This Article Abstract Full Text (PDF) Services Download to citation manager PubMed PubMed Citation

Change in Object Naming Ability During Adulthood

¹ Department of Radiology, Washington University School of Medicine, St. Louis, Missouri.
² Department of Neurology, Boston University and VA Boston Healthcare System, Massachusetts.
³ Epidemiology, Boston University and VA Boston Healthcare System, Massachusetts.
⁴ Program in Speech & Hearing Sciences, City University of New York.

Address correspondence to Lisa T. Connor, PhD, Washington University School of Medicine, Department of Radiology, Box 8225, 4525 Scott Avenue, St. Louis, MO 63110. E-mail: lconnor{at}npg.wustl.edu

	Abstract

TOP Abstract Methods Results Discussion References

 
Using longitudinal data on the Boston Naming Test ( Kaplan, Goodglass, & Weintraub, 1983) collected over 20 years from healthy individuals aged 30 to 94, we examined change in lexical retrieval with age, gender, education, and their interactions. We compared results between random-effects longitudinal and traditional cross-sectional models. Random-effects modeling revealed significant linear and quadratic change in lexical retrieval with age; it also showed a Gender x Education interaction, indicating poorest performance for women with less education. Cross-sectional analyses produced greater estimates of change with age than did longitudinal analyses.

Despite reports of word retrieval difficulties by older adults (Burke, Worthley, & Martin, 1988; Goodglass, 1980), a debate, beginning with a report by Borod, Goodglass, and Kaplan (1980), has played out over the past 20 years as to whether lexical retrieval decline is a normal part of the aging process. Many studies have reported evidence supporting age-related decline in lexical retrieval accuracy or speed of retrieval (these studies are denoted by an asterisk in the references). There are, however, several studies reporting either no age-associated decline in lexical retrieval or a nonsignificant trend toward decline with age (these studies are denoted by a dagger in the references).

Two review papers have attempted to resolve this ongoing discussion as to the nature of change in lexical retrieval abilities with age. Goulet, Ska, and Kahn (1994) conducted a qualitative review of 23 picture-naming studies to determine if there were age-related declines. They argued that factors such as differing age ranges, study designs, task demands, and statistical techniques across studies compromise the ability to unequivocally attribute age-related declines in picture naming to the aging of the lexical retrieval process per se. Feyereisen (1997) conducted a quantitative meta-analysis of the effect sizes in published studies to examine whether, despite the inconsistencies pointed out by Goulet, Ska, and Kahn, there was statistical evidence for age-related decline in lexical retrieval in confrontation naming tasks. On the basis of results from 11 (of 32) published studies that met his inclusion criteria, he concluded that decline in lexical retrieval began after a person reached the age of 70 years.

Although meta-analysis is a powerful technique, most of the published studies were not eligible for inclusion in Feyereisen's meta-analysis. Therefore, we turn in this study to an alternative empirical–statistical approach to determine whether lexical retrieval does decline in healthy older adults. We capitalized on more than 500 administrations of the Boston Naming Test that we have accumulated during a 20-year-long longitudinal study, using a relatively new statistical technique, random-effects modeling (e.g., Laird & Ware, 1982; Verbeke & Molenberghs, 2000). The advantage of this technique is that one can examine a collection of longitudinal observations made on individuals who vary both in the number of times evaluated and in the interval between evaluations. In addition, we compared age trends estimated from the random-effects approach with those obtained from the more commonly employed cross-sectional approach. Finally, we addressed questions of whether education or gender, factors that have been shown to influence lexical retrieval in prior studies (Borod et al., 1980; Neils et al., 1995; Welch, Doineau, Johnson, & King, 1996), influence the rate of change in lexical retrieval with age.

	METHODS

TOP Abstract Methods Results Discussion References

 
Sample
We recruited participants from the Greater Boston metropolitan area for a longitudinal study of age-related change in language abilities, the Language in the Aging Brain project, which began in 1979. Inclusion criteria were as follows: (a) no history of serious neurological incident, including head injuries (in the event of a head injury, a "serious" event is defined as any incident leading to loss of consciousness or a skull fracture); (b) no history of regular use of illegal drugs such as cocaine, LSD, or heroin; (c) no history of alcohol abuse; (d) no reported history of chronic psychological illness or symptoms of ongoing psychological disorders (e.g., depression); (e) no history of left-handedness or ambidexterity; and (f) being a native English speaker or acquiring English as a second language prior to the age of 6 years. In addition, participants passed an audiology examination for pure tone hearing level of 40 dB or better at three test frequencies (500, 1000, and 2000 Hz) in at least one ear. Participants demonstrated at least 20/40 corrected vision as determined by performance on a standard Snellen chart.

