Researchers have long interpreted the presence or absence of semantic interference in picture naming latencies as confirming or refuting theoretical claims regarding competitive lexical selection. But inconsistent empirical results challenge any mechanistic interpretation. A behavioral experiment first verified an apparent boundary condition in a blocked picture naming task: when orthogonally manipulating association type, taxonomic associations consistently elicit interference, while thematic associations do not. A plausible representational difference is that thematic feature activations depend more on supporting contexts. Simulations show that context-sensitivity emerges from the distributional statistics that are often used to measure thematic associations: residual semantic activation facilitates the retrieval of words that share semantic features, counteracting learning-based interference, and training a production model with greater sequential cooccurrence for thematically related words causes it to acquire stronger residual activation for thematic features. Modulating residual activation, either directly or through training, allows the model to capture gradient values of interference and facilitation, and in every simulation competitive and noncompetitive selection algorithms produce qualitatively equivalent results.

长期以来，研究人员一直将图片命名延迟中语义干扰的存在或不存在解释为证实或反驳有关竞争性词汇选择的理论主张。但不一致的实证结果对任何机械解释提出了挑战。行为实验首先验证了块图片命名任务中的明显边界条件：当正交操纵关联类型时，分类关联始终会引起干扰，而主题关联则不会。一个可能的代表性差异是主题特征激活更多地依赖于支持上下文。模拟表明，上下文敏感性源于通常用于测量主题关联的分布统计：残余语义激活有助于检索共享语义特征的单词，抵消基于学习的干扰，并训练具有更大主题顺序共现性的生产模型相关词使其对主题特征获得更强的残余激活。直接或通过训练调节残差激活，使模型能够捕获干扰和促进的梯度值，并且在每次模拟中，竞争性和非竞争性选择算法都会产生质量相同的结果。