Tom Beesley

1.7k total citations
39 papers, 1.1k citations indexed

About

Tom Beesley is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Social Psychology. According to data from OpenAlex, Tom Beesley has authored 39 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Cognitive Neuroscience, 10 papers in Developmental and Educational Psychology and 6 papers in Social Psychology. Recurrent topics in Tom Beesley's work include Neural and Behavioral Psychology Studies (21 papers), Memory and Neural Mechanisms (13 papers) and Child and Animal Learning Development (10 papers). Tom Beesley is often cited by papers focused on Neural and Behavioral Psychology Studies (21 papers), Memory and Neural Mechanisms (13 papers) and Child and Animal Learning Development (10 papers). Tom Beesley collaborates with scholars based in United Kingdom, Australia and Spain. Tom Beesley's co-authors include Mike E. Le Pelley, Oren Griffiths, Daniel Pearson, Andy J. Wills, Chris J. Mitchell, David N. George, David R. Shanks, Miguel A. Vadillo, David Luque and Catherine Haslam and has published in prestigious journals such as Journal of Neuroscience, Psychological Bulletin and Cognition.

In The Last Decade

Tom Beesley

38 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tom Beesley United Kingdom 18 895 232 186 125 95 39 1.1k
Oren Griffiths Australia 17 705 0.8× 203 0.9× 155 0.8× 85 0.7× 108 1.1× 45 924
Daniel Pearson Australia 18 871 1.0× 277 1.2× 186 1.0× 136 1.1× 95 1.0× 38 1.2k
Patryk A. Laurent United States 14 1.5k 1.7× 384 1.7× 120 0.6× 197 1.6× 134 1.4× 19 1.7k
David Luque Spain 15 557 0.6× 160 0.7× 128 0.7× 39 0.3× 87 0.9× 59 714
Evan J. Livesey Australia 18 860 1.0× 192 0.8× 404 2.2× 44 0.4× 130 1.4× 96 1.2k
Dobromir Rahnev United States 21 1.7k 1.9× 403 1.7× 126 0.7× 62 0.5× 192 2.0× 72 2.0k
Do-Joon Yi South Korea 15 1.1k 1.2× 214 0.9× 73 0.4× 45 0.4× 97 1.0× 28 1.3k
David N. George United Kingdom 15 587 0.7× 146 0.6× 192 1.0× 101 0.8× 141 1.5× 48 859
Michael J. Frank United States 4 513 0.6× 184 0.8× 59 0.3× 34 0.3× 88 0.9× 5 761
Holger Hecht Germany 21 1.1k 1.2× 442 1.9× 49 0.3× 87 0.7× 185 1.9× 28 1.5k

Countries citing papers authored by Tom Beesley

Since Specialization
Citations

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

Fields of papers citing papers by Tom Beesley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tom Beesley

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Beesley. A scholar is included among the top collaborators of Tom Beesley 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 Tom Beesley. Tom Beesley 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.
Beesley, Tom, et al.. (2022). Examining the role of depth information in contextual cuing using a virtual reality visual search task.. Journal of Experimental Psychology Human Perception & Performance. 48(12). 1313–1324.
2.
Luque, David, et al.. (2021). Contextual cuing of visual search does not guide attention automatically in the presence of top-down goals.. Journal of Experimental Psychology Human Perception & Performance. 47(8). 1080–1090. 3 indexed citations
3.
Navarro, Danielle, et al.. (2021). Protection from uncertainty in the exploration/exploitation trade-off.. Journal of Experimental Psychology Learning Memory and Cognition. 48(4). 547–568. 5 indexed citations
4.
Vadillo, Miguel A., et al.. (2020). There is more to contextual cuing than meets the eye: Improving visual search without attentional guidance toward predictable target locations.. Journal of Experimental Psychology Human Perception & Performance. 47(1). 116–120. 14 indexed citations
5.
Wills, Andy J., et al.. (2020). A dimensional summation account of polymorphous category learning. Learning & Behavior. 48(1). 66–83. 4 indexed citations
6.
7.
Beesley, Tom, et al.. (2019). Learned predictiveness models predict opposite attention biases in the inverse base-rate effect.. Journal of Experimental Psychology Animal Learning and Cognition. 45(2). 143–162. 6 indexed citations
8.
Beesley, Tom, et al.. (2018). Overt attention in contextual cuing of visual search is driven by the attentional set, but not by the predictiveness of distractors.. Journal of Experimental Psychology Learning Memory and Cognition. 44(5). 707–721. 13 indexed citations
9.
Griffiths, Oren, et al.. (2018). Outcome predictability biases cued search.. Journal of Experimental Psychology Learning Memory and Cognition. 44(8). 1215–1223. 7 indexed citations
10.
Pelley, Mike E. Le, et al.. (2017). Exploitative and Exploratory Attention in a Four-Armed Bandit Task.. Cognitive Science. 2 indexed citations
11.
Luque, David, Tom Beesley, Richard W. Morris, et al.. (2017). Goal-Directed and Habit-Like Modulations of Stimulus Processing during Reinforcement Learning. Journal of Neuroscience. 37(11). 3009–3017. 36 indexed citations
12.
Kennedy, Briana L., Daniel Pearson, David Sutton, Tom Beesley, & Steven B. Most. (2017). Spatiotemporal competition and task-relevance shape the spatial distribution of emotional interference during rapid visual processing: Evidence from gaze-contingent eye-tracking. Attention Perception & Psychophysics. 80(2). 426–438. 13 indexed citations
13.
Mifsud, Nathan, Tom Beesley, Tamara Watson, & Thomas J. Whitford. (2016). Attenuation of auditory evoked potentials for hand and eye-initiated sounds. Biological Psychology. 120. 61–68. 13 indexed citations
14.
Pelley, Mike E. Le, Chris J. Mitchell, Tom Beesley, David N. George, & Andy J. Wills. (2016). Attention and associative learning in humans: An integrative review.. Psychological Bulletin. 142(10). 1111–1140. 218 indexed citations
15.
Beesley, Tom, Daniel Pearson, & Mike E. Le Pelley. (2014). Implicit learning of gaze-contingent events. Psychonomic Bulletin & Review. 22(3). 800–807. 2 indexed citations
16.
Vadillo, Miguel A., Chris Street, Tom Beesley, & David R. Shanks. (2014). A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation. Behavior Research Methods. 47(4). 1365–1376. 20 indexed citations
17.
Beesley, Tom, Fergal W. Jones, & David R. Shanks. (2011). Out of control: An associative account of congruency effects in sequence learning. Consciousness and Cognition. 21(1). 413–421. 3 indexed citations
18.
Pelley, Mike E. Le, Tom Beesley, & Oren Griffiths. (2011). Overt attention and predictiveness in human contingency learning.. Journal of Experimental Psychology Animal Behavior Processes. 37(2). 220–229. 71 indexed citations
19.
Pelley, Mike E. Le, et al.. (2010). Stereotype formation: Biased by association.. Journal of Experimental Psychology General. 139(1). 138–161. 48 indexed citations
20.
Pelley, Mike E. Le, et al.. (2009). Learned predictiveness effects in humans: A function of learning, performance, or both?. Journal of Experimental Psychology Animal Behavior Processes. 35(3). 312–327. 22 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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