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Behavior

研究成人有无痴呆症或轻度认知障碍的文字识别的词汇决策任务

Published: June 25, 2019
doi:
10.3791/59753
, , ,
1Helsinki Collegium for Advanced Studies,University of Helsinki, 2University of California, Riverside, 3California State University, East Bay, 4Hunter College,City University of New York, 5Feinberg School of Medicine,Northwestern University, 6Shirley Ryan AbilityLab

Summary

本文介绍如何实施一个简单的词法决策实验,以评估神经健康参与者以及痴呆症和认知衰退患者的书面单词识别度。我们还使用主要成分分析 (PCA) 和混合效应建模提供了反应时间分析的详细说明。

Abstract

老年人比年轻人识别视觉物体的速度要慢。识别字母字符串是真实单词的情况也是如此。患有阿尔茨海默氏病(AD)或轻度认知障碍(MCI)的人,在书面单词识别中表现出比老年人对照组更长的反应。尽管在衰老和神经认知障碍中识别速度普遍较慢,但无论年龄或神经病理学如何,单词的某些特征都会影响单词识别速度(例如,单词的使用频率)。我们在这里提出了一个协议,用于检查词汇特征对单词识别响应时间的影响,在一个简单的词汇决策实验中,对年轻和老年人以及MCI或AD患者进行管理。在本实验中,要求参与者尽可能快速准确地决定给定的字母字符串是否是实际单词。我们还描述了混合效应模型和主要组件分析,可用于检测不同类型的词汇变量或参与者的个体特征对单词识别速度的影响。

Introduction

单词存储在高度互联的网络的心理词典中。单词之间的连接可能反映共享属性,如语义相似性(例如,狗和猫),形成相似性(狗和雾),或常见语言使用中频繁发生(例如,狗和猫)勒什)。语言的认知理论,如基于用法的理论1,认为语言使用者每次遇到一个词都会对单词的心理表现产生影响。根据 Exemplar 理论,单词的表示由许多范例组成,这些范例由语言使用的各个标记建立而成,表示给定类别存在的可变性。使用频率 2通过增强范例1的强度来影响内存中的表示。

词识别速度可以揭示心理词汇的特点。衡量单词识别速度的常用实验范式是词汇决策任务。在此任务中,在监视器上向参与者显示字母字符串,一次一个。他们被指示通过按下相应的按钮,尽快决定屏幕上的字母字符串是否是真字。

通过检查真实单词的反应时间,研究人员可以解决许多有关语言处理的重要问题。例如,确定哪些因素使识别速度更快,可以检验关于心理词典结构的假设,并揭示其结构。此外,对不同参与者群体的表现进行比较可以帮助我们理解各种语言体验的影响,或者,在衰老或神经退行性疾病(如阿尔茨海默氏病)的情况下,认知的作用下降。

某些因素(例如使用频率)对单词识别的影响大于其他因素(例如,单词长度)。随着年龄的增长,人们识别书面文字的方式可能会改变3,4。年轻人往往严重依赖一个词的语义(基于意义)方面,例如有多少化合物(例如,牛头犬)或派生词(例如,狗)与目标词(在本例中为狗)共享形式和含义的方面。老年人的单词识别似乎更受基于表单的方面的影响,例如两个后续字母在语言中共同出现的频率(例如,字母组合在英语单词中出现的频率比组合语更频繁)斯克)

为了确定影响不同组单词识别速度的因素,研究人员可以操纵刺激集中的某些变量,然后测试这些变量预测单词识别速度的能力。例如,为了测试单词识别是由语义因素还是基于表单的因素驱动,刺激集应包括反映单词与心理词典中语义邻居的连接程度或与其他单词的连接性的变量共享其部分形式。

该方法在目前的研究中用于研究单词识别速度是否受年轻人和老年人以及患有阿尔茨海默氏病(AD)或轻度认知障碍(MCI)3的人的不同因素的影响。此处描述的方法基于视觉单词识别,但可以适应听觉模式。但是,在典型的视觉词汇决策实验中,一些变量是反应时间的重要预测器,它们可能无法预测听觉词词决策中的响应延迟,或者可能具有相反的效果。例如,语音邻域在这两种模式中具有相反的效果 5:具有较大语音邻域的单词对视觉单词识别具有促进作用,但会导致在听觉词汇决定6。

