Ralf M. Haefner

1.2k total citations
25 papers, 571 citations indexed

About

Ralf M. Haefner is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Experimental and Cognitive Psychology. According to data from OpenAlex, Ralf M. Haefner has authored 25 papers receiving a total of 571 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Cognitive Neuroscience, 4 papers in Cellular and Molecular Neuroscience and 4 papers in Experimental and Cognitive Psychology. Recurrent topics in Ralf M. Haefner's work include Neural dynamics and brain function (17 papers), Visual perception and processing mechanisms (16 papers) and Neural and Behavioral Psychology Studies (12 papers). Ralf M. Haefner is often cited by papers focused on Neural dynamics and brain function (17 papers), Visual perception and processing mechanisms (16 papers) and Neural and Behavioral Psychology Studies (12 papers). Ralf M. Haefner collaborates with scholars based in United States, Germany and Canada. Ralf M. Haefner's co-authors include Bruce G. Cumming, József Fiser, Pietro Berkes, Adrian Bondy, Sebastian Gerwinn, Matthias Bethge, Jakob H. Macke, Christopher C. Pack, Liu D. Liu and Richard T. Born and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Ralf M. Haefner

22 papers receiving 570 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ralf M. Haefner United States 12 540 150 43 42 39 25 571
Chi-Tat Law United States 7 807 1.5× 195 1.3× 48 1.1× 65 1.5× 69 1.8× 7 860
Nir Levy Israel 8 327 0.6× 119 0.8× 48 1.1× 49 1.2× 22 0.6× 18 413
Matthew Chalk France 9 470 0.9× 178 1.2× 32 0.7× 29 0.7× 38 1.0× 15 513
Eelke Spaak Netherlands 13 1.1k 2.0× 216 1.4× 47 1.1× 105 2.5× 27 0.7× 26 1.2k
Corey M. Ziemba United States 10 452 0.8× 71 0.5× 19 0.4× 38 0.9× 30 0.8× 17 506
Jarrod Robert Dowdall Germany 7 967 1.8× 292 1.9× 32 0.7× 78 1.9× 26 0.7× 10 1.0k
Thomas C. Sprague United States 17 1.3k 2.4× 71 0.5× 36 0.8× 114 2.7× 16 0.4× 30 1.3k
Pinglei Bao United States 11 447 0.8× 95 0.6× 23 0.5× 33 0.8× 55 1.4× 17 525
Maryam Bijanzadeh United States 7 447 0.8× 176 1.2× 7 0.2× 29 0.7× 38 1.0× 8 514
Franz Aiple Germany 9 520 1.0× 110 0.7× 29 0.7× 91 2.2× 22 0.6× 10 610

Countries citing papers authored by Ralf M. Haefner

Since Specialization
Citations

This map shows the geographic impact of Ralf M. Haefner'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 Ralf M. Haefner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ralf M. Haefner more than expected).

Fields of papers citing papers by Ralf M. Haefner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ralf M. Haefner. 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 Ralf M. Haefner. The network helps show where Ralf M. Haefner may publish in the future.

Co-authorship network of co-authors of Ralf M. Haefner

This figure shows the co-authorship network connecting the top 25 collaborators of Ralf M. Haefner. A scholar is included among the top collaborators of Ralf M. Haefner 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 Ralf M. Haefner. Ralf M. Haefner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
DeAngelis, Gregory C., et al.. (2025). Hierarchical motion perception as causal inference. Nature Communications. 16(1). 3868–3868. 1 indexed citations
2.
DeAngelis, Gregory C., et al.. (2024). Causal inference predicts the transition from integration to segmentation in motion perception. Scientific Reports. 14(1). 27704–27704.
3.
Peters, Benjamin, Gunnar Blohm, Ralf M. Haefner, et al.. (2024). Generative adversarial collaborations: a new model of scientific discourse. Trends in Cognitive Sciences. 29(1). 1–4.
4.
Gómez-Laberge, Camille, et al.. (2023). Weak evidence for neural correlates of task-switching in macaque V1. Journal of Neurophysiology. 129(5). 1021–1044. 3 indexed citations
5.
Haefner, Ralf M., et al.. (2023). Bayesian encoding and decoding as distinct perspectives on neural coding. Nature Neuroscience. 26(12). 2063–2072. 11 indexed citations
6.
Haefner, Ralf M., et al.. (2022). Task-induced neural covariability as a signature of approximate Bayesian learning and inference. PLoS Computational Biology. 18(3). e1009557–e1009557. 13 indexed citations
7.
Chicharro, Daniel, Stefano Panzeri, & Ralf M. Haefner. (2021). Stimulus-dependent relationships between behavioral choice and sensory neural responses. eLife. 10. 5 indexed citations
8.
Haefner, Ralf M., et al.. (2021). Relating confidence judgements to temporal biases in perceptual decision-making. eScholarship (California Digital Library). 43(43). 1 indexed citations
9.
Beck, Jeffrey M., et al.. (2021). A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS Computational Biology. 17(11). e1009517–e1009517. 23 indexed citations
10.
DiCarlo, James J., Ralf M. Haefner, Leyla Işık, et al.. (2021). How does the brain combine generative models and direct discriminative computations in high-level vision?. 3 indexed citations
11.
DeAngelis, Gregory C., et al.. (2020). A causal inference model for the perception of complex motion in the presence of self-motion. Journal of Vision. 20(11). 1631–1631. 1 indexed citations
12.
Haefner, Ralf M., et al.. (2019). Task-uninformative visual stimuli improve auditory spatial discrimination in humans but not the ideal observer. PLoS ONE. 14(9). e0215417–e0215417. 3 indexed citations
13.
Haefner, Ralf M., et al.. (2018). Differentiating between Models of Perceptual Decision Making Using Pupil Size Inferred Confidence. Journal of Neuroscience. 38(41). 8874–8888. 16 indexed citations
14.
Bondy, Adrian, Ralf M. Haefner, & Bruce G. Cumming. (2018). Feedback determines the structure of correlated variability in primary visual cortex. Nature Neuroscience. 21(4). 598–606. 89 indexed citations
15.
Haefner, Ralf M., et al.. (2017). Characterizing and interpreting the influence of internal variables on sensory activity. Current Opinion in Neurobiology. 46. 84–89. 16 indexed citations
16.
Haefner, Ralf M., Pietro Berkes, & József Fiser. (2016). Perceptual Decision-Making as Probabilistic Inference by Neural Sampling. Neuron. 90(3). 649–660. 118 indexed citations
17.
Haefner, Ralf M., et al.. (2015). A Modality-Specific Feedforward Component of Choice-Related Activity in MT. Neuron. 87(1). 208–219. 29 indexed citations
18.
Haefner, Ralf M., Sebastian Gerwinn, Jakob H. Macke, & Matthias Bethge. (2013). Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nature Neuroscience. 16(2). 235–242. 110 indexed citations
19.
Tanabe, Seiji, Ralf M. Haefner, & Bruce G. Cumming. (2011). Suppressive Mechanisms in Monkey V1 Help to Solve the Stereo Correspondence Problem. Journal of Neuroscience. 31(22). 8295–8305. 25 indexed citations
20.
Haefner, Ralf M. & Bruce G. Cumming. (2008). Adaptation to Natural Binocular Disparities in Primate V1 Explained by a Generalized Energy Model. Neuron. 57(1). 147–158. 42 indexed citations

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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