Learning often requires splitting continuous signals into recurring units, such as the discrete words constituting fluent speech; these units then need to be encoded in memory. A prominent candidate mechanism involves statistical learning of co-occurrence statistics like transitional probabilities (TPs), reflecting the idea that items from the same unit (e.g., syllables within a word) predict each other better than items from different units. TP computations are surprisingly flexible and sophisticated. Humans are sensitive to forward and backward TPs, compute TPs between adjacent items and longer-distance items, and even recognize TPs in novel units. We explain these hallmarks of statistical learning with a simple model with tunable, Hebbian excitatory connections and inhibitory interactions controlling the overall activation. With weak forgetting, activations are long-lasting, yielding associations among all items; with strong forgetting, no associations ensue as activations do not outlast stimuli; with intermediate forgetting, the network reproduces the hallmarks above. Forgetting thus is a key determinant of these sophisticated learning abilities. Further, in line with earlier dissociations between statistical learning and memory encoding, our model reproduces the hallmarks of statistical learning in the absence of a memory store in which items could be placed.

学习往往需要将连续的信号分解成重复出现的单元，例如构成流利语音的离散词；然后需要在内存中对这些单元进行编码。一个突出的候选机制涉及对诸如过渡概率（TP）等共现统计的统计学习，反映了来自同一单元（例如，单词中的音节）的项目比来自不同单元的项目更好地相互预测的想法。TP 计算非常灵活和复杂。人类对前向和后向 TP 很敏感，计算相邻项目和更远距离项目之间的 TP，甚至可以识别新单位中的 TP。我们用一个简单的模型来解释统计学习的这些特征，该模型具有可调节的 Hebbian 兴奋性连接和控制整体激活的抑制性相互作用。以微弱的遗忘，激活是持久的，在所有项目之间产生关联；强烈的遗忘不会产生联想，因为激活不会超过刺激；通过中间遗忘，网络再现了上述特征。因此，遗忘是这些复杂学习能力的关键决定因素。此外，根据统计学习和记忆编码之间的早期分离，我们的模型在没有可以放置项目的记忆库的情况下再现了统计学习的标志。