Jensen Greg, Terrace Herbert S, Ferrera Vincent P
Department of Psychology, Columbia University, New York, NY, United States.
Department of Neuroscience, Columbia University, New York, NY, United States.
Front Neurosci. 2019 Aug 20;13:878. doi: 10.3389/fnins.2019.00878. eCollection 2019.
Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free (-learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.
人类和动物可以学会对一系列物品进行排序，而无需依赖明确的空间或时间线索。为此，它们似乎利用了传递性，这是所有有序集合的一个属性。在这里，我们总结了关于传递性推理（TI）范式及其与学习任意一组物品的潜在顺序之间关系的相关研究。我们比较了六种TI表现的计算模型，其中三种是无模型的（-学习、价值转移和REMERGE），三种是基于模型的（RL-Elo、顺序蒙特卡罗和贝塔排序）。我们的目标是评估这些模型产生TI行为的实证观察特征的能力。基于模型的方法在更广泛的场景下表现更好，但没有一个单一模型能够解释TI文献中报道的全部行为范围。
