Gatti Daniele, Petilli Marco, Marchetti Michela, Vecchi Tomaso, Mazzoni Giuliana, Rinaldi Luca, Marelli Marco
Department of Brain and Behavioural Science, University of Pavia, Piazza Botta 6, 27100, Pavia, Italy.
Department of Psychology, University of Milano-Bicocca, Milan, Italy.
Psychon Bull Rev. 2025 Mar 14. doi: 10.3758/s13423-025-02677-7.
Semantic knowledge plays an active role in many well-known false memory phenomena, including those emerging from the Deese-Roediger-McDermott (DRM) task. Indeed, in this experimental paradigm, humans tend to falsely recognize newly presented words via activation of other previously shown stimuli. In the present study we aimed to test what happens in cases in which no apparent prior semantic knowledge is available, like in the case of entirely novel lexical stimuli. To do so, we evaluated semantic similarity effects in a DRM task with lists entirely composed by pseudowords (or "novel words," i.e., letter strings resembling real words but lacking assigned meanings). Semantic similarity between pseudowords were established through a distributional semantic model able to represent in a vector space, not only attested words but also unmapped strings as bags of character n-grams. Participants were instructed to memorize those lists and then to perform a recognition task. Results showed that participants false and veridical recognition increased with increasing semantic similarity between each stimulus and the stimuli comprising its list, as estimated by the distributional model. These findings extend previous evidence indicating that humans are sensitive to the semantic (distributional) patterns elicited by novel words by showing that this sensitivity can even induce humans to falsely recognize stimuli that they have never encountered in their entire lives.
语义知识在许多著名的错误记忆现象中发挥着积极作用，包括那些源自迪斯-罗迪格-麦克德莫特（DRM）任务的现象。事实上，在这种实验范式中，人类往往会通过激活其他先前呈现的刺激来错误地识别新呈现的单词。在本研究中，我们旨在测试在没有明显先验语义知识的情况下会发生什么，比如在完全新颖的词汇刺激的情况下。为此，我们在一个DRM任务中评估了语义相似性效应，该任务的列表完全由伪词（或“新单词”，即类似于真实单词但没有指定含义的字母串）组成。伪词之间的语义相似性是通过一种分布语义模型建立的，该模型能够在向量空间中表示，不仅包括已证实的单词，还包括未映射的字符串，作为字符n元组的集合。参与者被指示记住这些列表，然后执行识别任务。结果表明，根据分布模型估计，随着每个刺激与其列表中包含的刺激之间语义相似性的增加，参与者的错误识别和真实识别都会增加。这些发现扩展了先前的证据，表明人类对新单词引发的语义（分布）模式敏感，表明这种敏感性甚至可以诱使人类错误地识别他们一生中从未遇到过的刺激。
