Processing speed is one of the major factor in general cognition and a fundamental part of the cognitive system (Kail & Salthouse, 1994).
Slow cognitive processing is linked to academic achievement and several clinical disorders. Vulnerability to information processing load is related to attentional problems. Inattentive children perform poorly on measures of information processing speed (Weiler, Bernstein, Bellinger, & Waber, 2000). Some authors talk about the possibility that inattentive subtype of ADHD (ADD), could be a different group from general ADHD characterized by poor cognitive interference control and slow processing (Goth-Owens, Martinez-Torteya, Martel, & Nigg, 2010). Likewise, attention and speed weaknesses coexist in children with Autism and ADHD, an contribute significantly to the prediction of academic achievement (Mayes & Calhoun, 2007). Deficits in processing speed are a cognitive risk factor for reading disabilities and ADHD (Shanahan et al., 2006). Children with dyslexia, compared with normal performance children, have persistent problems in naming speed for all stimuli regardless the stimulus requires grapheme-phoneme decoding. In the study of Fawcet (Fawcett & Nicolson1994), performance speed of children with dyslexia at the age of seventeen was close to those with eight years old at control group.
Naming speed can also be modified by medication. Children with ADHD taking methylphenidate selectively, can improve color naming speed but not the speed of naming letters or digits. These findings implicate that naming speed deficits are associated with effortful semantic processing in ADHD, and can be improved but not normalized by methylphenidate (Tannock, Martinussen, & Frijters, 2000).
The relationship between learning disabilities and intelligence is not clear. Children with low IQ scores can be good readers. Poor readers at variety of IQ levels show similar reading, spelling, language and memory deficits (Siegel, 1989).
Another important field is the study of neural correlates of processing speed. Children with developmental dyslexia show deficient phonological processing. When we study the functional networks with rapid auditory processing (RAP), we found functional alteration in left hemisphere frontal regions in prereading children at risk for dyslexia (Raschle, Stering, Meissner, & Gaab, 2013).
Finally, studies show a relation between speed processing, rapid naming and phonological awareness, and all three are related to reading achievement. Poor readers are more slow than good readers across response time measures on the rapid object naming task. Catts et al. 2002, suggest that some poor readers have a general deficit in speed processing, and that speed processing may be conceptualized as an “extraphonological” factor. Naming speed explains variance in reading skills independently of measures of phonological awareness (Bowers & Swanson, 1991).
If you want to receive more information, please fill out the contact form:
Bowers, P. G., & Swanson, L. B. (1991). Naming speed deficits in reading disability: Multiple measures of a singular process. Journal of Experimental Child Psychology, 51(2), 195-219. doi:10.1016/0022-0965(91)90032-N
Catts, H. W., Gillispie, M., Leonard, L. B., Kail, R. V., & Miller, C. A. (2002). The Role of Speed of Processing, Rapid Naming, and Phonological Awareness in Reading Achievement. Journal of Learning Disabilities, 35(6), 510-525. doi:10.1177/00222194020350060301
Fawcett, A. J., & Nicolson, R. I. (1994). Naming Speed in Children with Dyslexia. Journal of Learning Disabilities, 27(10), 641-646. doi:10.1177/002221949402701004
Goth-Owens, T. L., Martinez-Torteya, C., Martel, M. M., & Nigg, J. T. (2010). Processing Speed Weakness in Children and Adolescents with Non-Hyperactive but Inattentive ADHD (ADD). Child Neuropsychology, 16(6), 577-591. doi:10.1080/09297049.2010.485126
Kail, R., & Salthouse, T. A. (1994). Processing speed as a mental capacity. Acta Psychologica, 86(2–3), 199-225. doi:10.1016/0001-6918(94)90003-5
Mayes, S. D., & Calhoun, S. L. (2007). Learning, Attention, Writing, and Processing Speed in Typical Children and Children with ADHD, Autism, Anxiety, Depression, and Oppositional-Defiant Disorder. Child Neuropsychology, 13(6), 469-493. doi:10.1080/09297040601112773
