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Verbal fluency test

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A verbal fluency test is a kind of psychological test in which a participant is asked to produce as many words as possible from a category in a given time (usually 60 seconds). This category can be semantic, including objects such as animals or fruits, or phonemic, including words beginning with a specified letter, such as p, for example.[1] The semantic fluency test is sometimes described as the category fluency test or simply as "freelisting", while letter fluency is also referred to as phonemic test fluency. The Controlled Oral Word Association Test (COWAT) is the most employed phonemic variant.[2][3] Although the most common performance measure is the total number of words, other analyses such as number of repetitions, number and length of clusters of words from the same semantic or phonemic subcategory, or number of switches to other categories can be carried out.[4][5]

By means of curve fitting, temporal clusters, switches,[6][7] and the initial slope can be determined. Whereas the total number of words and the initial slope indicate the global (macro) structure, clusters and switches evaluate the performance’s local (micro) structure.[8]

Clinical use

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Verbal fluency tests have been validated as brief cognitive assessments for the detection of cognitive impairment and dementia in non-specialist clinical settings.[9][10]

The National Health and Nutrition Examination Survey (NHANES) administered the Animal fluency to over three thousand participants 60 years and older in 2011–2014. Trained interviewers administered the test at the end of a face-to-face private interview in an examination center. An extensive analysis of these data has been published.[11] Scores (median, 25th percentile, 75th percentile) declined with age: 60-69y: 19, 15, 22; 70-79y: 16.0, 12, 20; 80+y: 14, 11, 17.

Overall, alterations in verbal fluency performance have been observed in conditions such as aphasia, schizophrenia, epilepsy, Alzheimer’s, or Parkinson’s disease in adults, and attention deficit hyperactivity disorder (ADHD), Down syndrome, autism spectrum disorder (ASD), developmental language disorder (DLD), or dyslexia in children.[12]

Performance characteristics

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Performance in verbal fluency tests show a number of consistent characteristics in both children and adults:[13][6][14]

  • A declining rate of production of new items over the duration of the task, which was long discussed as following either an exponential[15] or a hyperbolic[16] time course,[7] which finally could be shown to be special cases of a unifying power function (the fused Bousfield function).[17]
  • More typical category exemplars are produced with higher frequency (i.e. by more subjects), and earlier in lists, than less typical ones.
  • Items are produced in bursts of semantically-related words in the case of the semantic version and phonemic in the case of phonemic version. It has been shown that also burst of semantically-related word can be found in the case of the phonemic version. [18]

Neural correlates

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Regarding the brain areas used in this task, neuropsychological investigations implicate both frontal and temporal lobe areas, the contribution of the former being more important in the phonemic variant and of the latter in the semantic variant.[19] Accordingly, different neurological pathologies affecting these areas produce impairments (typically a reduction in the number of items generated) in one or both versions of the task.[3] For this reason fluency tests are commonly included in clinical batteries,[1] they have also been widely used in cognitive psychological and neuropsychological investigations.

Exploration of semantic memory

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Cluster analysis of animal semantic fluency data from British schoolchildren.[20]

Priming studies indicate that when a word or concept is activated in memory, and then spoken, it will activate other words or concepts which are associatively related or semantically similar to it. This evidence suggests that the order in which words are produced in the fluency task will provide an indirect measure of semantic distance between the items generated. Data from this semantic version of the task have therefore been the subject of many studies aimed at uncovering the structure of semantic memory, determining how this structure changes during normal development, or becomes disorganized through neurological disease or mental illness.

These studies generally make use of multiple fluency lists in order to make estimates of the semantic distance between pairs of concepts.[21] Techniques such as multidimensional scaling and hierarchical clustering can then be used to visualize the semantic organization of the conceptual space. Such studies have generally found that semantic memory, at least as reflected by this test, has a schematic, or script-based, organization.[22] whose core aspects may remain stable throughout life.[20][23] For instance, the figure on the right shows a hierarchical clustering analysis of animal semantic fluency data from 55 British schoolchildren aged 7–8.[20] The analysis reveals that children have schematic organization for this category according to which animals are grouped by where they are most commonly seen (on the farm, at home, in the ocean, at the zoo). Children, adults, and even zoology PhD candidates, all show this same tendency to cluster animals according to the environmental context in which they are observed.[24]

It has been proposed that the semantic memory organization, underlying performance in the semantic fluency test, becomes disordered as the result of some forms of neuropsychological disorder such as Alzheimer's disease,[25] Parkinson's Disease[18] and schizophrenia;[26][27][28] however, the evidence for this has been queried on theoretical and methodological grounds.[21][29]

