RESUMEN
SUMMARY: The study of the shape variation in geometric morphometrics has an important limitation known as the Pinocchio effect. The Pinocchio effect produces artifactual variances of the landmarks and implies that it is not possible to know the morphological change structure of an object, other than by dividing the landmark sets and then comparing them. This, however, involves making prior assumptions about the pattern of variation of an object. In this study, we provide a code in R to iterate over a complete set of landmarks and test all possible combinations of landmarks until deliver those landmarks associated with the largest to the smallest morphological changes. We tested this on a sample of 28 landmarks in 143 3D models of human skulls. The results indicated that this process can result in a pooled variance of a subset of landmarks that is an order of magnitude larger than that of several other regions of the skull. This method makes it possible to describe the pattern of variation of any 2D or 3D object represented by fixed landmarks, to distinguish the shape features that have more morphological dispersion, and to avoid any aprioristic assumptions about how the morphological changes of an object behave.
El estudio de la variación de la forma en morfometría geométrica tiene una limitación importante conocida como efecto Pinocho. El efecto Pinocho produce variaciones artefactos de los puntos de referencia e implica que no es posible conocer la estructura del cambio morfológico de un objeto, salvo dividiendo los conjuntos de puntos de referencia y luego comparándolos. Sin embargo, esto implica hacer suposiciones previas sobre el patrón de variación de un objeto. En este estudio, proporcionamos un código en R para iterar sobre un conjunto completo de puntos de referencia y probar todas las combinaciones posibles de puntos de referencia hasta entregar aquellos puntos de referencia asociados con los cambios morfológicos más grandes a los más pequeños. Probamos esto en una muestra de 28 puntos de referencia en 143 modelos 3D de cráneos humanos. Los resultados indicaron que este proceso puede dar como resultado una variación combinada de un subconjunto de puntos de referencia que es un orden de magnitud mayor que el de varias otras regiones del cráneo. Este método permite describir el patrón de variación de cualquier objeto 2D o 3D representado por puntos de referencia fijos, distinguir las características de forma que tienen más dispersión morfológica y evitar suposiciones apriorísticas sobre cómo se comportan los cambios morfológicos de un objeto.
Asunto(s)
Humanos , Puntos Anatómicos de Referencia , Anatomía/instrumentación , BiometríaRESUMEN
Skeletal remains analyzed by anthropologists, paleontologists and forensic scientists are usually found fragmented or incomplete. Accurate estimations of the original morphologies are a challenge for which several digital reconstruction methods have been proposed. In this study, the accuracy of reconstructing bones based on multiple linear regression (RM) was tested. A total of 150 digital models from complete zygomatics from recent past populations (European and African American) were studied using high-density geometric morphometrics. Some landmarks (i.e., 2, 3 and 6) were coded as missing to simulate incomplete zygomatics and the missing landmarks were estimated with RM. In the zygomatics, this simulated damage affects a few square centimeters or less. Finally, the predicted and original shape data were compared. The results indicate that the predicted landmark coordinates were significantly different from the original ones, although this difference was less than the difference between the original zygomatic and the mean zygomatic in the sample. The performance of the method was affected by the location and the number of missing landmarks, with decreasing accuracy with increasing damaged area. We conclude that RM can accurately estimate the original appearance of the zygomatics when the damage is small.