深度daxes：相互排他性是通过神经网络中的学习偏见和务实策略产生的
Deep daxes: Mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
摘要：
儿童将新奇的词语与新奇的指称物联系起来的倾向被认为反映了一种相互排斥的偏见。这种倾向可能是有利的，因为（1）一个特设的所指选择启发式挑选出缺乏标签的所指和（2）词汇习得的组织原则。本文研究了在什么情况下跨情境神经模型可以表现出类似的行为，以儿童，重点是这两种可能性和它们的相互作用。为此，我们评估神经网络的符号数据，并作为第一，大规模的图像数据。我们发现，在学习和选择的限制可以促进相互排斥，只要他们把词在词汇意义的竞争。对于计算模型，这些研究结果澄清的作用，可用的选项，以更好地执行任务，互斥是有利的。对于认知研究，他们强调了词汇学习，指称选择机制和不同复杂性的刺激结构（符号和视觉）之间的潜在相互作用。
英文摘要：
Children's tendency to associate novel words with novel referents has been taken to reflect a bias toward mutual exclusivity. This tendency may be advantageous both as (1) an ad-hoc referent selection heuristic to single out referents lacking a label and as (2) an organizing principle of lexical acquisition. This paper investigates under which circumstances cross-situational neural models can come to exhibit analogous behavior to children, focusing on these two possibilities and their interaction. To this end, we evaluate neural networks' on both symbolic data and, as a first, on large-scale image data. We find that constraints in both learning and selection can foster mutual exclusivity, as long as they put words in competition for lexical meaning. For computational models, these findings clarify the role of available options for better performance in tasks where mutual exclusivity is advantageous. For cognitive research, they highlight latent interactions between word learning, referent selection mechanisms, and the structure of stimuli of varying complexity: symbolic and visual.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
