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Shape detection of Gaborized outline versions of everyday objects

We previously tested the identifiability of six versions of Gaborized outlines of everyday objects, differing in the orientations assigned to elements inside and outside the outline. We found significant differences in identifiability between the versions, and related a number of stimulus metrics to...

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Detalles Bibliográficos
Autores principales: Sassi, Michaël, Machilsen, Bart, Wagemans, Johan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pion 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3589903/
https://www.ncbi.nlm.nih.gov/pubmed/23483752
http://dx.doi.org/10.1068/i0499
Descripción
Sumario:We previously tested the identifiability of six versions of Gaborized outlines of everyday objects, differing in the orientations assigned to elements inside and outside the outline. We found significant differences in identifiability between the versions, and related a number of stimulus metrics to identifiability [Sassi, M., Vancleef, K., Machilsen, B., Panis, S., & Wagemans, J. (2010). Identification of everyday objects on the basis of Gaborized outline versions. i-Perception, 1(3), 121–142]. In this study, after retesting the identifiability of new variants of three of the stimulus versions, we tested their robustness to local orientation jitter in a detection experiment. In general, our results replicated the key findings from the previous study, and allowed us to substantiate our earlier interpretations of the effects of our stimulus metrics and of the performance differences between the different stimulus versions. The results of the detection task revealed a different ranking order of stimulus versions than the identification task. By examining the parallels and differences between the effects of our stimulus metrics in the two tasks, we found evidence for a trade-off between shape detectability and identifiability. The generally simple and smooth shapes that yield the strongest contour integration and most robust detectability tend to lack the distinguishing features necessary for clear-cut identification. Conversely, contours that do contain such identifying features tend to be inherently more complex and, therefore, yield weaker integration and less robust detectability.