Core Metrics
Total Citations
20,281
H-Index
71
Publications
438
i10-Index
206
2-Year Citedness
5.6
avg citations per work
Ability Dimensions
20,281 citations, h=71
2yr mean: 5.6
438 papers (24.3/year)
46 cites/paper
2 unique research topics
4 topic areas
Top 3 papers: 17% of citations
* Percentile scores are calculated relative to all scholars in the computational neuroscience dataset. Tags are assigned based on dimension combinations. Hover over the radar chart for details.
Scholar Profile Analysis
Samuel J. Gershman is a distinguished scholar with 20k+ citations in computational neuroscience, currently affiliated with Harvard University.
Over a 18-year academic career, published 438 papers (averaging 24.3 per year), with 20,281 citations.
An h-index of 71, well above field average, indicates a substantial body of highly-cited work.
Primary research areas include Public economics, Competence (human resources), Competence (human resources).
Key Findings
Signature Work
"Model-Based Influences on Humans' Choices and Striatal Prediction Errors" is the most influential work, with 1,868 citations, published in 2011.
Early Career Analysis (First 5 Years)
Career Start
2007 - 2011
Early Citations
3,746
Early Works
16
Early Impact %
18.5%
Top Early Career Paper
Model-Based Influences on Humans' Choices and Striatal Prediction Errors
Publication Timeline
Research Topics
Top Publications
Model-Based Influences on Humans' Choices and Striatal Prediction Errors
1,868
Citations
The hippocampus as a predictive map
973
Citations
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
678
Citations
A tutorial on Bayesian nonparametric models
592
Citations
Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework
478
Citations
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms
396
Citations
Context, learning, and extinction.
394
Citations
The successor representation in human reinforcement learning
387
Citations
The Curse of Planning
371
Citations
Predictive representations can link model-based reinforcement learning to model-free mechanisms
338
Citations
Impact Classification
高影响力
总引用超过2万次，是领域内公认的重要人物
稳定产出
h-index超过50，具有持续的学术产出能力