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Vision Sciences Society Annual Meeting Abstract September 2024

Cortical representations of core visual material dimensions

Hua-Chun Sun; Filipp Schmidt; Alexandra C. Schmid; Martin N. Hebart; Roland W. Fleming

Journal of Vision. 2024; 24(10):285. https://doi.org/10.1167/jov.24.10.285

Vision Sciences Society Annual Meeting Abstract September 2024

Quantifying the role of perceived curvature in the processing of natural object images

Laura M. Stoinski; Katya Müller; Martin N. Hebart

Journal of Vision. 2024; 24(10):975. https://doi.org/10.1167/jov.24.10.975

Vision Sciences Society Annual Meeting Abstract September 2024

Towards a detailed functional neuroanatomy of the ventral visual cortex

Josefine A. Zerbe; Maggie Mae Mell; Juliane Damm; Marianna Schmidt; Nikolaus Weiskopf; Evgeniya Kirilina; Tomas Knapen; Martin N. Hebart

Journal of Vision. 2024; 24(10):931. https://doi.org/10.1167/jov.24.10.931

Vision Sciences Society Annual Meeting Abstract August 2023

Revisiting the animacy, size, and curvature organization of human visual cortex

Laura M. Stoinski; Oliver Contier; Talia Konkle; Martin N. Hebart

Journal of Vision. 2023; 23(9):5072. https://doi.org/10.1167/jov.23.9.5072

Vision Sciences Society Annual Meeting Abstract August 2023

Assessing the feasibility of high stimulus presentation rates for contrasting conditions in functional MRI studies

Johannes Roth; Yoichi Miyawaki; Martin N. Hebart

Journal of Vision. 2023; 23(9):5070. https://doi.org/10.1167/jov.23.9.5070

Vision Sciences Society Annual Meeting Abstract August 2023

THINGS-drawings: A large-scale dataset containing human sketches of 1,854 object concepts

Judith E. Fan; Kushin Mukherjee; Holly Huey; Martin N. Hebart; Wilma A. Bainbridge

Journal of Vision. 2023; 23(9):5975. https://doi.org/10.1167/jov.23.9.5975

Vision Sciences Society Annual Meeting Abstract December 2022

Similarities and differences in the spatio-temporal neural dynamics underlying the recognition of natural images and line drawings

Johannes Singer; Radoslaw Martin Cichy; Martin N Hebart

Journal of Vision. 2022; 22(14):3248. https://doi.org/10.1167/jov.22.14.3248

Vision Sciences Society Annual Meeting Abstract December 2022

The effect of downsampling neural network activation patterns for improving their correspondence with brains and behavior

Yuxuan Dai; Martin N. Hebart

Journal of Vision. 2022; 22(14):3241. https://doi.org/10.1167/jov.22.14.3241

Vision Sciences Society Annual Meeting Abstract December 2022

The THINGS initiative: a global initiative of researchers for representative sampling of objects in brains, behavior, and computational models

Martin N. Hebart

Journal of Vision. 2022; 22(14):3203. https://doi.org/10.1167/jov.22.14.3203

Vision Sciences Society Annual Meeting Abstract December 2022

Semantics, not Atypicality Reflect Memorability Across Concrete Objects

Max A. Kramer; Martin N. Hebart; Chris I. Baker; Wima A. Bainbridge

Journal of Vision. 2022; 22(14):3634. https://doi.org/10.1167/jov.22.14.3634

Vision Sciences Society Annual Meeting Abstract December 2022

Automatic generation of semantic feature norms of objects using GPT-3

Hannes J. Hansen; Martin N. Hebart

Journal of Vision. 2022; 22(14):3461. https://doi.org/10.1167/jov.22.14.3461

Vision Sciences Society Annual Meeting Abstract December 2022

Distributed representation of behaviorally-relevant object dimensions in the human brain

