CoChat
Sign Up
Cited by 115Cognitive Abilities and TestingDevelopmental and Educational NeuropsychologyPsychological Testing and Assessment

The NIH Toolbox Pattern Comparison Processing Speed Test: Normative Data

Open access
Carlozzi, Noelle E., Beaumont, Jennifer L., Tulsky, David S., Gershon, Richard C.·2015·Archives of Clinical Neuropsychology
DOI 10.1093/arclin/acv031PMC PMC4542749
Open this paper in CoChat — free account

Abstract

The NIH Toolbox Pattern Comparison Processing Speed Test was developed to assess processing speed. While initial validation work provides preliminary support for this test in both children and adults, more work is needed to ensure dependability and generalizability. Thus, this replication study examines descriptive data (including age effects), test-retest reliability, and construct validity in n = 4,859 participants ages 3-85 years (matched to 2010 census data). Although the Pattern Comparison was not appropriate for all 3 and 4 years old, by ages 5 and 6, more meaningful scores were apparent. There was evidence for convergent and discriminant validity. There was also a moderate practice effect (i.e., increase of 5.5 points) over a 1-week time frame. Pattern Comparison exhibits a number of strengths: it is appropriate for use across the lifespan (ages 5-85), it is short and easy to administer, and there is support for construct validity.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/arclin/acv031

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Individuals in current sample had higher performance scores on Pattern Comparison compared to expected with WAIS-III and WAIS-IV Processing Speed Indices.

    “However, while the Pattern Comparison test is sensitive to the effects of TBI, individuals in the current sample had higher performance scores compared to what would have been expected with the WAIS-III (Donders et al., 2001) and the WAIS-IV Processing Speed Indices (Carlozzi, Beaumont, et al., 2015; Carlozzi, Kirsch, et al., 2015; Donders & Strong, 2015).”

    From Using the NIH Toolbox Cognition Battery (NIHTB-CB) in individuals with traumatic brain injury. (2017) · cites this paper as Carlozzi, Beaumont, et al., 2015

    The Results (Table 3; page unavailable) provide age-group norms for Pattern Comparison, but do not compare scores in the citing paper’s sample with WAIS-III or WAIS-IV Processing Speed Indices. Searches for those indices and TBI-related expected performance found no passage in our copy making that comparison. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 57

