跨情境词汇学习情景中的批评行为
Critical behavior in a cross-situational lexicon learning scenario
摘要：
联想主义者对早期词汇学习的解释是基于物体和词汇之间的共现。在这里，我们研究了一个简单的联想学习算法的性能，用于在跨情境的情况下，由上下文外的单词产生的噪声的影响下获得的单词的参考。我们发现一个临界值的噪声参数$γ_c$以上的学习是不可能的。我们使用有限尺寸标度表明，过渡的锐度持续通过区域的顺序$τ^{-1/2}$约$γ_c$，其中$τ$是学习试验的数量，以及获得学习误差（缩放函数）在临界区域。此外，我们还证明了当学习误差为零时，周期的持续时间在临界点处服从指数为-3/2的幂律分布。
英文摘要：
The associationist account for early word-learning is based on the co-occurrence between objects and words. Here we examine the performance of a simple associative learning algorithm for acquiring the referents of words in a cross-situational scenario affected by noise produced by out-of-context words. We find a critical value of the noise parameter $γ_c$ above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order $τ^{-1/2}$ about $γ_c$, where $τ$ is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point.
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