| Literature DB >> 25048813 |
Federica Savazzi1, Davide Massaro2, Cinzia Di Dio3, Vittorio Gallese4, Gabriella Gilli1, Antonella Marchetti2.
Abstract
Adolescence is a peculiar age mainly characterized by physical and psychological changes that may affect the perception of one's own and others' body. This perceptual peculiarity may influence the way in which bottom-up and top-down processes interact and, consequently, the perception and evaluation of art. This study is aimed at investigating, by means of the eye-tracking technique, the visual explorative behavior of adolescents while looking at paintings. Sixteen color paintings, categorized as dynamic and static, were presented to twenty adolescents; half of the images represented natural environments and half human individuals; all stimuli were displayed under aesthetic and movement judgment tasks. Participants' ratings revealed that, generally, nature images are explicitly evaluated as more appealing than human images. Eye movement data, on the other hand, showed that the human body exerts a strong power in orienting and attracting visual attention and that, in adolescence, it plays a fundamental role during aesthetic experience. In particular, adolescents seem to approach human-content images by giving priority to elements calling forth movement and action, supporting the embodiment theory of aesthetic perception.Entities:
Mesh:
Year: 2014 PMID: 25048813 PMCID: PMC4105571 DOI: 10.1371/journal.pone.0102888
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Aesthetic and Movement ratings for paintings representing nature and human figures.
Mean behavioral ratings (and standard error – SE) per sub-category for AJ and MJ.
| Top-Down | |||||||
| Mean (SE) | |||||||
| Judgment (1–7 Likert scale) | Movement (MJ) | Aesthetic (AJ) | |||||
| Nature | Human | Nature | Human | ||||
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| 3.01(.35) | 2.41(.28) |
| 4.99(.22) | 4.26(.20) |
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| Mean (SE) |
| 5.25(.26) | 4.21(.20) |
| 5.24(.24) | 4.32(.16) |
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Mean number of clusters (and standard error – SE) per sub-category.
| Top-Down | |||||||
| Mean (SE) | |||||||
| Movement (MJ) | Aesthetic (AJ) | ||||||
| Nature | Human | Nature | Human | ||||
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| 6.75(.87) | 5.25(.87) |
| 7.00(.87) | 5.50(.87) |
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| Mean (SE) |
| 6.75(.87) | 5.50(.87) |
| 7.75(.87) | 6.50(.87) |
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Mean number of fixations (and standard error – SE) on the total clustered area of images per sub-category.
| Top-Down | |||||||
| Mean (SE) | |||||||
| Movement (MJ) | Aesthetic (AJ) | ||||||
| Nature | Human | Nature | Human | ||||
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| 3.32(.29) | 3.53(.35) |
| 2.86(.25) | 2.96(.16) |
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| Mean (SE) |
| 2.69 (.19) | 3.07(.26) |
| 3.70(.50) | 3.26(.27) |
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Mean fixations duration (and standard error – SE) (in seconds) on the total clustered area of the images per sub-category.
| Top-Down | |||||||
| Mean (SE) | |||||||
| Movement (MJ) | Aesthetic (AJ) | ||||||
| Nature | Human | Nature | Human | ||||
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| .37(.03) | .44(.04) |
| .36(.03) | .46(.04) |
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| Mean (SE) |
| .35(.02) | .39(.03) |
| .32(.02) | .35(.02) |
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Clusters size (%) in image representing human vs. nature content.
| Human | Nature | |||||||
| Minimum | Maximum |
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| Minimum | Maximum |
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| .41 | 4.89 |
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| .21 | 4.47 |
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| .09 | 3.32 |
| . | .87 | 4.76 |
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| .34 | 3.99 |
| . | .22 | 3.92 |
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| .04 | 1.89 |
| . | .09 | 4.23 |
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GLM main effects for fixations and observations of the first 4 ROI's.
| Indexes | ROI | Content | Dynamism | Judgment Task | |||||||||||||||
| F | df |
| η2 | δ | F | df |
| η2 | δ | F | df |
| η2 | δ | |||||
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| 1 | - | - | MJ>AJ | 7.211 | 1,19 | .015 | .27 | .72 | ||||||||||
| 2 | H>N | 34.269 | 1,19 | .000 | .64 | 1.0 | S>D | 27.327 | 1,19 | .000 | .59 | .99 | MJ>AJ | 13.043 | 1,19 | .002 | .40 | .93 | |
| 3 | - | - | - | ||||||||||||||||
| 4 | - | - | - | ||||||||||||||||
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| 1 | - | - | MJAJ | 5.931 | 1,19 | .025 | .24 | .64 | ||||||||||
| 2 | H>N | 40.968 | 1,19 | .000 | .68 | 1.0 | S>D | 11.261 | 1,19 | .003 | .37 | .89 | MJ>AJ | 11.936 | 1,19 | .003 | .39 | .91 | |
| 3 | - | - | - | ||||||||||||||||
| 4 | - | - | - | ||||||||||||||||
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| 1 | H>N | 13.893 | 1,19 | .001 | .42 | .94 | - | MJ>AJ | 5.454 | 1,19 | .031 | .22 | .60 | |||||
| 2 | H>N | 91.000 | 1,19 | .000 | .83 | 1.0 | S>D | 63.246 | 1,19 | .000 | .77 | 1.0 | MJ>AJ | 15.813 | 1,19 | .001 | .45 | .96 | |
| 3 | H>N | 4.767 | 1,19 | .042 | .20 | .54 | - | - | |||||||||||
| 4 | - | - | - | ||||||||||||||||
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| 1 | H>N | 13.895 | 1,19 | .001 | .42 | .94 | - | - | ||||||||||
| 2 | H>N | 113.808 | 1,19 | .000 | .86 | 1.0 | S>D | 71.467 | 1,19 | .000 | .79 | 1.0 | MJ>AJ | 14.107 | 1,19 | .001 | .43 | .94 | |
| 3 | - | - | - | ||||||||||||||||
| 4 | - | - | - | ||||||||||||||||
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| 1 | - | S>D | 14.163 | 1,19 | .001 | .43 | .95 | - | ||||||||||
| 2 | H>N | 105.582 | 1,19 | .000 | .85 | 1.0 | S>D | 45.499 | 1,19 | .000 | .70 | 1.0 | MJ>AJ | 8.868 | 1,19 | .008 | .32 | .81 | |
| 3 | H>N | 6.085 | 1,19 | .023 | .24 | .65 | - | - | |||||||||||
| 4 | - | - | - | ||||||||||||||||
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| 1 | H>N | 10.821 | 1,19 | .004 | .39 | .87 | - | - | ||||||||||
| 2 | - | - | - | ||||||||||||||||
| 3 | - | - | - | ||||||||||||||||
| 4 | - | - | - | ||||||||||||||||
Figure 2Example of cluster distributions on a human static image (Old Woman Dozing, Nicolaes Maes, 1656).
Cluster number represents the temporal order in which clusters have been shaped considering the pattern of fixations from all participants. The percentage of participants looking in each cluster is reported.