  1. Baudouin A., Clarys D., Vanneste S., Isingrini M. (2009). Executive functioning and processing speed in age-related differences in memory: Contribution of a coding task. Brain and Cognition, 71 (3), 240–245. [DOI] [PubMed] [Google Scholar]
  2. Beaumont J. L., Havlik R., Cook K. F., Hays R. D., Wallner-Allen K., Korper S. P., et al. (2013). Norming plans for the NIH Toolbox. Neurology, 80 (11 Suppl. 3), S87–S92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bell N. L., Lassiter K. S., Matthews T. D., Hutchinson M. B. (2001). Comparison of the Peabody Picture Vocabulary Test—Third Edition and Wechsler Adult Intelligence Scale—Third Edition with University Students. Journal of Clinical Psychology, 57 (3), 417–422. [DOI] [PubMed] [Google Scholar]
  4. Boone K. B., Victor T. L., Wen J., Razani J., Ponton M. (2007). The association between neuropsychological scores and ethnicity, language, and acculturation variables in a large patient population. Archives of Clinical Neuropsychology, 22 (3), 355–365. [DOI] [PubMed] [Google Scholar]
  5. Breed S., Sacks A., Ashman T. A., Gordon W. A., Dahlman K., Spielman L. (2008). Cognitive functioning among individuals with traumatic brain injury, Alzheimer's disease, and no cognitive impairments. Journal of Head Trauma Rehabilitation, 23 (3), 149–157. [DOI] [PubMed] [Google Scholar]
  6. Carlozzi N. E., Tulsky D. S., Kail R. V., Beaumont J. L. (2013). Chapter VI. NIH Toolbox Cognition Battery (CB): Measuring processing speed. Monographs of the Society for Research in Child Development, 78 (4), 88–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Casaletto K. B., Umlauf A., Beaumont J., Gershon R., Slotkin J., Akshoomoff N., et al. (in press). Demographically corrected normative standards for the English version of the NIH Toolbox Cognition Battery. Journal of the International Neuropsychological Society. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chiaravalloti N. D., Christodoulou C., Demaree H. A., DeLuca J. (2003). Differentiating simple versus complex processing speed: Influence on new learning and memory performance. Journal of Clinical and Experimental Neuropsychology, 25 (4), 489–501. [DOI] [PubMed] [Google Scholar]
  9. Clay O. J., Edwards J. D., Ross L. A., Okonkwo O., Wadley V. G., Roth D. L., et al. (2009). Visual function and cognitive speed of processing mediate age-related decline in memory span and fluid intelligence. Journal of Aging and Health, 21 (4), 547–566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Coyle T. R., Pillow D. R., Snyder A. C., Kochunov P. (2011). Processing speed mediates the development of general intelligence (g) in adolescence. Psychological Science, 22 (10), 1265–1269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. de Jager C. A. (2004). Changes over time in memory, processing speed and clock drawing tests help to discriminate between vascular cognitive impairment, mild cognitive impairment and Alzheimer's disease. Neurological Research, 26 (5), 481–487. [DOI] [PubMed] [Google Scholar]
  12. DeLuca J., Chelune G. J., Tulsky D. S., Lengenfelder J., Chiaravalloti N. D. (2004). Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? Journal of Clinical and Experimental Neuropsychology, 26 (4), 550–562. [DOI] [PubMed] [Google Scholar]
  13. Demaree H. A., DeLuca J., Gaudino E. A., Diamond B. J. (1999). Speed of information processing as a key deficit in multiple sclerosis: Implications for rehabilitation. Journal of Neurology, Neurosurgery, & Psychiatry, 67 (5), 661–663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. De Monte V. E., Geffen G. M., May C. R., McFarland K. (2010). Improved sensitivity of the rapid screen of mild traumatic brain injury. Journal of Clinical and Experimental Neuropsychology, 32 (1), 28–37. [DOI] [PubMed] [Google Scholar]
  15. Denney D. R., Gallagher K. S., Lynch S. G. (2011). Deficits in processing speed in patients with multiple sclerosis: Evidence from explicit and covert measures. Archives of Clinical Neuropsychology, 26 (2), 110–119. [DOI] [PubMed] [Google Scholar]
  16. Donders J., Tulsky D. S., Zhu J. (2001). Criterion validity of new WAIS-II subtest scores after traumatic brain injury. Journal of the International Neuropsychological Society, 7 (7), 892–898. [PubMed] [Google Scholar]
  17. Duff K., Beglinger L. J., Schultz S. K., Moser D. J., McCaffrey R. J., Haase R. F., et al. (2007). Practice effects in the prediction of long-term cognitive outcome in three patient samples: A novel prognostic index. Archives of Clinical Neuropsychology, 22 (1), 15–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Earles J. L., Kersten A. W. (1999). Processing speed and adult age differences in activity memory. Experimental Aging Research, 25 (3), 243–253. [DOI] [PubMed] [Google Scholar]
