Navarro Danielle J
School of Psychology, University of New South Wales.
Perspect Psychol Sci. 2021 Jul;16(4):707-716. doi: 10.1177/1745691620974769. Epub 2021 Feb 16.
It is commonplace, when discussing the subject of psychological theory, to write articles from the assumption that psychology differs from the physical sciences in that we have no theories that would support cumulative, incremental science. In this brief article I discuss one counterexample: Shepard's law of generalization and the various Bayesian extensions that it inspired over the past 3 decades. Using Shepard's law as a running example, I argue that psychological theory building is not a statistical problem, mathematical formalism is beneficial to theory, measurement and theory have a complex relationship, rewriting old theory can yield new insights, and theory growth can drive empirical work. Although I generally suggest that the tools of mathematical psychology are valuable to psychological theorists, I also comment on some limitations to this approach.
在讨论心理学理论这一主题时，撰写文章时通常会基于这样一种假设：心理学与物理科学不同，因为我们没有能够支持累积性、渐进性科学的理论。在这篇简短的文章中，我将讨论一个反例：谢泼德泛化定律以及在过去三十年中它所引发的各种贝叶斯扩展理论。以谢泼德定律为例，我认为心理学理论构建并非一个统计问题，数学形式主义对理论有益，测量与理论有着复杂的关系，重写旧理论能产生新见解，且理论发展能够推动实证研究。虽然我总体上认为数学心理学工具对心理学理论家很有价值，但我也会评论这种方法的一些局限性。
