Hannah Sheahan

2.0k total citations · 1 hit paper
10 papers, 338 citations indexed

About

Hannah Sheahan is a scholar working on Cognitive Neuroscience, Social Psychology and Artificial Intelligence. According to data from OpenAlex, Hannah Sheahan has authored 10 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cognitive Neuroscience, 3 papers in Social Psychology and 2 papers in Artificial Intelligence. Recurrent topics in Hannah Sheahan's work include Motor Control and Adaptation (4 papers), Neural dynamics and brain function (4 papers) and Visual perception and processing mechanisms (3 papers). Hannah Sheahan is often cited by papers focused on Motor Control and Adaptation (4 papers), Neural dynamics and brain function (4 papers) and Visual perception and processing mechanisms (3 papers). Hannah Sheahan collaborates with scholars based in United Kingdom, United States and Italy. Hannah Sheahan's co-authors include Daniel M. Wolpert, David W. Franklin, Christopher Summerfield, Fabrice Luyckx, Koenraad Vandevoorde, James N. Ingram, David J. Herzfeld, Scott T. Albert, Reza Shadmehr and Stephanie Nelli and has published in prestigious journals such as Science, Neuron and Scientific Reports.

In The Last Decade

Hannah Sheahan

10 papers receiving 334 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

AI can help humans find common ground in democratic deliberation

2024 37 citations Michiel A. Bakker, Hannah Sheahan et al. Science profile →

Peers

Hannah Sheahan

CN

SP

BE

AI

DEP

Krishna Prasad Miyapuram India

Wolfgang Mack Germany

Jay B. Martin United States

Ignasi Cos France

N. Walker United States

Lukas Strnad Italy

Elizabeth E. Johns Canada

Benjamin D. Zinszer United States

David A. Caulton United States

Sylvia Vitello United Kingdom

Krishna Prasad Miyapuram India

Hannah Sheahan

254 ×1.0

CN

78 ×0.8

SP

19 ×0.3

BE

32 ×0.6

AI

31 ×0.6

DEP

Citations per year, relative to Hannah Sheahan Hannah Sheahan (= 1×) peers Krishna Prasad Miyapuram

Countries citing papers authored by Hannah Sheahan

Since Specialization

Citations

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

Fields of papers citing papers by Hannah Sheahan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hannah Sheahan. 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 Hannah Sheahan. The network helps show where Hannah Sheahan may publish in the future.

Co-authorship network of co-authors of Hannah Sheahan

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

All Works

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10 of 10 papers shown

1.

Lampinen, Andrew K., Ishita Dasgupta, Stephanie C. Y. Chan, et al.. (2024). Language models, like humans, show content effects on reasoning tasks. PNAS Nexus. 3(7). pgae233–pgae233. 24 indexed citations

2.

Sheahan, Hannah, et al.. (2024). Zero-shot counting with a dual-stream neural network model. Neuron. 112(24). 4147–4158.e5. 1 indexed citations

3.

Bakker, Michiel A., Hannah Sheahan, Martin J. Chadwick, et al.. (2024). AI can help humans find common ground in democratic deliberation. Science. 386(6719). eadq2852–eadq2852. 37 indexed citations breakdown →

4.

Muhle-Karbe, Paul S., Hannah Sheahan, Giovanni Pezzulo, et al.. (2023). Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex. Neuron. 111(23). 3885–3899.e6. 20 indexed citations

5.

Albert, Scott T., et al.. (2021). An implicit memory of errors limits human sensorimotor adaptation. Nature Human Behaviour. 5(7). 920–934. 50 indexed citations

6.

Sheahan, Hannah, et al.. (2021). Neural state space alignment for magnitude generalization in humans and recurrent networks. Neuron. 109(7). 1214–1226.e8. 28 indexed citations

7.

Sheahan, Hannah, et al.. (2019). The visual geometry of a tool modulates generalization during adaptation. Scientific Reports. 9(1). 2731–2731. 2 indexed citations

8.

Summerfield, Christopher, Fabrice Luyckx, & Hannah Sheahan. (2019). Structure learning and the posterior parietal cortex. Progress in Neurobiology. 184. 101717–101717. 54 indexed citations

9.

Sheahan, Hannah, et al.. (2018). Imagery of movements immediately following performance allows learning of motor skills that interfere. Scientific Reports. 8(1). 14330–14330. 27 indexed citations

10.

Sheahan, Hannah, David W. Franklin, & Daniel M. Wolpert. (2016). Motor Planning, Not Execution, Separates Motor Memories. Neuron. 92(4). 773–779. 95 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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