David E. Huber

2.6k total citations
71 papers, 1.7k citations indexed

About

David E. Huber is a scholar working on Cognitive Neuroscience, Social Psychology and Experimental and Cognitive Psychology. According to data from OpenAlex, David E. Huber has authored 71 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Cognitive Neuroscience, 13 papers in Social Psychology and 13 papers in Experimental and Cognitive Psychology. Recurrent topics in David E. Huber's work include Neural and Behavioral Psychology Studies (27 papers), Memory Processes and Influences (23 papers) and Face Recognition and Perception (14 papers). David E. Huber is often cited by papers focused on Neural and Behavioral Psychology Studies (27 papers), Memory Processes and Influences (23 papers) and Face Recognition and Perception (14 papers). David E. Huber collaborates with scholars based in United States, Canada and China. David E. Huber's co-authors include Yoon‐Hee Jang, Richard M. Shiffrin, Cory A. Rieth, Xing Tian, Randall C. O’Reilly, Keith B. Lyle, John T. Wixted, Piotr Winkielman, Kevin A. Smith and Edward Vul and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Personality and Social Psychology and Psychological Review.

In The Last Decade

David E. Huber

66 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David E. Huber United States 24 1.4k 474 358 293 263 71 1.7k
Lili Sahakyan United States 20 1.3k 0.9× 526 1.1× 208 0.6× 386 1.3× 315 1.2× 54 1.5k
Geoff Ward United Kingdom 23 1.4k 1.0× 517 1.1× 169 0.5× 403 1.4× 338 1.3× 49 1.8k
Glen E. Bodner Canada 22 1.4k 1.0× 435 0.9× 438 1.2× 616 2.1× 246 0.9× 65 1.7k
William E. Hockley Canada 28 2.0k 1.4× 472 1.0× 591 1.7× 584 2.0× 428 1.6× 82 2.3k
Keith B. Lyle United States 24 1.2k 0.8× 405 0.9× 321 0.9× 488 1.7× 138 0.5× 57 1.6k
Joshua R. de Leeuw United States 11 729 0.5× 440 0.9× 248 0.7× 247 0.8× 178 0.7× 19 1.5k
Benjamin C. Storm United States 25 1.5k 1.1× 761 1.6× 199 0.6× 617 2.1× 446 1.7× 70 2.0k
Jordan M. Province United States 7 933 0.7× 376 0.8× 275 0.8× 253 0.9× 162 0.6× 7 1.5k
Gregory V. Jones United Kingdom 21 1.2k 0.8× 481 1.0× 294 0.8× 543 1.9× 255 1.0× 80 1.7k
Steve Joordens Canada 27 1.8k 1.3× 506 1.1× 465 1.3× 759 2.6× 249 0.9× 62 2.5k

Countries citing papers authored by David E. Huber

Since Specialization
Citations

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

Fields of papers citing papers by David E. Huber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David E. Huber. 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 David E. Huber. The network helps show where David E. Huber may publish in the future.

Co-authorship network of co-authors of David E. Huber

This figure shows the co-authorship network connecting the top 25 collaborators of David E. Huber. A scholar is included among the top collaborators of David E. Huber 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 David E. Huber. David E. Huber 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.
Potter, Kevin, et al.. (2021). A neural habituation account of the negative compatibility effect.. Journal of Experimental Psychology General. 150(12). 2567–2590. 3 indexed citations
2.
Cowell, Rosemary A., et al.. (2019). A hierarchical Bayesian state trace analysis for assessing monotonicity while factoring out subject, item, and trial level dependencies. Journal of Mathematical Psychology. 90. 118–131. 1 indexed citations
3.
Huber, David E., et al.. (2018). Less “story” and more “reliability” in cognitive neuroscience. Cortex. 113. 347–349. 14 indexed citations
4.
Potter, Kevin, et al.. (2018). Does inhibition cause forgetting after selective retrieval? A reanalysis and failure to replicate. Cortex. 104. 26–45. 5 indexed citations
5.
Rusconi, Patrice & David E. Huber. (2017). The perceptual wink model of non-switching attentional blink tasks. Psychonomic Bulletin & Review. 25(5). 1717–1739. 5 indexed citations
6.
Jang, Yoon‐Hee, Thomas S. Wallsten, & David E. Huber. (2011). A stochastic detection and retrieval model for the study of metacognition.. Psychological Review. 119(1). 186–200. 41 indexed citations
7.
Liu, Jiangang, Jun Li, Cory A. Rieth, et al.. (2011). A dynamic causal modeling analysis of the effective connectivities underlying top-down letter processing. Neuropsychologia. 49(5). 1177–1186. 6 indexed citations
8.
Li, Jun, Jiangang Liu, Jimin Liang, et al.. (2010). Effective connectivities of cortical regions for top-down face processing: A Dynamic Causal Modeling study. Brain Research. 1340. 40–51. 42 indexed citations
9.
Weidemann, Christoph T., David E. Huber, & Richard M. Shiffrin. (2008). Prime diagnosticity in short-term repetition priming: Is primed evidence discounted, even when it reliably indicates the correct answer?. Journal of Experimental Psychology Learning Memory and Cognition. 34(2). 257–281. 17 indexed citations
10.
Zhang, Hongchuan, Jiangang Liu, David E. Huber, et al.. (2008). Detecting faces in pure noise images: a functional MRI study on top-down perception. Neuroreport. 19(2). 229–233. 49 indexed citations
11.
Huber, David E.. (2008). Immediate priming and cognitive aftereffects.. Journal of Experimental Psychology General. 137(2). 324–347. 54 indexed citations
12.
Tian, Xing & David E. Huber. (2007). Measures of Spatial Similarity and Response Magnitude in MEG and Scalp EEG. Brain Topography. 20(3). 131–141. 24 indexed citations
13.
14.
Huber, David E. & Cory A. Rieth. (2005). Using a Neural Network Model with Synaptic Depression to Assess the Dynamics of Feature-Based Versus Configural Processing in Face Identification. eScholarship (California Digital Library). 27(27). 4 indexed citations
15.
Huber, David E. & Randall C. O’Reilly. (2003). Persistence and accommodation in short‐term priming and other perceptual paradigms: temporal segregation through synaptic depression. Cognitive Science. 27(3). 403–430. 74 indexed citations
16.
17.
Mozer, Michael C., Michael Colagrosso, & David E. Huber. (2003). Mechanisms of long-term repetition priming and skill refinement: A probabilistic pathway model. eScholarship (California Digital Library). 25(25). 4 indexed citations
18.
Huber, David E. & Denis Cousineau. (2003). A Race Model of Perceptual Forced Choice Reaction Time. eScholarship (California Digital Library). 25(25). 13 indexed citations
19.
Huber, David E., Richard M. Shiffrin, Keith B. Lyle, & Kirsten I. Ruys. (2001). Perception and preference in short-term word priming.. Psychological Review. 108(1). 149–182. 79 indexed citations
20.
Shiffrin, Richard M., et al.. (1995). Effects of category length and strength on familiarity in recognition.. Journal of Experimental Psychology Learning Memory and Cognition. 21(2). 267–287. 26 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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