老年人的寻字困难通常归因于难以访问语音词的形式,而不是语义表示8的细目。然而,AD研究主要集中于语义衰退9,10,11,12,13,14。重要的是解开语义和正交因素如何影响在衰老中识别文字,有认知衰退,没有认知衰退。与形式相关的因素的影响在老年人中比在年轻人中更为明显,在MCI或AD3患者中仍然显著。因此,这种方法可以帮助我们发现不同人群的精神词典的特征,并识别词典组织随年龄和神经病理学而变化。在测试神经病理学患者时,一个顾虑是他们可能难以获得与任务相关的知识。然而,词汇决策任务是一个简单的任务,没有负担的工作记忆或其他复杂的认知技能,许多患者表现出问题。它被认为适合AD和MCI人群。

Protocol

该议定书遵循北萨沃医院区道德委员会(IRB00006251)的指导方针。 1. 参与者筛选 招募视力正常或矫正、以语言为母语的年轻和老年人,除非该研究涉及有关第二语言习得的特定研究问题。 对于健康对照组,不包括有神经或精神疾病史的参与者。 对于临床组,招募被诊断患有阿尔茨海默氏症15或轻度认知障碍16,17的个人。<…

Representative Results

表 1显示了从三个不同的源(语料库、字典和测试项的试验测试)获取的变量的列表,这些变量作为固定效应预测变量包含在分析中。这些变量中有许多以前报告会影响单词识别速度。 语料库： 基本频率 单词以各种形式出现在语料库中的次数(例如,儿童和儿…

Discussion

本研究使用不需要语言制作的简单语言任务,调查了各种词汇变量对神经健康的年轻人和老年人以及阿尔茨海默氏症患者单词识别的影响。或轻度认知障碍。招募”老年人”所用的年龄范围可能取决于具体的研究兴趣;然而,健康老年人群体的范围应尽可能与为同一研究招募的MCI或AD患者的年龄范围和分布相匹配。

为了避免预测变量之间的共线性,词法变量被正交到主分量中,并添加?…

Disclosures

The authors have nothing to disclose.

Acknowledgements

我们感谢明娜·莱赫托宁、图莫·亨尼宁、梅里亚·哈利伊宁和希尔卡·索伊宁对这里报道的数据收集和处理所作的贡献。数据收集得到了欧盟支持VPH痴呆症研究的支持,授权协议号为601055。

Materials

E-Prime Psychology Software Tools version 2.0.10.356.
PC with Windows and Keyboard
R R Foundation for Statistical Computing R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
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References