Raschle, N. M., Stering, P. L., Meissner, S. N., & Gaab, N. (2013). Altered Neuronal Response During Rapid Auditory Processing and Its Relation to Phonological Processing in Prereading Children at Familial Risk for Dyslexia. Cerebral Cortex, bht104. doi:10.1093/cercor/bht104
Shanahan, M. A., Pennington, B. F., Yerys, B. E., Scott, A., Boada, R., Willcutt, E. G., … DeFries, J. C. (2006). Processing Speed Deficits in Attention Deficit/Hyperactivity Disorder and Reading Disability. Journal of Abnormal Child Psychology, 34(5), 584-601. doi:10.1007/s10802-006-9037-8
Siegel, L. S. (1989). IQ Is Irrelevant to the Definition of Learning Disabilities. Journal of Learning Disabilities, 22(8), 469-478. doi:10.1177/002221948902200803
Tannock, R., Martinussen, R., & Frijters, J. (2000). Naming Speed Performance and Stimulant Effects Indicate Effortful, Semantic Processing Deficits in Attention-Deficit/Hyperactivity Disorder. Journal of Abnormal Child Psychology, 28(3), 237-252. doi:10.1023/A:1005192220001
Weiler, M. D., Bernstein, J. H., Bellinger, D. C., & Waber, D. P. (2000). Processing Speed in Children With Attention Deficit/Hyperactivity Disorder, Inattentive Type. Child Neuropsychology, 6(3), 218-234. doi:10.1076/chin.126.96.36.19956
Tests of verbal ability have been used by many years (Thurstone, 1938; Jones-Gotman & Milner, 1977). On this test, subject is presented with a category (e.g.words beginning with ‘M’, or names of animals) and is asked to produce as many examples of these as possible within a given time of period.
Verbal fluency has demonstrated to be impaired in dysphasic patients, but also in patients with lesions on the left (Benton, 1968) and right frontal lobe (Pendleton et al., 1982).
Naming performance has been used to test disabilities in the population. Longitudinal studies show that naming performance changes across the life span, declining specially in oldest subjects (Au et al., 1995), which reflects a breakdown in perceptual and semantic processes.
One of the most usual tests for examining this ability is the Boston Naming Test (BNT) and the Parietal Lobe Battery. The BNT enjoys and reach database in different countries and different pathologies, as well as normative data across age range.
One important point in fluency tasks is the category of the word. When we test people with mild dementia they perform better naming animals than naming words with specified letter on the beginning, which means that category structure influences retrieval processes (Rosen, 1980).
To test naming fluency is important to control the age of the participants. An effect of aging is observed specially after forty years age and a decline of the verbal ability after the sixties (Rodriguez-Aranda & Martinussen, 2006).
Naming ability is mediated by different strategies. When we compare two measures of verbal fluency, initial letter versus excluded letter (words produced not containing a designated letter), we found that both fluency tasks rely on verbal ability and articulation speed. Excluded letter fluency performance rely more on speak and executive function (Hughes & Bryan, 2002).
Verbal fluency is also a measure of verbal intelligence. In the study of Miller (Miller, 1984), they compared verbal fluency in two groups of patients, one with focal lesions and another with dementia. They use regression to predict fluency from an index of verbal intelligence. When verbal intelligence was taking into account using regression equation, they found that impaired fluency is a specific phenomenon following frontal lesions and not a consequence of intellectual deterioration in dementia.
The most used test of verbal fluency is the FAS. It consists on a task in which the participant has one minute to generate words beginning with each letter ‘F’, ‘A’, ‘S’ (phonemic fluency) and animal names (semantic fluency). The FAS has been shown to be more sensitive to the effects of education than age: the number of words increases as the level of education increase, while remains constant until age 60 (Tombaugh, Kozak, & Rees, 1999). Other studies have shown that level of education but not age or gender significantly influence verbal fluency (Mathuranath et al., 2003).
