Disclosing the interactive mechanism behind scientists' topic selection behavior from the perspective of the productivity and the impact

Shengzhi Huang, Yong Huang, Yi Bu, Zhuoran Luo, Wei Lu

Abstract: Highlights•This paper proposes two correlation metrics, by which we verify that the productivity and the impact is related to the evolution of scientists’ research interests. This study may help researchers deeply understand scientists’ topic selection process.•This paper proposes a novel Q seashore walk model, which effectively imitates the interaction process between scientists and the environment. Our model contributes to the theoretical advancement of applying the reinforcement learning theory to analyze scientists’ behavior.•This paper shows that proper rewards effectively stimulate the performance of scientists, but excessive rewards inhibit their performance. This study may provide theoretical evidence for policy intervention in scientific research.