  19. Forn C., Belenguer A., Parcet-Ibars M. A., Avila C. (2008). Information-processing speed is the primary deficit underlying the poor performance of multiple sclerosis patients in the Paced Auditory Serial Addition Test (PASAT). Journal of Clinical and Experimental Neuropsycholology, 30 (7), 789–796. [DOI] [PubMed] [Google Scholar]
  20. Funder D. C., Levine J. M., Mackie D. M., Morf C. C., Sansone C., Vazire S., et al. (2014). Improving the dependability of research in personality and social psychology: Recommendations for research and educational practice. Personality and Social Psychology Review, 18 (1), 3–12. [DOI] [PubMed] [Google Scholar]
  21. Gerritsen M. J., Berg I. J., Deelman B. G., Visser-Keizer A. C., Meyboom-de Jong B. (2003). Speed of information processing after unilateral stroke. Journal of Clinical and Experimental Neuropsychology, 25 (1), 1–13. [DOI] [PubMed] [Google Scholar]
  22. Gershon R. C., Cook K. F., Mungas D., Manly J. J., Slotkin J., Beaumont J. L., et al. (2014). Language measures of the NIH Toolbox Cognition Battery. Journal of the International Neuropsychological Society, 20 (6), 642–651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gershon R. C., Slotkin J., Manly J. J., Blitz D. L., Beaumont J. L., Schnipke D., et al. (2013). IV. NIH Toolbox Cognition Battery (CB): Measuring language (vocabulary comprehension and reading decoding). Monographs of the Society for Research in Child Development, 78 (4), 49–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Gershon R. C., Wagster M. V., Hendrie H. C., Fox N. A., Cook K. F., Nowinski C. J. (2013). NIH Toolbox for assessment of neurological and behavioral function. Neurology, 80 (Suppl. 3), S2–S6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gontkovsky S. T., Beatty W. W. (2006). Practical methods for the clinical assessment of information processing speed. International Journal of Neuroscience, 116 (11), 1317–1325. [DOI] [PubMed] [Google Scholar]
  26. Gonzalez-Blanch C., Rodriguez-Sanchez J. M., Perez-Iglesias R., Pardo-Garcia G., Martinez-Garcia O., Vazquez-Barquero J. L., et al. (2010). First-episode schizophrenia patients neuropsychologically within the normal limits: Evidence of deterioration in speed of processing. Schizophrenia Research, 119 (1–3), 18–26. [DOI] [PubMed] [Google Scholar]
  27. Heaton R. K., Taylor M. J., Manly J. (2003). Demographic effects and use of demographically corrected norms with the WAIS-III and WMS-III. In Tulsky D. S., et al. (Eds.), Clinical interpretation of the WAIS-III and WMS-III. (pp. 181–210). San Diego: Academic Press. [Google Scholar]
  28. Heyanka D. J., Mackelprang J. L., Golden C. J., Marke C. D. (2010). Distinguishing Alzheimer's disease from vascular dementia: An exploration of five cognitive domains. International Journal of Neuroscience, 120 (6), 409–414. [DOI] [PubMed] [Google Scholar]
  29. Kail R. (1998). Speed of information processing in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 20 (1), 98–106. [DOI] [PubMed] [Google Scholar]
  30. Klinteberg B. A., Levander S. E., Schalling D. (1987). Cognitive sex differences: Speed and problem-solving strategies on computerized neuropsychological tasks. Perceptual and Motor Skills, 65 (3), 683–697. [DOI] [PubMed] [Google Scholar]
  31. Leskela M., Hietanen M., Kalska H., Ylikoski R., Pohjasvaara T., Mantyla R., et al. (1999). Executive functions and speed of mental processing in elderly patients with frontal or nonfrontal ischemic stroke. European Journal of Neuroscience, 6 (6), 653–661. [DOI] [PubMed] [Google Scholar]
  32. Lindenberger U., Mayr U., Kliegl R. (1993). Speed and intelligence in old age. Psychology and Aging, 8 (2), 207–220. [DOI] [PubMed] [Google Scholar]
  33. Llorente A. M., Turcich M., Lawrence K. A. (2004). Differences in neuropsychological performance associated with ethnicity in children with HIV-1 infection: Preliminary findings. Applied Neuropsychology, 11 (1), 47–53. [DOI] [PubMed] [Google Scholar]
  34. Loranger M., Lussier J., Pepin M., Hopps S. L., Senecal B. (2000). Information-processing speed and assessment of early response latency among stroke patients. Psychological Reports, 87 (3 Pt 1), 893–900. [DOI] [PubMed] [Google Scholar]
  35. Machulda M. M., Pankratz V. S., Christianson T. J., Ivnik R. J., Mielke M. M., Roberts R. O., et al. (2013). Practice effects and longitudinal cognitive change in normal aging vs. incident mild cognitive impairment and dementia in the Mayo Clinic Study of Aging. The Clinical Neuropsychologist, 27 (8), 1247–1264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Majeres R. L. (1997). Sex differences in phonetic processing: Speed of identification of alphabetical sequences. Perceptual and Motor Skills, 85 (3 Pt 2), 1243–1251. [DOI] [PubMed] [Google Scholar]
  37. Majeres R. L. (1999). Sex differences in phonological processes: Speeded matching and word reading. Memory and Cognition, 27 (2), 246–253. [DOI] [PubMed] [Google Scholar]
  38. Makel M. C., Plucker J. A. (2014). Creativity is more than novelty: Reconsidering replication as a creativity act. Psychology of Aesthetics Creativity and the Arts, 8 (1), 27–29. [Google Scholar]
