What mechanisms underlie learning in newborn brains? Recently, researchers reported that newborn chicks use unsupervised statistical learning to encode the transitional probabilities (TPs) of shapes in a sequence, suggesting that TP-based statistical learning can be present in newborn brains. Using a preregistered design, we attempted to reproduce this finding with an automated method that eliminated experimenter bias and allowed more than 250 times more data to be collected per chick. With precise measurements of each chick’s behavior, we were able to perform individual-level analyses and substantially reduce measurement error for the group-level analyses. We found no evidence that newborn chicks encode the TPs between sequentially presented shapes. None of the chicks showed evidence for this ability. Conversely, we obtained strong evidence that newborn chicks encode the shapes of individual objects, showing that this automated method can produce robust results. These findings challenge the claim that TP-based statistical learning is present in newborn brains.

中文翻译：

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自动化研究挑战新生雏鸡基础统计学习能力的存在

新生儿大脑的学习机制是什么？最近，研究人员报告说，新生小鸡使用无监督统计学习来编码序列中形状的转移概率（TP），这表明基于 TP 的统计学习可以存在于新生儿大脑中。使用预先注册的设计，我们尝试通过自动化方法重现这一发现，该方法消除了实验者偏差，并允许每只雏鸡收集超过 250 倍的数据。通过对每只雏鸡行为的精确测量，我们能够进行个体层面的分析，并大大减少群体层面分析的测量误差。我们没有发现任何证据表明新生小鸡对顺序呈现的形状之间的 TP 进行编码。没有一只小鸡表现出这种能力的证据。相反，我们获得了强有力的证据，表明新生小鸡对单个物体的形状进行编码，这表明这种自动化方法可以产生可靠的结果。这些发现挑战了新生儿大脑中存在基于 TP 的统计学习的说法。