Curve fitting allows an analysis of the global (macro) structure as well as the temporal dynamics of clusters and switches, and a combination with semantic analysis allows the derivation of time-semantic relations. Furthermore, hypotheses about the structure of the underlying semantic network have been formulated based on the temporal analysis of verbal fluency tasks by means of curve fitting.[7][30]

See also

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References

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  1. ^ a b Lezak, Muriel Deutsch (1995). Neuropsychological assessment. Oxford [Oxfordshire]: Oxford University Press. ISBN 978-0-19-509031-4.
  2. ^ Loonstra AS, Tarlow AR, Sellers AH (2001). "COWAT metanorms across age, education, and gender". Appl Neuropsychol. 8 (3): 161–6. doi:10.1207/S15324826AN0803_5. PMID 11686651. S2CID 13558144.
  3. ^ a b Ardila, A.; Ostrosky-solís, F.; Bernal, B. (2006). "Cognitive testing toward the future: The example of Semantic Verbal Fluency (ANIMALS)". International Journal of Psychology. 41 (5): 324–332. doi:10.1080/00207590500345542.
  4. ^ Troyer AK, Moscovitch M, Winocur G (January 1997). "Clustering and switching as two components of verbal fluency: evidence from younger and older healthy adults". Neuropsychology. 11 (1): 138–46. doi:10.1037/0894-4105.11.1.138. PMID 9055277.
  5. ^ Troyer AK, Moscovitch M, Winocur G, Leach L, Freedman M (March 1998). "Clustering and switching on verbal fluency tests in Alzheimer's and Parkinson's disease". J Int Neuropsychol Soc. 4 (2): 137–43. doi:10.1017/S1355617798001374. PMID 9529823. S2CID 25320796.
  6. ^ a b Gruenewald PJ, Lockhead GR (1980). "The free recall of category examples". Journal of Experimental Psychology: Human Learning & Memory. 6 (3): 225–241. doi:10.1037/0278-7393.6.3.225.
  7. ^ a b c Wixted JT, Rohrer D (1994). "Analyzing the dynamics of free recall: An integrative review of the empirical literature". Psychonomic Bulletin & Review. 1 (1): 89–106. doi:10.3758/BF03200763. PMID 24203416. S2CID 18338411.
  8. ^ Meyer DJ, Messer J, Singh T, Thomas PJ, Woyczynski WA, Kaye J, Lerner AJ (2012). "Random local temporal structure of category fluency responses". Journal of Computational Neuroscience. 32 (2): 213–231. doi:10.1007/s10827-011-0349-5. PMID 21739237. S2CID 5902186.
  9. ^ Sebaldt, Rolf; Dalziel, William; Massoud, Fadi; Tanguay, André; Ward, Rick; Thabane, Lehana; Melnyk, Peter; Landry, Pierre-Alexandre; Lescrauwaet, Benedicte (September 2009). "Detection of cognitive impairment and dementia using the animal fluency test: the DECIDE study". The Canadian Journal of Neurological Sciences. 36 (5): 599–604. doi:10.1017/S0317167100008106. ISSN 0317-1671. PMID 19831129.
  10. ^ Canning, S. J. Duff; Leach, L.; Stuss, D.; Ngo, L.; Black, S. E. (2004-02-24). "Diagnostic utility of abbreviated fluency measures in Alzheimer disease and vascular dementia". Neurology. 62 (4): 556–562. doi:10.1212/WNL.62.4.556. ISSN 1526-632X. PMID 14981170. S2CID 12836529.
  11. ^ Brody, D.J.; Kramarow, E.A.; Taylor, C.A.; McGuire, L.C. (1 Sep 2019). "Cognitive Performance in Adults Aged 60 and Over: National Health and Nutrition Examination Survey, 2011-2014". Natl Health Stat Report (126). CDC/National Center for Health Statistics: 1–23. PMID 31751207.
  12. ^ Karousou, Alexandra; et al. (2023). "Clustering and Switching in Semantic Verbal Fluency: Their Development and Relationship with Word Productivity in Typically Developing Greek-Speaking Children and Adolescents". Journal of Intelligence. 11 (11): 209.
  13. ^ Henley NM (1969). "A psychological study of the semantics of animal terms". Journal of Verbal Learning and Verbal Behavior. 8 (2): 176–184. doi:10.1016/S0022-5371(69)80058-7.
  14. ^ Kail R, Nippold MA (1984). "Unconstrained retrieval from semantic memory". Child Development. 55 (3): 944–951. doi:10.2307/1130146. JSTOR 1130146. PMID 6734329.