We initially recruited 160 participants from four defined age spans: 30–39 years old, 50–59 years old, 60–69 years old, and 70–80 years old. Within each age span, about half of the participants were men and about half were women. If participants dropped out at Time 2 or Time 3, we replaced them with persons of the same gender and age who met the inclusion criteria. After Time 3, we did not systematically recruit new participants to replace dropouts. We also screened participants at each test session and excluded some on the basis of new information about eligibility. For instance, if a participant experienced a stroke between visits, he or she was excluded from that time forward, though earlier data were included in the data set. Table 1 presents the sample sizes at each test session, stratified by gender.

Materials
We administered the BNT as part of a larger test battery at each of the five sessions. At Occasions 1–4, we used the 85-item experimental edition (Kaplan, Goodglass, & Weintraub, 1976). At Occasion 5, we administered the 60-item published version of the test (Kaplan et al., 1983). The scores reported are the percentage named correctly of the 60 items retained in the published version of the BNT (BNT %).

During the administration of the BNT, we asked participants to name simple line drawings of objects. If the participant was unable to name the item (e.g., accordion) as a result of an apparent difficulty in perceiving the object (for instance, said "It's a high-rise building"), then we gave a predetermined semantic cue (e.g., "It's a musical instrument," though the cue for the other items was not always a superordinate category name). If the response was semantically related but incorrect ("a piano"), then we gave an eliciting cue ("Is there another word for that?") to prompt for another response. If the participant continued to be unable to give a response or gave an incorrect response, then we provided a predetermined phonemic cue ("acc"). Responses were tape-recorded. We transcribed and scored all responses, both correct and incorrect. For more information about the types of errors that participants make and the degree to which they benefit from cueing, see MacKay, Connor, Albert, and Obler (2002) and Nicholas, Barth, Obler, Au, and Albert (1997).

	RESULTS

TOP Abstract Methods Results Discussion References

 
We first examined whether men and women differed in age and education, which are known to affect BNT scores (Borod et al., 1980; Neils et al., 1995; Welch et al., 1996). To the extent that such differences were found, we included them in our modeling of age-related change in BNT%. We found no gender difference in age (men, M = 58.3, SD = 15.2, vs women, M = 58.5, SD = 14.6, t(234) = 0.09 p >.9) or education (men, M = 14.4, SD = 2.5 vs women, M = 13.9, SD = 2.9, t(234) = 1.42, p >.15). When we compared initial BNT% performance between men and women, we found men to have a higher score than women, 92% versus 87%, t(237) = 4.02, p <.001. The zero-order correlation between age and BNT% for participants' first administration of the BNT (N = 238) was r = –.40.

We used the random-effects approach to model change in BNT% with age in order to make optimal use of the data available. These models estimate both fixed effects (which indicate how BNT% changes with age for the sample as a whole) and random effects (which indicate whether there are differences among persons in change). In other words, the fixed-effects portion of the model describes average change over time; the random-effects portion describes individual differences in change.

In our models, we centered age at 62, the mean across observations, and then converted the centered age to decades (i.e., age_c = (age – 62)/10). Thus, the coefficients for age indicate BNT% change per decade. We first compared linear and quadratic (age²) fixed and random effects of age. All models included education (centered at 12 years) and a dummy variable for gender (1 = female); interactions among gender, education, and age were also considered (results not shown because all of them were nonsignificant). On the basis of these preliminary comparisons, we estimated the following model:

The first six terms on the right side of the equation are the fixed-effects portion of the model, which describes BNT% performance for the sample as a whole; the last two terms (with a subscript i) refer to the random-effects portion of the model, indicating that individual participants were allowed to deviate from the overall sample curve in both level (intercept_i) and linear rate of change with age (age_ci). That is, a given participant could have a predicted BNT% (at age 62, the intercept_i) that was higher or lower than the sample as a whole, and the participant could also have a rate of change in BNT% (age_ci) that differed from the sample average. In other words, random-effects models allow persons to show different average levels and to change at different rates.

The model provided an acceptable fit to the data (–2 log likelihood = 3508.2); the residual variance was 19.12. Estimates of fixed and random effects are shown in Table 2. Examining the fixed effects, we see that the intercept of 91.58% indicates the predicted value of BNT% at age 62 for men with 12 years of education; the effects for education, gender, and the Gender x Education interaction are the predicted differences from this value for women and for those with more (or less) than 12 years of education. The age effects indicate the linear and quadratic change in BNT% per decade; these effects were constant across gender and education (i.e., there were no interactions of age with either gender or education).

Using the fixed effects estimates, we computed predicted BNT trajectories separately for men and women with different educational levels; they are shown in Figure 1. The trajectories are based on the following equation, computed from Table 2, where we have adjusted the intercepts to include the effects of gender, education, and their interaction:

where k = 92.70 for men with 16 years education, k = 92.61 for men with 12 years education, k = 90.87 for women with 16 years education, and k = 86.25 for women with 12 years education. As specified by the model, the curves for the various gender and education groups are parallel (because there were no interactions of age with either education or gender). Note that BNT% scores for women with 12 years of education were 5 to 6 percentage points lower than the other groups' scores.