Neural correlates of fluency task
Letter and category fluency tasks are associated with frontal and temporal lobe. Letter fluency presents greater activation in left pre-central and inferior frontal gyrus, while category fluency presents greater activation in left middle frontal gyrus and left fusiform gyrus.
Location and cortical activity can be modulated by varying verbal fluency task demands. Right hemisphere activation is greater during automatic speech in response to over-learned category while left hemisphere activation is greater in letter fluency tasks when demands are on executive function (Birn et al., 2010). Furthermore, the uncinate fasciculus shows positive correlation with Boston Naming Test (Catani et al., 2013).
If you want to contact a psychologist or receive more information, please fill out the contact form:
Au, R., Joung, P., Nicholas, M., Obler, L. K., Kass, R., & Albert, M. L. (1995). Naming ability across the adult life span. Aging, Neuropsychology, and Cognition, 2(4), 300-311. doi:10.1080/13825589508256605
Benton, A. L. (1968). Differential behavioral effects in frontal lobe disease. Neuropsychologia, 6, 5360.
Birn, R. M., Kenworthy, L., Case, L., Caravella, R., Jones, T. B., Bandettini, P. A., & Martin, A. (2010). Neural systems supporting lexical search guided by letter and semantic category cues: A self-paced overt response fMRI study of verbal fluency. NeuroImage, 49(1), 1099-1107. doi:10.1016/j.neuroimage.2009.07.036
Catani, M., Mesulam, M. M., Jakobsen, E., Malik, F., Martersteck, A., Wieneke, C.,… Rogalski, E. (2013). A novel frontal pathway underlies verbal fluency in primary progressive aphasia. Brain, 136(8), 2619-2628. doi:10.1093/brain/awt163
Hughes, D. L., & Bryan, J. (2002). Adult Age Differences in Strategy Use During Verbal Fluency Performance. Journal of Clinical and Experimental Neuropsychology, 24(5), 642-654. doi:10.1076/jcen.24.5.642.1002
Jones-Gotman, M. & Milner, B. (1977). Design fluency: The invention of nonsense drawings after focal cortical lesions. Neuropsychologia, 15, 653-674.
Mathuranath, P. S., George, A., Cherian, P. J., Alexander, A., Sarma, S. G., & Sarma, P. S. (2003). Effects of Age, Education and Gender on Verbal Fluency. Journal of Clinical and Experimental Neuropsychology, 25(8), 1057-1064. doi:10.1076/jcen.25.8.1057.16736
Miller, E. (1984). Verbal fluency as a function of a measure of verbal intelligence and in relation to different types of cerebral pathology. British Journal of Clinical Psychology, 23(1), 53–57. doi:10.1111/j.2044-8260.1984.tb00626.x
Pendleton, M. G., Heaton. R. K.. Lehman, R. A. W. & Hulihan, D. (1982). Diagnostic utility of the Thurstone word fluency test in neuropsychological evaluation. Journal of Clinical Neuropsychology, 4, 307-3 17.
Rodriguez-Aranda, C., & Martinussen, M. (2006). Age-Related Differences in Performance of Phonemic Verbal Fluency Measured by Controlled Oral Word Association Task (COWAT): A Meta-Analytic Study. Developmental Neuropsychology, 30(2), 697-717. doi:10.1207/s15326942dn3002_3
Rosen, W. G. (1980). Verbal fluency in aging and dementia. Journal of Clinical Neuropsychology, 2(2), 135-146. doi:10.1080/01688638008403788
Thurstone. L. L. (1938). Primary Mental Abilities. Chicago: Chicago University Press
Tombaugh, T. N., Kozak, J., & Rees, L. (1999). Normative Data Stratified by Age and Education for Two Measures of Verbal Fluency: FAS and Animal Naming. Archives of Clinical Neuropsychology, 14(2), 167-177. doi:10.1016/S0887-6177(97)00095-4