詞彙共識：人工代理中的接地詞彙學習與共享意義

摘要

人工智慧系統通常透過任務表現與行為模仿進行評估，但此類評估無法確定人工代理是否能從接地經驗中獲取、穩定化及運用新的詞彙意義。本文提出「詞彙共識」實驗框架，旨在研究在結構化感知基礎上的接地詞彙學習。透過凍結的DINOv2視覺嵌入、卡羅式假詞、可解釋詞彙學習器及線性基線模型，我們測試代理是否能習得視覺概念的人工標籤、雙向推廣這些標籤，並在受控環境中將其穩定化。主要結果呈現出穩健的感知連貫性梯度：自然類別最易學習，連貫性過度延伸仍可學習，中範圍析取概念表現下降，而遠距析取概念則趨近隨機水準。一項預先註冊的CIFAR-100分離實驗證實，此梯度受感知距離而非語義關聯性所支配：感知距離預測習得準確度（偏R² = 0.245，p < 1e-7），而語義距離無顯著解釋力（偏R² = 0.002，p = 0.660）。雙向評估顯示命名與檢索為不同歷程：在標籤到圖像的檢索中，基於範例的機制優於質心原型，揭示出與命名準確性無關的記憶忠實度面向。偽證控制、同質候選池評估，以及表徵重構的無效結果，均表明凍結的感知幾何結構既能促成詞彙接地，同時也限制了在無表徵調適下所能習得的內容。

English

Artificial intelligence systems are commonly evaluated through task performance and behavioral imitation, but such evaluations leave open whether an artificial agent can acquire, stabilize, and use new lexical meanings from grounded experience. This paper introduces Lexical Consensus, an experimental framework for studying grounded word learning over a structured perceptual substrate. Using frozen DINOv2 visual embeddings, Carroll-style nonce words, and interpretable lexical learners plus linear baselines, we test whether agents can acquire artificial labels for visual concepts, generalize them bidirectionally, and stabilize them across controlled settings. The main result is a robust perceptual-coherence gradient: native categories are easiest to learn, coherent overextensions remain learnable, mid-range disjunctive concepts degrade, and far-disjunctive concepts approach chance. A pre-registered CIFAR-100 dissociation experiment confirms that this gradient is governed by perceptual distance rather than semantic relatedness: perceptual distance predicts acquisition accuracy (partial R^2 = 0.245, p < 1e-7), while semantic distance adds no significant explanatory power (partial R^2 = 0.002, p = 0.660). Bidirectional evaluation shows that naming and retrieval are distinct: exemplar-based mechanisms outperform centroid prototypes in label-to-image retrieval, exposing a memory-fidelity dimension separate from naming accuracy. Falsification controls, homogeneous candidate-pool evaluations, and null results on representational restructuring indicate that frozen perceptual geometry both enables lexical grounding and limits what can be acquired without representational adaptation.