Raj Rohan, Hörberg Thomas, Lindroos Robert, Larsson Maria, Herman Pawel, Laukka Erika J, Olofsson Jonas K
Gösta Ekman Laboratory, Department of Psychology, Stockholm University, Stockholm, Sweden.
Computational Brain Science Lab, Division of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Cognition. 2023 Jul;236:105445. doi: 10.1016/j.cognition.2023.105445. Epub 2023 Apr 5.
Human olfaction can be extraordinarily sensitive, and its most common assessment method is odor identification (OID), where everyday odors are matched to word labels in a multiple-choice format. However, many older persons are unable to identify familiar odors, a deficit that is associated with the risk of future dementia and mortality. The underlying processes subserving OID in older adults are poorly understood. Here, we analyzed error patterns in OID to test whether errors could be explained by perceptual and/or semantic similarities among the response alternatives. We investigated the OID response patterns in a large, population-based sample of older adults in Sweden (n = 2479; age 60-100 years). Olfaction was assessed by a 'Sniffin ́ TOM OID test with 16 odors; each trial involved matching a target odor to a correct label among three distractors. We analyzed the pattern of misidentifications, and the results showed that some distractors were more frequently selected than others, suggesting cognitive or perceptual factors may be present. Relatedly, we conducted a large online survey of older adults (n = 959, age 60-90 years) who were asked to imagine and rate the perceptual similarity of the target odors and the three corresponding distractors (e.g. "How similar are these smells: apple and mint?"). We then used data from the Swedish web corpus and the Word2Vec neural network algorithm to quantify the semantic association strength between the labels of each target odor and its three distractors. These data sources were used to predict odor identification errors. We found that the error patterns were partly explained by both the semantic similarity between target-distractor pairs, and the imagined perceptual similarity of the target-distractor pair. Both factors had, however, a diminished prediction in older ages, as responses became gradually less systematic. In sum, our results suggest that OID tests not only reflect olfactory perception, but also likely involve the mental processing of odor-semantic associations. This may be the reason why these tests are useful in predicting dementia onset. Our insights into olfactory-language interactions could be harnessed to develop new olfactory tests that are tailored for specific clinical purposes.
人类嗅觉可能极其灵敏,其最常见的评估方法是气味识别(OID),即让日常气味与多项选择题形式的文字标签进行匹配。然而,许多老年人无法识别熟悉的气味,这种缺陷与未来患痴呆症和死亡的风险相关。目前对老年人中支持气味识别的潜在过程了解甚少。在这里,我们分析了气味识别中的错误模式,以测试这些错误是否可以通过反应选项之间的感知和/或语义相似性来解释。我们在瑞典一个基于人群的大型老年样本(n = 2479;年龄60 - 100岁)中研究了气味识别反应模式。通过“嗅探TOM气味识别测试”评估嗅觉,该测试有16种气味;每次试验都涉及在三个干扰项中将目标气味与正确标签进行匹配。我们分析了错误识别模式,结果表明某些干扰项比其他干扰项被更频繁地选择,这表明可能存在认知或感知因素。相关地,我们对老年人(n = 959,年龄60 - 90岁)进行了一项大型在线调查,要求他们想象并评价目标气味与三个相应干扰项的感知相似性(例如,“苹果和薄荷这两种气味有多相似?”)。然后,我们使用来自瑞典网络语料库的数据和Word2Vec神经网络算法来量化每个目标气味的标签与其三个干扰项之间的语义关联强度。这些数据源被用于预测气味识别错误。我们发现错误模式部分是由目标 - 干扰项对之间的语义相似性以及目标 - 干扰项对的想象感知相似性所解释的。然而,随着年龄增长,这两个因素的预测能力都有所下降,因为反应逐渐变得不那么系统。总之,我们的结果表明气味识别测试不仅反映嗅觉感知,还可能涉及气味 - 语义关联的心理处理。这可能就是为什么这些测试在预测痴呆症发病方面有用的原因。我们对嗅觉 - 语言相互作用的见解可用于开发针对特定临床目的定制的新嗅觉测试。