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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images
Marcie L. King, Iris I.A. Groen, Adam Steel, et al.
NeuroImage (2019) Vol. 197, pp. 368-382
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Grace W. Lindsay
Journal of Cognitive Neuroscience (2020) Vol. 33, Iss. 10, pp. 2017-2031
Open Access | Times Cited: 442
Limits to visual representational correspondence between convolutional neural networks and the human brain
Yaoda Xu, Maryam Vaziri-Pashkam
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 115
Categorical representation from sound and sight in the ventral occipito-temporal cortex of sighted and blind
Stefania Mattioni, Mohamed Rezk, Ceren Battal, et al.
eLife (2020) Vol. 9
Open Access | Times Cited: 75
Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions
Greta Tuckute, Jenelle Feather, Dana Boebinger, et al.
PLoS Biology (2023) Vol. 21, Iss. 12, pp. e3002366-e3002366
Open Access | Times Cited: 25
Connectivity analyses for task-based fMRI
Shenyang Huang, Felipe De Brigard, Roberto Cabeza, et al.
Physics of Life Reviews (2024) Vol. 49, pp. 139-156
Open Access | Times Cited: 11
Neural representations underlying psychological responses to natural and artificial features in indoor architecture.
Izabela Maria Sztuka, Maxi Becker, Simone Kühn
Journal of Environmental Psychology (2025), pp. 102553-102553
Closed Access | Times Cited: 1
Dimensions underlying human understanding of the reachable world
Emilie Josephs, Martin N. Hebart, Talia Konkle
Cognition (2023) Vol. 234, pp. 105368-105368
Open Access | Times Cited: 16
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks
Lukas Muttenthaler, Martin N. Hebart
Frontiers in Neuroinformatics (2021) Vol. 15
Open Access | Times Cited: 31
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Meike Nauta, Annemarie Jutte, Jesper Provoost, et al.
Communications in computer and information science (2021), pp. 441-456
Closed Access | Times Cited: 29
Recent advances in understanding object recognition in the human brain: deep neural networks, temporal dynamics, and context
Susan G. Wardle, Chris I. Baker
F1000Research (2020) Vol. 9, pp. 590-590
Open Access | Times Cited: 24
Disentangling five dimensions of animacy in human brain and behaviour
Kamila M. Jozwik, Elias Najarro, Jasper J.F. van den Bosch, et al.
Communications Biology (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 13
The Emotional Facet of Subjective and Neural Indices of Similarity
Martina Riberto, Gorana Pobric, Deborah Talmi
Brain Topography (2019) Vol. 32, Iss. 6, pp. 956-964
Open Access | Times Cited: 21
Visual recognition memory of scenes is driven by categorical, not sensory, visual representations.
Ricardo Morales‐Torres, Erik A. Wing, Lifu Deng, et al.
Journal of Neuroscience (2024) Vol. 44, Iss. 21, pp. e1479232024-e1479232024
Closed Access | Times Cited: 2
Representing stuff in the human brain
Alexandra C. Schmid, Katja Doerschner
Current Opinion in Behavioral Sciences (2019) Vol. 30, pp. 178-185
Open Access | Times Cited: 17
General object-based features account for letter perception
Daniel Janini, Chris Hamblin, Arturo Deza, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 9, pp. e1010522-e1010522
Open Access | Times Cited: 9
Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans
Juhyeon Lee, Minyoung Jung, Niv Lustig, et al.
Human Brain Mapping (2023) Vol. 44, Iss. 5, pp. 2018-2038
Open Access | Times Cited: 5
Similarity and structured representation in human and nonhuman apes
Carl J. Hodgetts, James Close, Ulrike Hahn
Cognition (2023) Vol. 236, pp. 105419-105419
Open Access | Times Cited: 5
Empirically Identifying and Computationally Modeling the Brain–Behavior Relationship for Human Scene Categorization
Agnessa Karapetian, Antoniya Boyanova, Muthukumar Pandaram, et al.
Journal of Cognitive Neuroscience (2023) Vol. 35, Iss. 11, pp. 1879-1897
Open Access | Times Cited: 5
Reaction times predict dynamic brain representations measured with MEG for only some object categorisation tasks
Erika W. Contini, Erin Goddard, Susan G. Wardle
Neuropsychologia (2020) Vol. 151, pp. 107687-107687
Closed Access | Times Cited: 14
DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains
Xiayu Chen, Ming Zhou, Zhengxin Gong, et al.
Frontiers in Computational Neuroscience (2020) Vol. 14
Open Access | Times Cited: 14
Limited correspondence in visual representation between the human brain and convolutional neural networks
Yaoda Xu, Maryam Vaziri-Pashkam
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 13
Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network
Hui Zhang, Xuetong Ding, Ning Liu, et al.
NeuroImage (2023) Vol. 273, pp. 120067-120067
Open Access | Times Cited: 4
“Identifying and characterizing scene representations relevant for categorization behavior”
Johannes Singer, Agnessa Karapetian, Martin N. Hebart, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 4
Improved prediction of behavioral and neural similarity spaces using pruned DNNs
Priya Tarigopula, Scott L. Fairhall, Anna Bavaresco, et al.
Neural Networks (2023) Vol. 168, pp. 89-104
Open Access | Times Cited: 4
Naturalistic food categories are driven by subjective estimates rather than objective measures of food qualities
Madeline Carrington, Alexander Liu, Caroline Candy, et al.
Food Quality and Preference (2023) Vol. 113, pp. 105073-105073
Open Access | Times Cited: 3
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