Poth Nina
Department of Philosophy, Berlin School of Mind & Brain, Humboldt University Berlin, 10099 Berlin, Germany.
Research Cluster of Excellence, Science of Intelligence, 10587 Berlin, Germany.
Entropy (Basel). 2023 Sep 30;25(10):1407. doi: 10.3390/e25101407.
The notions of psychological similarity and probabilistic learning are key posits in cognitive, computational, and developmental psychology and in machine learning. However, their explanatory relationship is rarely made explicit within and across these research fields. This opinionated review critically evaluates how these notions can mutually inform each other within computational cognitive science. Using probabilistic models of concept learning as a case study, I argue that two notions of psychological similarity offer important normative constraints to guide modelers' interpretations of representational primitives. In particular, the two notions furnish probabilistic models of cognition with meaningful interpretations of what the associated subjective probabilities in the model represent and how they attach to experiences from which the agent learns. Similarity representations thereby provide probabilistic models with cognitive, as opposed to purely mathematical, content.
心理相似性和概率学习的概念是认知心理学、计算心理学、发展心理学以及机器学习中的关键假设。然而，在这些研究领域内部和之间，它们的解释关系很少被明确阐述。这篇有倾向性的综述批判性地评估了这些概念如何在计算认知科学中相互提供信息。以概念学习的概率模型为例，我认为心理相似性的两个概念提供了重要的规范性约束，以指导建模者对表征原语的解释。特别是，这两个概念为认知概率模型提供了有意义的解释，说明模型中相关主观概率代表了什么，以及它们如何与主体学习的经验相关联。因此，相似性表征为概率模型提供了认知内容，而非仅仅是数学内容。