  15. ^ Bousfield WA, Sedgewick CH (1944). "An analysis of sequences of restricted associative responses". The Journal of General Psychology. 30 (2): 149–165. doi:10.1080/00221309.1944.10544467.
  16. ^ Bousfield WA, Sedgewick CH, Cohen BH (1954). "Certain temporal characteristics of the recall of verbal associates". American Journal of Psychology. 67 (1): 111–118. doi:10.2307/1418075. JSTOR 1418075. PMID 13138773.
  17. ^ Ehlen F, Fromm O, Vonberg I, Klostermann F (2016). "Overcoming duality: the fused bousfieldian function for modeling word production in verbal fluency tasks". Psychonomic Bulletin & Review. 23 (5): 1354–1373. doi:10.3758/s13423-015-0987-0. PMID 26715583.
  18. ^ a b Hähnel, Tom; Feige, Tim; Kunze, Julia; Epler, Andrea; Frank, Anika; Bendig, Jonas; Schnalke, Nils; Wolz, Martin; Themann, Peter; Falkenburger, Björn (2023-08-02). "A Semantic Relatedness Model for the Automatic Cluster Analysis of Phonematic and Semantic Verbal Fluency Tasks Performed by People With Parkinson Disease: Prospective Multicenter Study". JMIR Neurotechnology. 2: e46021. doi:10.2196/46021. ISSN 2817-092X.
  19. ^ Baldo JV, Schwartz S, Wilkins D, Dronkers NF (November 2006). "Role of frontal versus temporal cortex in verbal fluency as revealed by voxel-based lesion symptom mapping". J Int Neuropsychol Soc. 12 (6): 896–900. doi:10.1017/S1355617706061078. PMID 17064451. S2CID 2319136.
  20. ^ a b c Crowe SJ, Prescott TJ (2003). "Continuity and change in the development of category structure: Insights from the semantic fluency task". Int J Behav Dev. 27 (5): 467–479. doi:10.1080/01650250344000091. S2CID 54534372.
  21. ^ a b Prescott TJ, Newton LD, Mir NU, Woodruff P, Parks RW (2006). "A new dissimilarity measure for finding semantic structure in category fluency data with implications for understanding memory organization in schizophrenia". Neuropsychology. 20 (5): 685–699. doi:10.1037/0894-4105.20.6.685. PMID 17100513.
  22. ^ Lucariello J, Kyratzis A, Nelson K (1992). "Taxonomic knowledge - what kind and when". Child Development. 63 (4). Child Development, Vol. 63, No. 4: 978–998. doi:10.2307/1131248. JSTOR 1131248.
  23. ^ Mandler JM. "Representation". In Flavell JH, Markman EM (eds.). Cognitive development. Vol. 3. New York: Wiley.
  24. ^ Storm C (1980). "The semantic structure of animal terms - a developmental-study". Int J Behav Dev. 3 (4): 381–407. doi:10.1177/016502548000300403. S2CID 145575546.
  25. ^ Chan AS, Butters N, Salmon DP, Johnson SA, Paulsen JS, Swenson MR (1995). "Comparison of the semantic networks in patients with dementia and amnesia". Neuropsychology. 9 (2): 177–186. doi:10.1037/0894-4105.9.2.177. S2CID 210773289.
  26. ^ Aloia AS, Gourovitch ML, Weinberger DR, Goldberg TE (1996). "An investigation of semantic space in patients with schizophrenia". Journal of the International Neuropsychological Society. 2 (4): 267–273. doi:10.1017/S1355617700001272. PMID 9375174. S2CID 13149262.
  27. ^ Paulsen JS; Romero R; Chan AS; Davis AV; Heaton R.K; Jeste DV (1996). "Impairment of the semantic network in schizophrenia". Psychiatry Research. 63 (2–3): 109–121. doi:10.1016/0165-1781(96)02901-0. PMID 8878307. S2CID 19183218.
  28. ^ Sumiyoshi C, Matsui M, Sumiyoshi T, Yamashita I, Sumiyoshi S, Kurachi M (2001). "Semantic structure in schizophrenia as assessed by the category fluency test: Effect of verbal intelligence and age of onset". Psychiatry Research. 105 (3): 187–199. doi:10.1016/S0165-1781(01)00345-6. PMID 11814538. S2CID 9571760.
  29. ^ Storms G, Dirikx T, Saerens J, Verstraeten S, De Deyn PP (2003). "On the use of scaling and clustering in the study of semantic deficits". Neuropsychology. 17 (2): 289–301. doi:10.1037/0894-4105.17.2.289. PMID 12803435.
  30. ^ Fromm O, Klosterman F, Ehlen F (2020). "A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production". Frontiers in Systems Neuroscience. 14: 58. doi:10.3389/fnsys.2020.00058. PMC 7485382. PMID 32982704.
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