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- W2938150501 abstract "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." @default.
- W2938150501 created "2019-04-25" @default.
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- W2938150501 date "2019-04-11" @default.
- W2938150501 modified "2023-09-27" @default.
- W2938150501 title "Fragmented ambiguous objects: Stimuli with stable low-level features for object recognition tasks" @default.
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- W2938150501 doi "https://doi.org/10.1371/journal.pone.0215306" @default.
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