Simon D. Lilburn

516 total citations
19 papers, 270 citations indexed

About

Simon D. Lilburn is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Social Psychology. According to data from OpenAlex, Simon D. Lilburn has authored 19 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cognitive Neuroscience, 4 papers in Experimental and Cognitive Psychology and 3 papers in Social Psychology. Recurrent topics in Simon D. Lilburn's work include Neural and Behavioral Psychology Studies (11 papers), Visual perception and processing mechanisms (7 papers) and Neural dynamics and brain function (6 papers). Simon D. Lilburn is often cited by papers focused on Neural and Behavioral Psychology Studies (11 papers), Visual perception and processing mechanisms (7 papers) and Neural dynamics and brain function (6 papers). Simon D. Lilburn collaborates with scholars based in Australia, United States and Denmark. Simon D. Lilburn's co-authors include Philip L. Smith, David K. Sewell, Stefan Bode, Jason D. Forte, Jutta Stähl, Søren Kyllingsbæk, Adam F Osth, Daniel R. Little, Gordon D. Logan and Daniel B. Shank and has published in prestigious journals such as Journal of Neuroscience, PLoS ONE and Psychological Review.

In The Last Decade

Simon D. Lilburn

18 papers receiving 268 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simon D. Lilburn Australia 10 229 55 29 23 19 19 270
Fabrice Luyckx United Kingdom 6 161 0.7× 43 0.8× 40 1.4× 21 0.9× 5 0.3× 6 220
Kyle O. Hardman United States 7 241 1.1× 79 1.4× 16 0.6× 10 0.4× 10 0.5× 9 271
Bradley J. Wolfgang Australia 9 319 1.4× 100 1.8× 5 0.2× 23 1.0× 19 1.0× 10 341
William T. Adler United States 6 151 0.7× 29 0.5× 13 0.4× 19 0.8× 4 0.2× 6 208
Carlos González‐García Spain 12 295 1.3× 54 1.0× 22 0.8× 11 0.5× 13 0.7× 41 345
Vy A. Vo United States 7 217 0.9× 23 0.4× 21 0.7× 6 0.3× 15 0.8× 16 280
Michael Shvartsman United States 7 136 0.6× 24 0.4× 26 0.9× 11 0.5× 8 0.4× 15 174
Aspen H. Yoo United States 7 196 0.9× 43 0.8× 21 0.7× 10 0.4× 7 0.4× 10 258
Christopher L. Blume United States 7 280 1.2× 126 2.3× 24 0.8× 8 0.3× 8 0.4× 7 345
Doris Pischedda Germany 7 94 0.4× 27 0.5× 17 0.6× 6 0.3× 5 0.3× 14 139

Countries citing papers authored by Simon D. Lilburn

Since Specialization
Citations

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

Fields of papers citing papers by Simon D. Lilburn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Simon D. Lilburn. 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 Simon D. Lilburn. The network helps show where Simon D. Lilburn may publish in the future.

Co-authorship network of co-authors of Simon D. Lilburn

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

All Works

19 of 19 papers shown
1.
Logan, Gordon D., et al.. (2024). No position-specific interference from prior lists in cued recognition: A challenge for position coding (and other) theories of serial memory. Cognitive Psychology. 149. 101641–101641. 1 indexed citations
2.
Logan, Gordon D., et al.. (2024). Attention focused on memory: The episodic flanker effect with letters, words, colors, and pictures. Attention Perception & Psychophysics. 86(8). 2690–2706.
3.
Osth, Adam F, et al.. (2023). Novelty rejection in episodic memory.. Psychological Review. 130(3). 720–769. 8 indexed citations
4.
Logan, Gordon D., et al.. (2023). Serial attention to serial memory: The psychological refractory period in forward and backward cued recall. Cognitive Psychology. 145. 101583–101583. 3 indexed citations
5.
Logan, Gordon D., et al.. (2022). The spotlight turned inward: the time-course of focusing attention on memory. Psychonomic Bulletin & Review. 30(3). 1028–1040. 4 indexed citations
6.
Smith, Philip L., et al.. (2022). Diffusion theory of the antipodal “shadow” mode in continuous-outcome, coherent-motion decisions.. Psychological Review. 130(5). 1167–1202. 7 indexed citations
7.
Osth, Adam F, et al.. (2021). A circular diffusion model of continuous-outcome source memory retrieval: Contrasting continuous and threshold accounts. Psychonomic Bulletin & Review. 28(4). 1112–1130. 11 indexed citations
8.
Smith, Philip L. & Simon D. Lilburn. (2020). Vision for the blind: visual psychophysics and blinded inference for decision models. Psychonomic Bulletin & Review. 27(5). 882–910. 18 indexed citations
9.
Smith, Philip L., et al.. (2020). Modeling continuous outcome color decisions with the circular diffusion model: Metric and categorical properties.. Psychological Review. 127(4). 562–590. 12 indexed citations
10.
Lilburn, Simon D. & Philip L. Smith. (2020). A single, simple, statistical mechanism explains resource distribution and temporal updating in visual short-term memory. Cognitive Psychology. 122. 101330–101330. 4 indexed citations
11.
Lilburn, Simon D., Daniel R. Little, Adam F Osth, & Philip L. Smith. (2019). Cultural Problems Cannot Be Solved with Technical Solutions Alone. Computational Brain & Behavior. 2(3-4). 170–175. 3 indexed citations
12.
Lilburn, Simon D., Philip L. Smith, & David K. Sewell. (2019). The separable effects of feature precision and item load in visual short-term memory. Journal of Vision. 19(1). 2–2. 6 indexed citations
13.
Smith, Philip L., et al.. (2018). The power law of visual working memory characterizes attention engagement.. Psychological Review. 125(3). 435–451. 10 indexed citations
14.
Sewell, David K., P. J. Rayner, Daniel B. Shank, et al.. (2017). Causal knowledge promotes behavioral self-regulation: An example using climate change dynamics. PLoS ONE. 12(9). e0184480–e0184480. 12 indexed citations
15.
Sewell, David K., Simon D. Lilburn, & Philip L. Smith. (2016). Object selection costs in visual working memory: A diffusion model analysis of the focus of attention.. Journal of Experimental Psychology Learning Memory and Cognition. 42(11). 1673–1693. 19 indexed citations
16.
Smith, Philip L., et al.. (2016). The attention-weighted sample-size model of visual short-term memory: Attention capture predicts resource allocation and memory load. Cognitive Psychology. 89. 71–105. 20 indexed citations
17.
Smith, Philip L., David K. Sewell, & Simon D. Lilburn. (2014). From shunting inhibition to dynamic normalization: Attentional selection and decision-making in brief visual displays. Vision Research. 116(Pt B). 219–240. 15 indexed citations
18.
Sewell, David K., Simon D. Lilburn, & Philip L. Smith. (2014). An information capacity limitation of visual short-term memory.. Journal of Experimental Psychology Human Perception & Performance. 40(6). 2214–2242. 34 indexed citations
19.
Bode, Stefan, David K. Sewell, Simon D. Lilburn, et al.. (2012). Predicting Perceptual Decision Biases from Early Brain Activity. Journal of Neuroscience. 32(36). 12488–12498. 83 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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