Katharina Dobs

869 total citations
17 papers, 436 citations indexed

About

Katharina Dobs is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology. According to data from OpenAlex, Katharina Dobs has authored 17 papers receiving a total of 436 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cognitive Neuroscience, 9 papers in Computer Vision and Pattern Recognition and 5 papers in Experimental and Cognitive Psychology. Recurrent topics in Katharina Dobs's work include Face Recognition and Perception (16 papers), Face recognition and analysis (9 papers) and Visual Attention and Saliency Detection (5 papers). Katharina Dobs is often cited by papers focused on Face Recognition and Perception (16 papers), Face recognition and analysis (9 papers) and Visual Attention and Saliency Detection (5 papers). Katharina Dobs collaborates with scholars based in Germany, United States and France. Katharina Dobs's co-authors include Nancy Kanwisher, Johannes Schultz, I Bülthoff, Dimitrios Pantazis, Leyla Işık, Meenakshi Khosla, Alexander J.E. Kell, Justin L. Gardner, Martin Breidt and Quoc C. Vuong and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and NeuroImage.

In The Last Decade

Katharina Dobs

16 papers receiving 427 citations

Peers

Countries citing papers authored by Katharina Dobs

Since Specialization

Citations

This map shows the geographic impact of Katharina Dobs's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Katharina Dobs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katharina Dobs more than expected).

Fields of papers citing papers by Katharina Dobs

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katharina Dobs. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Katharina Dobs. The network helps show where Katharina Dobs may publish in the future.

Co-authorship network of co-authors of Katharina Dobs

This figure shows the co-authorship network connecting the top 25 collaborators of Katharina Dobs. A scholar is included among the top collaborators of Katharina Dobs based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Katharina Dobs. Katharina Dobs is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

#	Work	Indexed citations
1	Category selectivity as a window into behavioral relevance 2025 ·Cognitive Neuroscience ·(unknown), Katharina Dobs	0
2	Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition 2025 ·PLoS Computational Biology ·(unknown), Katharina Dobs	1
3	Artificial intelligence meets body sense: task-driven neural networks reveal computational principles of the proprioceptive pathway 2024 ·Signal Transduction and Targeted Therapy ·(unknown), Frank Bremmer, Katharina Dobs	1
4	Models of vision need some action 2023 ·Behavioral and Brain Sciences ·Constantin A. Rothkopf, Frank Bremmer, Katja Fiehler, Katharina Dobs, Jochen Triesch	1
5	CNNs reveal the computational implausibility of the expertise hypothesis 2023 ·iScience ·Nancy Kanwisher, (unknown), Katharina Dobs	6
6	Using artificial neural networks to ask ‘why’ questions of minds and brains 2023 ·Trends in Neurosciences ·Nancy Kanwisher, Meenakshi Khosla, Katharina Dobs	68
7	Deep neural networks optimized for both face detection and face discrimination most accurately predict face-selective neurons in macaque inferior temporal cortex 2023 ·Kohitij Kar, Nancy Kanwisher, Katharina Dobs	1
8	Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition 2023 ·Proceedings of the National Academy of Sciences ·Katharina Dobs, (unknown), (unknown), Nancy Kanwisher	13
9	Brain-like functional specialization emerges spontaneously in deep neural networks 2022 ·Science Advances ·Katharina Dobs, (unknown), Alexander J.E. Kell, Nancy Kanwisher	69
10	Using task-optimized neural networks to understand why brains have specialized processing for faces 2020 ·Journal of Vision ·Katharina Dobs, Alexander J.E. Kell, (unknown), Michael S. Cohen, Nancy Kanwisher	3
11	How face perception unfolds over time 2019 ·Nature Communications ·Katharina Dobs, Leyla Işık, Dimitrios Pantazis, Nancy Kanwisher	137
12	Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks 2019 ·Katharina Dobs, Alexander J.E. Kell, (unknown), Michael B. Cohen, Nancy Kanwisher	3
13	Task-dependent enhancement of facial expression and identity representations in human cortex 2018 ·NeuroImage ·Katharina Dobs, Johannes Schultz, I Bülthoff, Justin L. Gardner	29
14	Use and Usefulness of Dynamic Face Stimuli for Face Perception Studies—a Review of Behavioral Findings and Methodology 2018 ·Frontiers in Psychology ·Katharina Dobs, I Bülthoff, Johannes Schultz	54
15	Near-optimal integration of facial form and motion 2017 ·Scientific Reports ·Katharina Dobs, Wei Ji, Leila Reddy	8
16	Identity information content depends on the type of facial movement 2016 ·Scientific Reports ·Katharina Dobs, I Bülthoff, Johannes Schultz	14
17	Quantifying human sensitivity to spatio-temporal information in dynamic faces 2014 ·Vision Research ·Katharina Dobs, I Bülthoff, Martin Breidt, Quoc C. Vuong, Cristóbal Curio, Johannes Schultz	28

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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