TY - JOUR
T1 - Fragmented ambiguous objects
T2 - Stimuli with stable low-level features for object recognition tasks
AU - Olman, Cheryl A.
AU - Espensen-Sturges, Tori
AU - Muscanto, Isaac
AU - Longenecker, Julia M.
AU - Burton, Philip C.
AU - Grant, Andrea N.
AU - Sponheim, Scott R.
N1 - Publisher Copyright:
© 2019 Olman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/4
Y1 - 2019/4
N2 - Visual object recognition is a complex skill that relies on the interaction of many spatially distinct and specialized visual areas in the human brain. One tool that can help us better understand these specializations and interactions is a set of visual stimuli that do not differ along low-level dimensions (e.g., orientation, contrast) but do differ along high-level dimensions, such as whether a real-world object can be detected. The present work creates a set of line segment-based images that are matched for luminance, contrast, and orientation distribution (both for single elements and for pair-wise combinations) but result in a range of object and non-object percepts. Image generation started with images of isolated objects taken from publicly available databases and then progressed through 3-stages: a computer algorithm generating 718 candidate images, expert observers selecting 217 for further consideration, and naïve observers performing final ratings. This process identified a set of 100 images that all have the same low-level properties but cover a range of recognizability (proportion of naïve observers (N = 120) who indicated that the stimulus “contained a known object”) and semantic stability (consistency across the categories of living, non-living/manipulable, and non-living/non-manipulable when the same observers named “known” objects). Stimuli are available at https://github.com/caolman/FAOT.git.
AB - Visual object recognition is a complex skill that relies on the interaction of many spatially distinct and specialized visual areas in the human brain. One tool that can help us better understand these specializations and interactions is a set of visual stimuli that do not differ along low-level dimensions (e.g., orientation, contrast) but do differ along high-level dimensions, such as whether a real-world object can be detected. The present work creates a set of line segment-based images that are matched for luminance, contrast, and orientation distribution (both for single elements and for pair-wise combinations) but result in a range of object and non-object percepts. Image generation started with images of isolated objects taken from publicly available databases and then progressed through 3-stages: a computer algorithm generating 718 candidate images, expert observers selecting 217 for further consideration, and naïve observers performing final ratings. This process identified a set of 100 images that all have the same low-level properties but cover a range of recognizability (proportion of naïve observers (N = 120) who indicated that the stimulus “contained a known object”) and semantic stability (consistency across the categories of living, non-living/manipulable, and non-living/non-manipulable when the same observers named “known” objects). Stimuli are available at https://github.com/caolman/FAOT.git.
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U2 - 10.1371/journal.pone.0215306
DO - 10.1371/journal.pone.0215306
M3 - Article
C2 - 30973914
AN - SCOPUS:85064270872
SN - 1932-6203
VL - 14
JO - PloS one
JF - PloS one
IS - 4
M1 - e0215306
ER -