twist the simulation of emotions and sensations as well as the body would provoke the simulation of actions. This interpretation offers a new conceptualization of dynamism category that differs from the classical description of depression level visually-driven bottom-up processes, nonetheless recognized for nature-content paintings [21], [22]. More specifically, when a homo subject is present in an epitome, the recognition of dynamism shifts from a visual decoding of perceptual elements (bottom-up process) to an embodied processing of the image semantics defined past the represented deportment (actual content-driven peak-down process). In other terms, as suggested by Freedberg and Gallese [40], the hypothesis of embodied simulation would permit the identification of the emotions and the bodily engagement with the gestures, a pre-rational way to "make sense of the actions, emotions and sensations of others" (p. 198).

The question then arises of what determines dynamism perception in artworks representing nature. Is dynamism in paintings of natural scenes a sole effect of visual complexity, as our data suggest and, if then, in what terms is it coded? In terms of a possible physiological explanation, in which dynamism perception is associated with heart gazing variables, we hypothesized that, if perception of dynamism is a proprioceptive epiphenomenon elicited by eye-movements, at that place should be an clan between number of fixations and movement judgment. Behavioral information obtained from movement judgment status already indicated the lack of clan between physiological measures and dynamism judgment in nature-content images. Additionally, analysis of physiological data solitary showed that dynamic nature stimuli were characterized by a fewer number of clusters (narrow explorative behavior) than static stimuli and by equal number of fixations, suggesting that middle-movements did not affect the perception of dynamism in nature images. Peradventure, even when contemplating a waterfall, embodiment is relevant. As the German art historian Heinrich Wölfflin suggested [61] (p. 151) "…as human beings with a body that teaches us the nature of gravity, wrinkle, strength, and so on, nosotros gather the experience that enables u.s.a. to place with the conditions of other forms".

Supporting Information

Author Contributions

Conceived and designed the experiments: DM FS CDD DF VG GG AM. Performed the experiments: DM FS. Analyzed the data: DM FS CDD. Wrote the paper: DM FS CDD DF VG GG AM.