  39. Mathias J. L., Wheaton P. (2007). Changes in attention and information-processing speed following severe traumatic brain injury: A meta-analytic review. Neuropsychology, 21 (2), 212–223. [DOI] [PubMed] [Google Scholar]
  40. McCaffrey R. J., Duff K., Westervelt H. (2000). Practitioner's guide to evaluating change with neuropsychological assessment instruments. New York: Kluwer Academic/Plenum Publishers. [Google Scholar]
  41. Mendez M. F., Cherrier M. M., Perryman K. M. (1997). Differences between Alzheimer's disease and vascular dementia on information processing measures. Brain and Cognition, 34 (2), 301–310. [DOI] [PubMed] [Google Scholar]
  42. Morrens M., Hulstijn W., Matton C., Madani Y., van Bouwel L., Peuskens J., et al. (2008). Delineating psychomotor slowing from reduced processing speed in schizophrenia. Cognitive Neuropsychiatry, 13 (6), 457–471. [DOI] [PubMed] [Google Scholar]
  43. Norman M. A., Moore D. J., Taylor M., Franklin D., Jr., Cysique L., Ake C., et al. (2011). Demographically corrected norms for African Americans and Caucasians on the Hopkins Verbal Learning Test-Revised, Brief Visuospatial Memory Test-Revised, Stroop Color and Word Test, and Wisconsin Card Sorting Test 64-Card Version. Journal of Clinical and Experimental Neuropsychology, 33 (7), 793–804. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Ojeda N., Pena J., Sanchez P., Elizagarate E., Ezcurra J. (2008). Processing speed mediates the relationship between verbal memory, verbal fluency, and functional outcome in chronic schizophrenia. Schizophrenia Research, 101 (1–3), 225–233. [DOI] [PubMed] [Google Scholar]
  45. Perbal S., Couillet J., Azouvi P., Pouthas V. (2003). Relationships between time estimation, memory, attention, and processing speed in patients with severe traumatic brain injury. Neuropsychologia, 41 (12), 1599–1610. [DOI] [PubMed] [Google Scholar]
  46. Peters M. (1997). Gender differences in intercepting a moving target by using a throw or button press. Journal of Motor Behavior, 29 (4), 290–296. [DOI] [PubMed] [Google Scholar]
  47. Rao S. M., St Aubin-Faubert P., Leo G. J. (1989). Information processing speed in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsycholology, 11 (4), 471–477. [DOI] [PubMed] [Google Scholar]
  48. Roig M., Placakis N. (1992). Hemisphericity style, sex, and performance on a mirror-tracing task. Perceptual and Motor Skills, 74 (3 Pt 2), 1143–1148. [DOI] [PubMed] [Google Scholar]
  49. Salthouse T. A., Coon V. E. (1993). Influence of task-specific processing speed on age differences in memory. Journal of Gerontology, 48 (5), P245–P255. [DOI] [PubMed] [Google Scholar]
  50. Salthouse T. A., Kail R. (1983). Memory development throughout the life-span – the role of processing rate. Life-Span Development and Behavior, 5, 89–116. [Google Scholar]
  51. Schwartz B. S., Glass T. A., Bolla K. I., Stewart W. F., Glass G., Rasmussen M., et al. (2004). Disparities in cognitive functioning by race/ethnicity in the Baltimore Memory Study. Environ Health Perspect, 112 (3), 314–320. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Sliwinski M., Buschke H. (1999). Cross-sectional and longitudinal relationships among age, cognition, and processing speed. Psychology and Aging, 14 (1), 18–33. [DOI] [PubMed] [Google Scholar]
  53. Tombaugh T. N., Rees L., Stormer P., Harrison A. G., Smith A. (2007). The effects of mild and severe traumatic brain injury on speed of information processing as measured by the computerized tests of information processing (CTIP). Archives of Clinical Neuropsychology, 22 (1), 25–36. [DOI] [PubMed] [Google Scholar]
  54. Tulsky D. S., Carlozzi N., Chiaravalloti N. D., Beaumont J. L., Kisala P. A., Mungas D., et al. (2014). NIH Toolbox Cognition Battery (NIHTB-CB): List sorting test to measure working memory. Journal of the International Neuropsychological Society, 20 (6), 599–610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Tulsky D. S., Carlozzi N. E., Chevalier N., Espy K. A., Beaumont J. L., Mungas D. (2013). V. NIH Toolbox Cognition Battery (CB): Measuring working memory. Monographs of the Society for Research in Child Development, 78 (4), 70–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zelazo P. D., Anderson J. E., Richler J., Wallner-Allen K., Beaumont J. L., Conway K. P., et al. (2014). NIH Toolbox Cognition Battery (CB): Validation of executive function measures in adults. Journal of the International Neuropsychological Society, 20 (6), 620–629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zelazo P. D., Anderson J. E., Richler J., Wallner-Allen K., Beaumont J. L., Weintraub S. (2013). II. NIH Toolbox Cognition Battery (CB): Measuring executive function and attention. Monographs of the Society for Research in Child Development, 78 (4), 16–33. [DOI] [PubMed] [Google Scholar]

