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.
Bibliographical noteFunding Information:
This work was supported by the VA Merit Grant CX000227 and NIH R01 MH112583 to SS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 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.