References

1. 1. Leder H, Belke B, Oeberst A, Augustin D (2004) A model of aesthetic appreciation and artful judgments. British Journal of Psychology 95: 489–508.
   • View Article
   • Google Scholar
2. two. Locher P, Overbeeke K, Wensveen S (2010) Artful interaction: A framework. Design Bug 26: 70–79.
   • View Article
   • Google Scholar
3. 3. Cupchik GC, Vartanian O, Crawley A, Mikulis DJ (2009) Viewing artworks: Contributions of cognitive control and perceptual facilitation to artful experience. Brain and cognition 70: 84–91.
   • View Article
   • Google Scholar
4. 4. Locher P (2006) The usefulness of heart motion recordings to subject an aesthetic episode with visual fine art to empirical scrutiny. Psychology Scientific discipline 48: 106.
   • View Commodity
   • Google Scholar
5. five. Locher PJ (1996) The contribution of middle-movement research to an agreement of the nature of pictorial residue perception: A review of the literature. Empirical Studies of the Arts.
   • View Article
   • Google Scholar
6. 6. Buswell GT (1935) How people look at pictures. Chicago: University of Chicago Press.
7. seven. Yarbus AL (1967) Centre movements and vision. New York: Plenum Printing.
8. 8. Antes JR (1974) The time course of picture viewing. Journal of Experimental Psychology 103: 62.
   • View Commodity
   • Google Scholar
9. nine. Mackworth NH, Morandi AJ (1967) The gaze selects informative details inside pictures. Attending, Perception, & Psychophysics ii: 547–552.
   • View Article
   • Google Scholar
10. 10. Henderson JM, Hollingworth A (1999) High-level scene perception. Annual Review of Psychology l: 243–271.
    • View Article
    • Google Scholar
11. 11. Rizzolatti Thou, Riggio L, Dascola I, Umiltá C (1987) Reorienting attention across the horizontal and vertical meridians: Evidence in favor of a premotor theory of attention. Neuropsychologia 25: 31–forty.
    • View Article
    • Google Scholar
12. 12. Hekkert P, Leder H (2008) Product aesthetics. In: Schifferstain H, Hekkert P, editors. Product feel. London: Elsevier Ltd. pp. 259–285.
13. 13. Ramachandran VS, Hirstein W (1999) The science of fine art: A neurological theory of artful feel. Journal of Consciousness Studies 6 6: 15–51.
    • View Article
    • Google Scholar
14. 14. Boselie F, Leeuwenberg E (1985) Birkhoff revisited: Beauty equally a function of effect and means. The American periodical of psychology 1–39.
    • View Article
    • Google Scholar
15. xv. Hekkert P, van Wieringen PCW (1996) The impact of level of expertise on the evaluation of original and altered versions of postal service-impressionistic paintings. Acta Psychologica 94: 117–131.
    • View Article
    • Google Scholar
16. 16. Berlyne DE (1971) Aesthetics and psychobiology. New York: Appleton-Century-Crofts.
17. 17. Jacobsen T, Hofel L (2002) Aesthetic judgments of novel graphic patterns: analyses of individual judgments. Perceptual and Motor Skills 95: 755–766.
    • View Article
    • Google Scholar
18. 18. Livingstone Grand, Hubel DH (2002) Vision and art: The biology of seeing. New York, NY: Harry N. Abrams.
19. nineteen. Graham DJ, Redies C (2010) Statistical regularities in art: Relations with visual coding and perception. Vision inquiry 50: 1503–1509.
    • View Commodity
    • Google Scholar
20. 20. Le Meur O, Chevet JC (2010) Relevance of a feed-forrad model of visual attending for goal-oriented and free-viewing tasks. IEEE Transactions on Prototype Processing 19: 2801–2813.
    • View Article
    • Google Scholar
21. 21. Wallraven C, Cunningham D, Rigau J, Feixas M, Sbert M (2009) Aesthetic appraisal of fine art-from middle movements to computers. Computational Aesthetics 137–144.
    • View Commodity
    • Google Scholar
22. 22. Graham DJ, Friedenberg JD, McCandless CH, Rockmore DN (2010) Preference for art: similarity, statistics, and selling cost. Guild of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 7527: 36.
    • View Article
    • Google Scholar
23. 23. Martindale C, Moore Thou (1988) Priming, prototypicality, and preference. Periodical of Experimental Psychology: Human Perception and Operation 14: 661.
    • View Article
    • Google Scholar
24. 24. Berlyne DE (1970) Novelty, complication, and hedonic value. Attending, Perception, & Psychophysics 8: 279–286.
    • View Article
    • Google Scholar
25. 25. Zellner DA, Lankford Grand, Ambrose L, Locher P (2010) Fine art on the plate: Consequence of balance and color on attractiveness of, willingness to try and liking for food. Food Quality and Preference 21: 575–578.
    • View Article
    • Google Scholar
26. 26. Arnheim R (1992) To the rescue of fine art: Twenty-6 essays. Berkeley: University of California Press.
27. 27. Gori S, Pedersini R, Giora Due east (2008) How do painters represent movement in garments? Graphic invariants across centuries. Spatial Vision 21: 201–227.
    • View Commodity
    • Google Scholar
28. 28. Jacobsen T (2006) Bridging the arts and sciences: A framework for the psychology of aesthetics. Leonardo 39: 155–162.
    • View Article
    • Google Scholar
29. 29. Locher P (1995) A measure of the information content of visual art stimuli for studies in experimental aesthetics. Empirical Studies of the Arts thirteen: 183–191.
    • View Article
    • Google Scholar
30. xxx. Platt ML, Glimcher Prisoner of war (1999) Neural correlates of determination variables in parietal cortex. Nature 400: 233–238.
    • View Article
    • Google Scholar
31. 31. Rothkopf CA, Ballard DH, Hayhoe MM (2007) Task and context make up one's mind where y'all await. Journal of Vision 7: 16.1–1620.
    • View Article
    • Google Scholar
32. 32. Zangemeister WH, Sherman K, Stark L (1995) Prove for a global scanpath strategy in viewing abstruse compared with realistic images. Neuropsychologia 33: 1009–1025.
    • View Article
    • Google Scholar
