James N. Ingram

2.5k total citations
33 papers, 1.5k citations indexed

About

James N. Ingram is a scholar working on Cognitive Neuroscience, Social Psychology and Biomedical Engineering. According to data from OpenAlex, James N. Ingram has authored 33 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Cognitive Neuroscience, 17 papers in Social Psychology and 14 papers in Biomedical Engineering. Recurrent topics in James N. Ingram's work include Motor Control and Adaptation (27 papers), Action Observation and Synchronization (17 papers) and Muscle activation and electromyography studies (13 papers). James N. Ingram is often cited by papers focused on Motor Control and Adaptation (27 papers), Action Observation and Synchronization (17 papers) and Muscle activation and electromyography studies (13 papers). James N. Ingram collaborates with scholars based in United Kingdom, United States and Canada. James N. Ingram's co-authors include Daniel M. Wolpert, Ian S. Howard, Konrad P. Körding, Martin Voss, Patrick Haggard, J. Randall Flanagan, David W. Franklin, Michael N. Shadlen, Christoph Teufel and Paul C. Fletcher and has published in prestigious journals such as Journal of Neuroscience, Nature Neuroscience and PLoS ONE.

In The Last Decade

James N. Ingram

33 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James N. Ingram United Kingdom 20 1.3k 537 486 121 102 33 1.5k
Aymar de Rugy France 24 1.2k 1.0× 707 1.3× 422 0.9× 139 1.1× 197 1.9× 69 1.6k
Ian S. Howard United Kingdom 20 1.1k 0.9× 522 1.0× 490 1.0× 221 1.8× 94 0.9× 58 1.5k
Andrew A. G. Mattar Canada 11 1.0k 0.8× 571 1.1× 510 1.0× 183 1.5× 171 1.7× 14 1.4k
Kunlin Wei China 23 973 0.8× 752 1.4× 436 0.9× 136 1.1× 187 1.8× 63 1.6k
Philip N. Sabes United States 23 2.1k 1.7× 693 1.3× 525 1.1× 129 1.1× 181 1.8× 39 2.4k
Miya K. Rand United States 22 1.6k 1.3× 432 0.8× 468 1.0× 222 1.8× 134 1.3× 51 2.0k
Masaya Hirashima Japan 20 820 0.7× 562 1.0× 286 0.6× 106 0.9× 98 1.0× 40 1.3k
Jason Friedman Israel 18 668 0.5× 359 0.7× 227 0.5× 84 0.7× 58 0.6× 68 925
Emmanuel Guigon France 17 1.1k 0.9× 383 0.7× 284 0.6× 68 0.6× 115 1.1× 41 1.3k
Laure Fernandez France 11 839 0.7× 664 1.2× 188 0.4× 97 0.8× 168 1.6× 19 1.1k

Countries citing papers authored by James N. Ingram

Since Specialization
Citations

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

Fields of papers citing papers by James N. Ingram

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James N. Ingram. 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 James N. Ingram. The network helps show where James N. Ingram may publish in the future.

Co-authorship network of co-authors of James N. Ingram

This figure shows the co-authorship network connecting the top 25 collaborators of James N. Ingram. A scholar is included among the top collaborators of James N. Ingram 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 James N. Ingram. James N. Ingram 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.
Ingram, James N., et al.. (2024). Exploration biases forelimb reaching strategies. Cell Reports. 43(4). 113958–113958. 3 indexed citations
2.
Ingram, James N., et al.. (2024). Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience. Nature Human Behaviour. 8(6). 1209–1224. 4 indexed citations
3.
Carroll, Timothy J., Daniel McNamee, James N. Ingram, & Daniel M. Wolpert. (2019). Rapid Visuomotor Responses Reflect Value-Based Decisions. Journal of Neuroscience. 39(20). 3906–3920. 30 indexed citations
4.
Ingram, James N., et al.. (2019). Unimodal statistical learning produces multimodal object-like representations. eLife. 8. 13 indexed citations
5.
Sheahan, Hannah, et al.. (2019). The visual geometry of a tool modulates generalization during adaptation. Scientific Reports. 9(1). 2731–2731. 2 indexed citations
6.
Ingram, James N., et al.. (2018). Adaptive coupling influences generalization of sensorimotor learning. PLoS ONE. 13(11). e0207482–e0207482. 3 indexed citations
7.
Heald, James B., James N. Ingram, J. Randall Flanagan, & Daniel M. Wolpert. (2018). Multiple motor memories are learned to control different points on a tool. Nature Human Behaviour. 2(4). 300–311. 39 indexed citations
8.
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
9.
Ingram, James N., et al.. (2017). An error-tuned model for sensorimotor learning. PLoS Computational Biology. 13(12). e1005883–e1005883. 9 indexed citations
10.
Ingram, James N., et al.. (2014). Motor Effort Alters Changes of Mind in Sensorimotor Decision Making. PLoS ONE. 9(3). e92681–e92681. 67 indexed citations
11.
Ingram, James N., Ian S. Howard, J. Randall Flanagan, & Daniel M. Wolpert. (2011). A Single-Rate Context-Dependent Learning Process Underlies Rapid Adaptation to Familiar Object Dynamics. PLoS Computational Biology. 7(9). e1002196–e1002196. 32 indexed citations
12.
Ingram, James N. & Daniel M. Wolpert. (2011). Naturalistic approaches to sensorimotor control. Progress in brain research. 191. 3–29. 35 indexed citations
13.
Ingram, James N., Ian S. Howard, J. Randall Flanagan, & Daniel M. Wolpert. (2010). Multiple Grasp-Specific Representations of Tool Dynamics Mediate Skillful Manipulation. Current Biology. 20(7). 618–623. 52 indexed citations
14.
Teufel, Christoph, et al.. (2010). Deficits in sensory prediction are related to delusional ideation in healthy individuals. Neuropsychologia. 48(14). 4169–4172. 59 indexed citations
15.
Howard, Ian S., James N. Ingram, & Daniel M. Wolpert. (2009). A modular planar robotic manipulandum with end-point torque control. Journal of Neuroscience Methods. 181(2). 199–211. 167 indexed citations
16.
Howard, Ian S., James N. Ingram, & Daniel M. Wolpert. (2008). Composition and Decomposition in Bimanual Dynamic Learning. Journal of Neuroscience. 28(42). 10531–10540. 28 indexed citations
17.
Voss, Martin, James N. Ingram, Daniel M. Wolpert, & Patrick Haggard. (2008). Mere Expectation to Move Causes Attenuation of Sensory Signals. PLoS ONE. 3(8). e2866–e2866. 88 indexed citations
18.
Ingram, James N., Konrad P. Körding, Ian S. Howard, & Daniel M. Wolpert. (2008). The statistics of natural hand movements. Experimental Brain Research. 188(2). 223–236. 229 indexed citations
19.
Voss, Martin, James N. Ingram, Patrick Haggard, & Daniel M. Wolpert. (2005). Sensorimotor attenuation by central motor command signals in the absence of movement. Nature Neuroscience. 9(1). 26–27. 169 indexed citations
20.
Körding, Konrad P., Izumi Fukunaga, Ian S. Howard, James N. Ingram, & Daniel M. Wolpert. (2004). A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control. PLoS Biology. 2(10). e330–e330. 47 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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