Extensive research in the behavioral sciences has addressed people's ability to learn stationary probabilities, which stay constant over time, but only recently have there been attempts to model the cognitive processes whereby people learn-and track-nonstationary probabilities. In this context, the old debate on whether learning occurs by the gradual formation of associations or by occasional shifts between hypotheses representing beliefs about distal states of the world has resurfaced. Gallistel et al. (2014) pitched the two theories against each other in a nonstationary probability learning task. They concluded that various qualitative patterns in their data were incompatible with trial-by-trial associative learning and could only be explained by a hypothesis-testing model. Here, we contest that claim and demonstrate that it was premature. First, we argue that their experimental paradigm consisted of two distinct tasks: probability tracking (an estimation task) and change detection (a decision-making task). Next, we present a model that uses the (associative) delta learning rule for the probability tracking task and bounded evidence accumulation for the change detection task. We find that this combination of two highly established theories accounts well for all qualitative phenomena and outperforms the alternative model proposed by Gallistel et al. (2014) in a quantitative model comparison. In the spirit of cumulative science, we conclude that current experimental data on human learning of nonstationary probabilities can be explained as a combination of associative learning and bounded evidence accumulation and does not require a new model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

---

对概率的进一步看法：捍卫关联模型。

行为科学领域的广泛研究已经解决了人们学习平稳概率的能力，这种概率随着时间的推移保持不变，但直到最近才尝试对人们学习和跟踪非平稳概率的认知过程进行建模。在这种背景下，关于学习是通过逐渐形成关联还是通过代表世界遥远状态的信念之间的偶尔转变而发生的古老争论重新浮出水面。加利斯特等人。(2014) 在非平稳概率学习任务中将这两种理论相互对比。他们得出的结论是，数据中的各种定性模式与逐次试验的联想学习不相容，只能通过假设检验模型来解释。在这里，我们对这一说法提出异议，并证明它还为时过早。首先，我们认为他们的实验范式由两个不同的任务组成：概率跟踪（估计任务）和变化检测（决策任务）。接下来，我们提出一个模型，该模型使用（关联）增量学习规则进行概率跟踪任务，并使用有界证据积累进行变化检测任务。我们发现，两种高度成熟的理论的结合很好地解释了所有定性现象，并且优于 Gallistel 等人提出的替代模型。（2014）定量模型比较。本着累积科学的精神，我们得出的结论是，当前人类学习非平稳概率的实验数据可以解释为联想学习和有界证据积累的结合，不需要新的模型。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。