Efficient communication requires balancing informativity and simplicity when encoding meanings. The Information Bottleneck (IB) framework captures this trade-off formally, predicting that natural language systems cluster near an optimal accuracy-complexity frontier. While supported in visual domains such as colour and motion, linguistic stimuli such as words in sentential context remain unexplored. We address this gap by framing translation as an IB optimisation problem, treating source sentences as stimuli and target sentences as compressed meanings. This allows IB analyses to be performed directly on bitexts rather than controlled naming experiments. We applied this to spatial prepositions across English, German and Serbian translations of a French novel. To estimate informativity, we conducted a pile-sorting pilot-study (N=35) and obtained similarity judgements of pairs of prepositions. We trained a low-rank projection model (D=5) that predicts these judgements (Spearman correlation: 0.78). Attested translations of prepositions lie closer to the IB optimal frontier than counterfactual alternatives, offering preliminary evidence that human translators exhibit communicative efficiency pressure in the spatial domain. More broadly, this work suggests that translation can serve as a window into the cognitive efficiency pressures shaping cross-linguistic semantic systems.

翻译：高效沟通要求在对含义编码时平衡信息性与简洁性。信息瓶颈（IB）框架形式化描述了这一权衡，预测自然语言系统趋近于最优精度-复杂度前沿。尽管该理论在颜色、运动等视觉域已获验证，但在句子语境中的词汇等语言刺激领域仍待探索。为填补这一空白，我们将翻译视作IB优化问题：将源语句作为刺激输入，目标语句视为压缩后的含义表达。这使得IB分析可直接应用于双语语料，而非受控命名实验。我们选取法语小说的英德塞三语译本，对空间介词展开分析。为估计信息性，我们开展了一项分拣试点研究（N=35），获取成对介词的相似性判断，并训练低秩投影模型（D=5）预测这些判断（斯皮尔曼相关系数：0.78）。相较于反事实替代选项，实际翻译中的介词更接近IB最优前沿，初步表明人类译者在空间域展现出交际效率压力。更广泛而言，本研究揭示翻译可成为窥探塑造跨语言语义系统的认知效率压力的窗口。