Fragmented ambiguous objects: Stimuli with stable low-level features for object recognition tasks

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 dim...

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Bibliographic Details
Published in:PloS one 2019-04, Vol.14 (4), p.e0215306-e0215306
Main Authors: Olman, Cheryl A, Espensen-Sturges, Tori, Muscanto, Isaac, Longenecker, Julia M, Burton, Philip C, Grant, Andrea N, Sponheim, Scott R
Format: Article
Language:English
Subjects:
Adolescent
Adult
Algorithms
Analysis
Biology and Life Sciences
Brain
Brain - physiology
Brain research
Feature recognition
Female
Humans
Image contrast
Image detection
Image processing
Level (quantity)
Luminance distribution
Male
Medicine and Health Sciences
Middle Aged
Neurosciences
Object recognition
Observers
Orientation
Pattern recognition
Pattern recognition (Computers)
Pattern Recognition, Visual - physiology
Photic Stimulation - methods
Physical Sciences
Psychiatry
Recognition, Psychology - physiology
Research and Analysis Methods
Schizophrenia
Social Sciences
Studies
Task complexity
Visual perception
Visual Perception - physiology
Visual stimuli
Visual tasks
Young Adult
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0215306