Cited by

Sorted by citations

Authors
Carlozzi, N. E., Beaumont, J. L., Tulsky, D. S., & Gershon, R. C.
Affiliations
  • University of Michigan (US)
  • Northwestern University (US)
  • Kessler Foundation (US)
Corresponding author
Carlozzi, Noelle E.
ORCID
Venue
Archives of Clinical Neuropsychology
Publisher
Oxford University Press (OUP)
Published
2015-05-29
Volume
30
Issue
5
Pages
359-368
ISSN
  • 0887-6177 (ISSN-L)
  • 1873-5843
DOI
10.1093/arclin/acv031
PMC
PMC4542749
OpenAlex
W2215325240
Cited by
115
References
12
Citations by year
2026: 11 · 2025: 22 · 2024: 18 · 2023: 9 · 2022: 14 · 2021: 11 · 2020: 12 · 2019: 8 · 2018: 5 · 2017: 3 · 2016: 2
FWCI
4.56
Type
journal article
Language
en
Open access
Locations
  • Archives of Clinical Neuropsychology (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
  • Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
Keywords
NIH Toolbox, processing speed, reference values, test-retest reliability, construct validity, convergent validity, discriminant validity, practice effects, age effects, children and adults
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Neuropsychological Tests, Pattern Recognition, Visual, Practice, Psychological, Psychomotor Performance, Reaction Time, Reference Values, Reproducibility of Results, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review