Oliver Contier; Martin N. Hebart

Journal of Vision. 2022; 22(14):3951. https://doi.org/10.1167/jov.22.14.3951

Vision Sciences Society Annual Meeting Abstract December 2022

Efficiently-generated object similarity scores predicted from human feature ratings and deep neural network activations

Martin N Hebart; Philipp Kaniuth; Jonas Perkuhn

Journal of Vision. 2022; 22(14):4057. https://doi.org/10.1167/jov.22.14.4057

Vision Sciences Society Annual Meeting Abstract December 2022

Interpretable object dimensions in deep neural networks and their similarities to human representations

Lukas Muttenthaler; Martin N. Hebart

Journal of Vision. 2022; 22(14):4516. https://doi.org/10.1167/jov.22.14.4516

Article February 2022

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

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Johannes J. D. Singer; Katja Seeliger; Tim C. Kietzmann; Martin N. Hebart
TAGS: convolutional neural networks, generalization (psychology), natural scenes, registered diagnostic medical sonographer, shape, singers, training, attenuation, multidimensional scaling analysis
Journal of Vision. 2022; 22(2):4. https://doi.org/10.1167/jov.22.2.4

Includes: Supplemental Content

Vision Sciences Society Annual Meeting Abstract September 2021

Emergent dimensions underlying human perception of the reachable world

Emilie L. Josephs; Martin N. Hebart; Talia Konkle

Journal of Vision. 2021; 21(9):2154. https://doi.org/10.1167/jov.21.9.2154

Vision Sciences Society Annual Meeting Abstract September 2021

The mental representation of materials distilled from >1.5 million similarity judgements

Filipp Schmidt; Martin N. Hebart; Alexandra Schmid; Roland W. Fleming

Journal of Vision. 2021; 21(9):1981. https://doi.org/10.1167/jov.21.9.1981

Vision Sciences Society Annual Meeting Abstract September 2021

A data-driven investigation of human action representations

Diana C Dima; Tyler Tomita; Christopher Honey; Martin N Hebart; Leyla Isik

Journal of Vision. 2021; 21(9):2552. https://doi.org/10.1167/jov.21.9.2552

Vision Sciences Society Annual Meeting Abstract September 2021

THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images

Oliver Contier; Martin N. Hebart; Adam H. Dickter; Lina Teichmann; Alexis Kidder; Anna Corriveau; Charles Zheng; Maryam Vaziri-Pashkam; Charles Baker

Journal of Vision. 2021; 21(9):2633. https://doi.org/10.1167/jov.21.9.2633

Vision Sciences Society Annual Meeting Abstract October 2020

Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data

Philipp Kaniuth; Martin N Hebart

Journal of Vision. 2020; 20(11):1076. https://doi.org/10.1167/jov.20.11.1076

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Cortical representations of core visual material dimensions

Quantifying the role of perceived curvature in the processing of natural object images

Towards a detailed functional neuroanatomy of the ventral visual cortex

Revisiting the animacy, size, and curvature organization of human visual cortex

Assessing the feasibility of high stimulus presentation rates for contrasting conditions in functional MRI studies

THINGS-drawings: A large-scale dataset containing human sketches of 1,854 object concepts

Similarities and differences in the spatio-temporal neural dynamics underlying the recognition of natural images and line drawings

The effect of downsampling neural network activation patterns for improving their correspondence with brains and behavior

The THINGS initiative: a global initiative of researchers for representative sampling of objects in brains, behavior, and computational models

Semantics, not Atypicality Reflect Memorability Across Concrete Objects

Automatic generation of semantic feature norms of objects using GPT-3

Distributed representation of behaviorally-relevant object dimensions in the human brain

Efficiently-generated object similarity scores predicted from human feature ratings and deep neural network activations

Interpretable object dimensions in deep neural networks and their similarities to human representations

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Emergent dimensions underlying human perception of the reachable world

The mental representation of materials distilled from >1.5 million similarity judgements

A data-driven investigation of human action representations

THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images

Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data

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