ABSTRACT

Theories of language and cognition develop iteratively from ideas, experiments and models. The abstract nature of “cognitive processes” means that computational models play a critical role in this, yet bridging the gaps between models, data, and interpretations is challenging. While the how and why computations are performed is often the primary research focus, the conclusions drawn from models can be compromised by the representations chosen for them. To illustrate this point, we revisit a set of empirical studies of language acquisition that appear to support different models of learning from implicit negative evidence. We examine the degree to which these conclusions were influenced by the representations chosen and show how a plausible single mechanism account of the data can be formulated for representations that faithfully capture the task design. The need for input representations to be incorporated into model conceptualisations, evaluations, and comparisons is discussed.

摘要

语言和认知理论从想法、实验和模型中迭代发展。“认知过程”的抽象性质意味着计算模型在其中起着关键作用，但弥合模型、数据和解释之间的差距具有挑战性。虽然如何以及为什么执行的计算通常是主要的研究重点，从模型中得出的结论可能会因为它们选择的表示而受到影响。为了说明这一点，我们重新审视了一组语言习得的实证研究，这些研究似乎支持从隐性负面证据中学习的不同模型。我们检查了这些结论在多大程度上受到所选表示的影响，并展示了如何为忠实捕捉任务设计的表示制定合理的单一数据机制说明。讨论了将输入表示纳入模型概念化、评估和比较的必要性。