How Exactly Did the Nose Get That Long? A Critical Rethinking of the Pinocchio Effect and How Shape Changes Relate to Landmarks

Klingenberg, Christian Peter

doi:10.1007/s11692-020-09520-y

Cited by 26 publications

(26 citation statements)

References 47 publications

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“…It should be noted that Procrustes superimposition changes perceived patterns of variation in landmark coordinates, often rather drastically (Rohlf & Slice, 1990; Walker, 2000). Such phenomena are probably to be seen as properties of shape variables, rather than necessarily nuisance artefacts (Klingenberg, 2021). Whether these can be of concern or not would depend on the scope of individual analyses (see also Machado et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Statistics of eigenvalue dispersion indices: quantifying the magnitude of phenotypic integration

Watanabe

2021

Preprint

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Quantification of the magnitude of covariation plays a major role in the studies of phenotypic integration, for which statistics based on dispersion of eigenvalues of a covariance or correlation matrix—eigenvalue dispersion indices—are commonly used. However, their use has been hindered by a lack of clear understandings on their statistical meaning and sampling properties such as the magnitude of sampling bias and error. This study remedies these issues by investigating properties of these statistics with both analytic and simulation-based approaches. The relative eigenvalue variance of a covariance matrix is known in the statistical literature as a test statistic for sphericity, thus is an appropriate measure of eccentricity of variation. The same of a correlation matrix is exactly equal to the average squared correlation, thus is a clear measure of overall integration. Exact and approximate expressions for the mean and variance of these statistics are analytically derived for the null and arbitrary conditions under multivariate normality, clarifying the effects of sample size N, number of variables p, and parameters on the sampling bias and error. Accuracy of the approximate expressions are evaluated with simulations, confirming that most of them work reasonably well with a moderate sample size (N ≥ 16–64). Importantly, sampling properties of these indices are not adversely affected by high p:N ratio, promising their utility in high-dimensional phenotypic analyses. These statistics can potentially be applied to shape variables and phylogenetically structured data, for which necessary assumptions and modifications are presented.

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Section: Discussionmentioning

confidence: 99%

Statistics of eigenvalue dispersion indices: quantifying the magnitude of phenotypic integration

Watanabe

2021

Preprint

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“…This is only possible with all the hand bones in one morphospace and all the foot bones in another. Moreover, Klingenberg (2021) states that there is no basis for the preference of methods that adjust for the Pinocchio effect. Furthermore, several analyses were repeated without the thumb and the big toes to show that those two bones do not have a large effect on the combined morphospace.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the GPA was performed based on relatively distinct forms (lateral toes and fingers vs. pollex and hallux). If the difference in specific landmarks is excessive between specimens, this could possibly lead to a Pinocchio effect (e.g., Klingenberg, 2021), which affects the position of other landmarks in the configuration. Furthermore, the distinct forms (ray 1) could hinder differences amongst forms that are more similar in the dataset (rays 2–5).…”

Section: Discussionmentioning

confidence: 99%

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Covariation of proximal finger and toe phalanges inHomo sapiens: A novel approach to assess covariation of serially corresponding structures

Heteren

Frieß

Détroit

et al. 2021

American Journal of Biological Anthropology

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Objectives: As hands and feet are serially repeated corresponding structures in tetrapods, the morphology of fingers and toes is expected to covary due to a shared developmental origin. The present study focuses on the covariation of the shape of proximal finger and toe phalanges of adult Homo sapiens to determine whether covariation is different in the first ray relative to the others, as its morphology is also different.Material and methods: Proximal phalanges of 76 individuals of unknown sex (Muséum national d'Histoire naturelle, Paris, and the Natural History Museum, London) were digitized using a surface scanner. Landmarks were positioned on 3D surface models of the phalanges. Generalized Procrustes analysis and two-block partial least squares (PLS) analyses were conducted. A novel landmark-based geometric morphometric approach focusing on covariation is based on a PCoA of the angles between PLS axes in morphospace. The results can be statistically evaluated. Results:The difference in PCo scores between the first and the other rays indicates that the integration between the thumb and the big toe is different from that between the lateral rays of the hand and foot.Discussion: We speculate that the results are possibly the evolutionary consequence of differential selection pressure on the big toe relative to the other toes related to the rise of bipedalism, which is proposed to have emerged very early in the hominin clade. In contrast, thumb morphology and its precision grip never ceased undergoing changes, suggesting less acute selection pressures related to the evolution of the precision grip.

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“…For example, an actual bird bill and its image on a digital platform are two different icons for the bill shape. Thus, much of our thought about shape and shape variation involves icons rather than actual specimens, which applies to statistical analyses (Klingenberg 2021).…”

Section: Introductionmentioning

confidence: 99%

An empirical evaluation to determine the effects of Object-orientation on the Procrustes Shape

Parthiban

Mahendiran

Chitra

et al. 2022

Preprint

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Nowadays, clear digital images of wild flora and fauna are readily available because of which the research communities are showing interest in in-situ measurements. However, object orientation is the primary constraint in estimating size and shape. It is often sighted in fossil specimens or natural history collections that produce shape deformation and distortions from their actual size and shape. We conducted an empirical evaluation with Cardboard Model Fishes to determine the shape variations due to digitization error and object orientation. Our results show that (a) Object-orientation creates Procrustes shape variations and (b) Generalized Procrustes Superimposition (using least square fit) reduces non-shape variations and the issues of Object-orientation. In the Procrustes Generalized Least Square optimization method, the variance gets distributed across landmarks that minimize the differences due to Object-orientation up to ≤ 20° away from the image plane. These minor corrections by the Procrustes method assume significance that aids in defining filter criteria for the profitable usage of digital images with Object-orientation issues taken under field conditions and in the augmentation of digital images in Machine Learning. Our results show that transfer learning (in silico) is more efficient than Procrustes detecting subtle shape variations. Our evaluation would encourage others to derive the maximum benefits of digital images acquired from Webcam, Digital cameras, Camera traps, Unmanned Vehicles, and broader applications in e-commerce platforms.

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How Exactly Did the Nose Get That Long? A Critical Rethinking of the Pinocchio Effect and How Shape Changes Relate to Landmarks

Cited by 26 publications

References 47 publications

Statistics of eigenvalue dispersion indices: quantifying the magnitude of phenotypic integration

Statistics of eigenvalue dispersion indices: quantifying the magnitude of phenotypic integration

Covariation of proximal finger and toe phalanges inHomo sapiens: A novel approach to assess covariation of serially corresponding structures

An empirical evaluation to determine the effects of Object-orientation on the Procrustes Shape

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