• Publications
  • Influence
Future impact: Predicting scientific success
Daniel E. Acuna, Stefano Allesina and Konrad P. Kording present a formula to estimate the future h-index of life scientists.
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A high-reproducibility and high-accuracy method for automated topic classification
TLDR
We propose a new algorithm which displays high-reproducibility and high-accuracy, and also has high computational efficiency. Expand
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Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications
TLDR
We develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Expand
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Chunking as the result of an efficiency computation trade-off
TLDR
How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Expand
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Bayesian modeling of human sequential decision-making on the multi-armed bandit problem
TLDR
We investigate human exploration/exploitation behavior in sequential-decision making tasks and show that Bayesian models of human behavior for the Multi-Armed Bandit Problem on experimental data perform better than previous accounts. Expand
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Intellectual synthesis in mentorship determines success in academic careers
As academic careers become more competitive, junior scientists need to understand the value that mentorship brings to their success in academia. Previous research has found that, unsurprisingly,Expand
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Using psychophysics to ask if the brain samples or maximizes.
The two-alternative forced-choice (2AFC) task is the workhorse of psychophysics and is used to measure the just-noticeable difference, generally assumed to accurately quantify sensory precision.Expand
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Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test
TLDR
In the Turing test a computer model is deemed to “think intelligently” if it can generate answers that are indistinguishable from those of a human with varying levels of noise. Expand
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Structure Learning in Human Sequential Decision-Making
TLDR
We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. Expand
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The sociology of scientific validity: How professional networks shape judgement in peer review
Professional connections between the creators and evaluators of scientific work are ubiquitous, and the possibility of bias ever-present. Although connections have been shown to bias predictions ofExpand
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