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

Cheryl A. Olman, Tori Espensen-Sturges, Isaac Muscanto, Julia M. Longenecker, Philip C. Burton, Andrea N. Grant, Scott R. Sponheim

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article numbere0215306
JournalPloS one
Volume14
Issue number4
DOIs
StatePublished - Apr 2019

Fingerprint

Object recognition
Semantics
Luminance
Brain
Databases
brain

Cite this

Fragmented ambiguous objects : Stimuli with stable low-level features for object recognition tasks. / Olman, Cheryl A.; Espensen-Sturges, Tori; Muscanto, Isaac; Longenecker, Julia M.; Burton, Philip C.; Grant, Andrea N.; Sponheim, Scott R.

In: PloS one, Vol. 14, No. 4, e0215306, 04.2019.

Research output: Contribution to journalArticle

@article{dcbfb37ea3e144a9b79c21e81d1662f0,
title = "Fragmented ambiguous objects: Stimuli with stable low-level features for object recognition tasks",
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 na{\"i}ve 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 na{\"i}ve 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.",
author = "Olman, {Cheryl A.} and Tori Espensen-Sturges and Isaac Muscanto and Longenecker, {Julia M.} and Burton, {Philip C.} and Grant, {Andrea N.} and Sponheim, {Scott R.}",
year = "2019",
month = "4",
doi = "10.1371/journal.pone.0215306",
language = "English (US)",
volume = "14",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "4",

}

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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85064270872&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85064270872&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0215306

DO - 10.1371/journal.pone.0215306

M3 - Article

C2 - 30973914

AN - SCOPUS:85064270872

VL - 14

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 4

M1 - e0215306

ER -