33. 33. Locher P, Cavegn D, Groner Thousand, Muller P, d'Ydewalle G, et al. (1993) The effects of stimulus symmetry and job requirements on scanning patterns. In: d'Ydewalle 1000, Van Rensbergen J, editors. Perception and noesis. Amsterdam: Elsevier Science. pp. 59–69.
34. 34. Nodine CF (1982) Compositional blueprint equally a perceptual determinant of artful judgment. Review of Inquiry in Visual Arts Teaching 43–54.
    • View Commodity
    • Google Scholar
35. 35. Gallese V, Fadiga L, Fogassi Fifty, Rizzolatti Thousand (1996) Action recognition in the premotor cortex. Brain 119: 593.
    • View Article
    • Google Scholar
36. 36. Rizzolatti G, Fadiga L, Gallese Five, Fogassi L (1996) Premotor cortex and the recognition of motor actions. Cognitive encephalon research 3: 131–141.
    • View Commodity
    • Google Scholar
37. 37. Gallese V, Sinigaglia C (2011) What is so special well-nigh embodied simulation? Trends in Cognitive Sciences xv: 512–519.
    • View Article
    • Google Scholar
38. 38. Gallese V (2003) The manifold nature of interpersonal relations: the quest for a mutual mechanism. Philosophical Transactions of the Purple Society B 358: 517–528.
    • View Article
    • Google Scholar
39. 39. Gallese V (2005) Embodied simulation: From neurons to astounding experience. Phenomenology and the Cognitive Sciences 4: 23–48.
    • View Commodity
    • Google Scholar
40. 40. Freedberg D, Gallese Five (2007) Motion, emotion and empathy in esthetic experience. Trends in Cognitive Sciences xi: 197–203.
    • View Article
    • Google Scholar
41. 41. Grundland M, Dodgson NA (2007) Decolorize: Fast, dissimilarity enhancing, colour to grayscale conversion. Pattern Recognition xl: 2891–2896.
    • View Article
    • Google Scholar
42. 42. Smith One thousand, Pierre-Edouard Landes JT, Myszkowski K (2008) Apparent greyscale: A simple and fast conversion to percep- tually accurate images and video. Proc of EUROGRAPHICS 27: 193–200.
    • View Commodity
    • Google Scholar
43. 43. Santella A, DeCarlo D (2004) Robust Clustering of Eye Movement Recordings for Quantification of Visual Involvement. Eye tracking inquiry & applications (ETRA) 23: 27–34.
    • View Commodity
    • Google Scholar
44. 44. Bell BA, Morgan GB, Schoeneberger JA, Loudermilk BL, Kromrey JD, et al. (2010) Dancing the sample size limbo with mixed models: How depression tin you go? SAS Global Forum 2010 Paper 197–2010.
45. 45. Maas CJM, Hox JJ (2004) Robustness issues in multilevel regression analysis. Statistica Neerland 58: 127–137.
    • View Article
    • Google Scholar
46. 46. Maas CJM, Hox JJ (2005) Sufficient Sample Sizes for Multilevel Modeling. Methodology: European Periodical of Enquiry Methods for the Behavioral and Social Sciences ane: 86–92.
    • View Article
    • Google Scholar
47. 47. Vermunt JK, Magidson J (2000) Latent GOLD User's Manual. Boston: Statistical Innovations Inc. 185 p.
48. 48. Magidson J, Vermunt JK (2002) Latent class models for clustering: A comparison with Chiliad-means. Canadian Journal of Marketing Inquiry twenty: 36–43.
    • View Commodity
    • Google Scholar
49. 49. Abbas Z, Duchaine B (2008) The part of holistic processing in judgments of facial bewitchery. Perception 37: 1187–1196.
    • View Commodity
    • Google Scholar
50. 50. Young AW, Hellawell D, Hay DC (1987) Configurational information in face perception. Perception 16: 747–759.
    • View Article
    • Google Scholar
51. 51. Tatler BW, Wade NJ, Kaulard 1000 (2007) Examining art: Dissociating pattern and perceptual influences on oculomotor behaviour. Spatial Vision 21 1: 165–184.
    • View Article
    • Google Scholar
52. 52. Aharon I, Etcoff N, Ariely D, Chabris CF, O'Connor Eastward, et al. (2001) Beautiful Faces Have Variable Reward Value: fMRI and Behavioral Testify. Neuron 32: 537–551.
    • View Article
    • Google Scholar
53. 53. Adams RB, Kleck RE (2003) Perceived gaze direction and the processing of facial displays of emotion. Psychological Scientific discipline fourteen: 644–647.
    • View Article
    • Google Scholar
54. 54. O'Doherty J, Winston J, Critchley H, Perrett D, Burt DM, et al. (2003) Beauty in a smile: the office of medial orbitofrontal cortex in facial attractiveness. Neuropsychologia 41: 147–155.
    • View Article
    • Google Scholar
55. 55. Downing PE, Jiang Y, Shuman M, Kanwisher N (2001) A cortical area selective for visual processing of the human trunk. Scientific discipline 293: 2470–2473.
    • View Article
    • Google Scholar
56. 56. Calvo-Merino B, Glaser DE, Grezes J, Passingham RE, Haggard P (2005) Activity observation and acquired motor skills: An fMRI study with adept dancers. Cerebral cortex 15: 1243.
    • View Article
    • Google Scholar
57. 57. Calvo-Merino B, Urgesi C, Orgs G, Aglioti SM, Haggard P (2010) Extrastriate body area underlies artful evaluation of torso stimuli. Experimental encephalon research 204: 447–456.
    • View Article
    • Google Scholar
58. 58. Di Dio C, Gallese V (2009) Neuroaesthetics: a review. Current opinion in neurobiology 19: 682–687.
    • View Article
    • Google Scholar
59. 59. Gallese 5, Di Dio C (2012) "Neuroesthetics. The trunk in artful feel". In: Ramachandran V, editor. Encyclopedia of Human Behavior. 2nd Edition. San Diego (CA): Academic Press.
60. 60. Heidenreich SM, Turano KA (2011) Where Does One Look When Viewing Artwork in a Museum? Empirical Studies of the Arts 29: 51–72.
    • View Article
    • Google Scholar
61. 61. Wölfflin H (1886/1994) Prolegomena su einer Psychologie der Architektur. In: Mallgrave HF, Ikonomou East, editors. Empathy, Class and Space: Issues in German Aesthetics, 1873–1893. Santa Monica: Getty Heart Publications. pp. 149–162.
62. 62. Tobii (2008) Tobii Studio. User Manual. Darderyd, Sweden: Tobii Technology AB.

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