  1. Bybee, J. From Usage to Grammar: The Mind’s Response to Repetition. Language. 82 (4), 711-733 (2006).
  2. Oldfield, R. C., Wingfield, A. Response latencies in naming objects. The Quarterly Journal of Experimental Psychology. 17, 273-281 (1965).
  3. Nikolaev, A., et al. Effects of morphological family on word recognition in normal aging, mild cognitive impairment, and Alzheimer’s disease. Cortex. 116, 91-103 (2019).
  4. Milin, P., Feldman, L. B., Ramscar, M., Hendrix, P., Baayen, R. H. Discrimination in lexical decision. PLoS ONE. 12 (2), 1-42 (2017).
  5. Andrews, S. Frequency and neighborhood size effects on lexical access: activation or search?. Journal of Experimental Psychology: Learning, Memory, and Cognition. 15, 802-814 (1989).
  6. Grainger, J., Muneaux, M., Farioli, F., Ziegler, J. C. Effects of phonological and orthographic neighbourhood density interact in visual word recognition. The Quarterly Journal of Experimental Psychology. 58 (6), 981-998 (2005).
  7. Ossher, L., Flegal, K. E., Lustig, C. Everyday memory errors in older adults. Aging, Neuropsychology, and Cognition. 20, 220-242 (2013).
  8. Barresi, B. A., Nicholas, M., Connor, L. T., Obler, L. K., Albert, M. Semantic degradation and lexical access in age-related naming failures. Aging, Neuropsychology, and Cognition. 7, 169-178 (2000).
  9. Chertkow, H., Whatmough, C., Saumier, D., Duong, A. Cognitive neuroscience studies of semantic memory in Alzheimer’s disease. Progress in Brain Research. 169, 393-407 (2008).
  10. Cuetos, F., Arce, N., Martínez, C. Word recognition in Alzheimers’s disease: Effects of semantic degeneration. Journal of Neuropsychology. 11, 26-39 (2015).
  11. Stilwell, B. L., Dow, R. M., Lamers, C., Woods, R. T. Language changes in bilingual individuals with Alzheimer’s disease. International Journal of Language & Communication Disorders. 51, 113-127 (2016).
  12. Obler, L. K., Segalowitz, S. Language and brain dysfunction in dementia. Language functions and brain organization. , 267-282 (1983).
  13. Obler, L. K., Albert, M. L., Sarno, M. T. Language in the elderly aphasic and in the demented patient. Acquired aphasia. , 385-398 (1981).
  14. Obler, L. K., Gjerlow, K. . Language and the brain. , (1999).
  15. McKhann, G. M., et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging – Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia. 7, 263-269 (2011).
  16. Winblad, B., et al. Mild cognitive impairment – beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine. 256, 240-246 (2004).
  17. Albert, M. S., et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging – Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association. 7, 270-279 (2011).
  18. Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A., Martin, R. L. A new clinical scale for the staging of dementia. The British Journal of Psychiatry. 140, 566-572 (1982).
  19. Baayen, R. H., Cutler, A. Data Mining at the Intersection of Psychology and Linguistics. Twenty-first century psycholinguistics: Four cornerstones. , 69-83 (2005).
  20. Brants, T., Franz, A. . Web 1T 5-gram, version 1. , (2006).
  21. Baayen, R. H., Piepenbrock, R., Gulikers, L. . The CELEX lexical database (CD-ROM). , (1995).
  22. Wagenmakers, E. J., Ratcliff, R., Gomez, P., McKoon, G. A diffusion model account of criterion shifts in the lexical decision task. Journal of Memory and Language. 58, 140-159 (2008).
  23. Dufau, S., Grainger, J., Ziegler, J. C. How to say “no” to a non-word: a leaky competing accumulator model of lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition. 38, 1117-1128 (2012).
  24. R Core Team. . R: A language and environment for statistical computing. , (2018).
  25. Venables, W. N., Ripley, B. D. . Modern applied statistics with S. , (2002).
  26. Baayen, R. H., Milin, P. Analyzing reaction times. International Journal of Psychological Research. 3 (2), 12-28 (2010).
  27. Koller, M. robustlmm: An R package for robust estimation of linear mixed-effects models. Journal of Statistical Software. 75 (6), 1-24 (2016).
  28. Bates, D., Mächler, M., Bolker, B., Walker, S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 67, 1-48 (2015).
  29. Kuznetsova, A., Brockhoff, P. B., Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software. 82, 1-26 (2017).
  30. Akaike, H., Petrov, B. N., Csaki, B. F. Information theory and an extension of the maximum likelihood principle. Second International Symposium on Information Theory. , 267-281 (1973).
  31. Sakamoto, Y., Ishiguro, M., Kitagawa, G. . Akaike Information Criterion Statistics. , (1986).
  32. Barr, D. J., Levy, R., Scheepers, C., Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language. 68, 255-278 (2013).
  33. Bates, D., Kliegl, R., Vasishth, S., Baayen, H. Parsimonious mixed models. arXiv:1506.04967v2. , (2015).
  34. Harrison, X. A., et al. Brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ. 6, 1-32 (2018).
  35. Kimball, A. E., Shantz, K., Eager, C., Roy, C. E. J. Confronting quasi-separation in logistic mixed effects for linguistic data: a Bayesian approach. Journal of Quantitative Linguistics. , (2018).
  36. Coltheart, M., Davelaar, E., Jonasson, J. T., Besner, D., Dornick, S. Access to the internal lexicon. Attention and performance, vol. VI. , 535-556 (1977).
  37. Caselli, N. K., Caselli, M. K., Cohen-Goldberg, A. M. Inflected words in production: Evidence for a morphologically rich lexicon. The Quarterly Journal of Experimental Psychology. 69, 432-454 (2016).
  38. Yarkoni, T., Balota, D., Yap, M. Moving beyond Coltheart’s N: A new measure of orthographic similarity. Psychonomic Bulletin & Review. 15 (5), 971-979 (2008).
  39. Cohen, G. Recognition and retrieval of proper names: Age differences in the fan effect. European Journal of Cognitive Psychology. 2 (3), 193-204 (1990).
  40. Kemmerer, D. . Cognitive neuroscience of language. , (2015).
  41. Baayen, R. H. . Analyzing linguistic data: A practical introduction to statistics using R. , (2008).
  42. Nikolaev, A., Lehtonen, M., Higby, E., Hyun, J., Ashaie, S. A facilitatory effect of rich stem allomorphy but not inflectional productivity on single-word recognition. Applied Psycholinguistics. 39, 1221-1238 (2018).
  43. Nikolaev, A., et al. Behavioural and ERP effects of paradigm complexity on visual word recognition. Language, Cognition and Neuroscience. 10, 1295-1310 (2014).

Tags

Lexical Decision TaskWord RecognitionDementiaMild Cognitive ImpairmentMental LexiconAgingNeurodegenerative DisordersLanguage TasksCognitionReaction TimeData AnalysisR ProgrammingLme4LmerTest
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Nikolaev, A., Higby, E., Hyun, J., Ashaie, S. Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment. J. Vis. Exp. (148), e59753, doi:10.3791/59753 (2019).
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