Kazuyuki Samejima
- Cognitive Neuroscience top 1%
- Cellular and Molecular Neuroscience top 5%
- Artificial Intelligence top 5%
- Experimental and Cognitive Psychology top 5%
- Social Psychology top 5%
- Co-authors
- Kenji DoyaMinoru KimuraYasumasa UedaMitsuo KawatoKeisuke ToyamaMasahiko HarunoHiroshi ImamizuKou Murayama
- Topics
- Neural dynamics and brain function (12 papers)Neural and Behavioral Psychology Studies (12 papers)Reinforcement Learning in Robotics (6 papers)
- Partner nations
- JapanUnited StatesBelgium
In The Last Decade
Kazuyuki Samejima
33 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Cognitive Neuroscience 1.4k
- Cellular and Molecular Neuroscience 563
- Artificial Intelligence 248
- Experimental and Cognitive Psychology 218
- Social Psychology 205
Countries citing papers authored by Kazuyuki Samejima
This map shows the geographic impact of Kazuyuki Samejima'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 Kazuyuki Samejima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kazuyuki Samejima more than expected).
Fields of papers citing papers by Kazuyuki Samejima
This network shows the impact of papers produced by Kazuyuki Samejima. 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 Kazuyuki Samejima. The network helps show where Kazuyuki Samejima may publish in the future.
Co-authorship network of co-authors of Kazuyuki Samejima
This figure shows the co-authorship network connecting the top 25 collaborators of Kazuyuki Samejima. A scholar is included among the top collaborators of Kazuyuki Samejima 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 Kazuyuki Samejima. Kazuyuki Samejima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 116 | |
| 2 | 11 | |
| 3 | 25 | |
| 4 | 5 | |
| 5 | 83 | |
| 6 | 12 | |
| 7 | 30 | |
| 8 | 6 | |
| 9 | 0 | |
| 10 | 59 | |
| 11 | 73 | |
| 12 | 58 | |
| 13 | 4 | |
| 14 | Representation of Action-Specific Reward Values in the Striatumbreakdown → | 669 |
| 15 | 237 | |
| 16 | 40 | |
| 17 | 13 | |
| 18 | 5 | |
| 19 | 32 | |
| 20 | Adaptive State Space Formation in Reinforcement Learning. | 3 |
About Kazuyuki Samejima
Kazuyuki Samejima is a scholar working on Cognitive Neuroscience, Equine and General Decision Sciences, having authored 36 papers that have together received 2.1k indexed citations. Recurring topics across this work include Neural dynamics and brain function (12 papers), Neural and Behavioral Psychology Studies (12 papers) and Reinforcement Learning in Robotics (6 papers). The work is most often cited by research in General Decision Sciences (136 citations), Cognitive Neuroscience (1.4k citations) and Cellular and Molecular Neuroscience (563 citations). Kazuyuki Samejima has collaborated with scholars based in Japan, United States and Belgium. Frequent co-authors include Kenji Doya, Minoru Kimura, Yasumasa Ueda, Mitsuo Kawato, Keisuke Toyama, Masahiko Haruno, Hiroshi Imamizu, Kou Murayama, Madoka Matsumoto and Keise Izuma. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Neuron.
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.