View larger version (14K): [in this window] [in a new window]   Figure 1. Predicted BNT% (percentage of items named correctly in the Boston Naming Test) by gender and education: random-effects model

View larger version (14K): [in this window] [in a new window]   Figure 2. Predicted BNT% (percentage of items named correctly in the Boston Naming Test) by gender and education: cross-sectional model

	DISCUSSION

TOP Abstract Methods Results Discussion References

With regard to the second question, we found that education affects naming, which is not surprising (Borod et al., 1980; Neils et al., 1995; Welch et al., 1996), but our results did not produce the Age x Education interaction reported by Welch and colleagues. Somewhat more surprising to us was that the interaction of gender and education was a predictor of performance. Though the rate of change in naming ability was not gender dependent, the average score was lower for women than for men, particularly for women with less education. This finding, though not unprecedented for the BNT (e.g., Fastenau, Denburg & Mauer, 1998), is by no means universal; however, we can offer no good explanation for it, and we offer this topic as relevant for future research.

With regard to our third question, our comparison of longitudinal and cross-sectional models of change in naming revealed notable differences. Specifically, the cross-sectional model produced larger estimates for change with age—a finding that has been reported by others for the BNT (e.g., Cruice et al., 2000) and for other measures of cognitive performance (e.g., Hultsch, Hertzog, Small, McDonald-Miszczak & Dixon, 1992; McCarty, Siegler, & Logue, 1982; cf. Zelinski & Burnight, 1997). Those individuals who remained in the study may have benefited from practice effects that were due to taking the test several times, whereas those who dropped out may have been more likely to experience difficulties in naming.

We considered the first possibility by comparing, at Exams 1–4, those who were first tested at that time with those tested previously; we found no differences in BNT%. We considered potential dropout effects by comparing BNT% between those who had only one assessment with those who had more, and we found no differences; we also compared, at each of the first three exams, those who were subsequently retested and those who were not, and we found no differences in BNT%. These findings demonstrate that practice effects and selective dropout effects are unlikely explanations for the cross-sectional model overestimating change with age. Cohort effects, however, may be responsible for the difference between cross-sectional and longitudinal estimates of change.

Why do older adults experience accelerating decline in lexical retrieval abilities? Accelerating decline with advancing age is not unique in the field of cognitive aging. In fact, the psychometric literature is full of examples of this pattern. Salthouse, Fristoe, and Rhee (1996) provided a striking example, showing remarkably similar trajectories of accelerating decline for measures of perceptual speed, episodic memory, inductive reasoning, and spatial visualization. Because this pattern is ubiquitous in cognitive aging, it may require a more general explanation rather than one that focuses on decline in lexical retrieval specifically. In their review of methodological approaches to assessing psychological change in adulthood, Hertzog and Nesselroade (2003) reviewed evidence for terminal decline as a model of cognitive development. Rabbitt and colleagues (2002) reported variables that predict time to death. Interestingly, although the correlation between age and verbal ability (the ability to accurately define "rare" words) was not significant over all participants, decline in verbal ability was a predictor of death. Our findings may reflect the performance of older participants who are experiencing terminal decline and are thus drawing the overall distribution down at the end of the age distribution. We know that there are individual differences in rate of change, based on the significant random effects portion of our model; this might indicate that older individuals are showing greater decline. Unfortunately, the issue of terminal decline must remain unaddressed because we have followed few participants until their death.

Another account of cognitive decline in old age is focused at the level of changes in the neural substrate of cognition. Cumulative health burdens may produce decline in cognitive processing in older adults (Hassing et al., 2002; MacDonald, Dixon, Cohen, & Hazlitt, 2004). For example, Rubin and colleagues (1998) argued that healthy older adults measured longitudinally on an array of cognitive measures maintained their level of performance until they developed a dementing illness, after which they showed a rapid rate of decline. Though this could explain decline on the BNT, a recent study by MacKay, Connor, and Storandt (in press) showed that dementia does not explain the negative correlation between age and BNT in a sample of older adults screened annually for dementia. Moreover, the magnitude of the correlation in the study by MacKay and colleagues (r = –.36) was similar to the zero-order correlation found in the current study (r = –.40).

Another approach to examining the effects of health on cognition is to measure risk factors for cerebrovascular disease, best demonstrated by Brady and colleagues (Brady, Spiro & Gaziano, 2004; Brady, Spiro, McGlinchey-Berroth, Milberg, & Gaziano, 2001). Their findings suggest that frontally mediated cognitive functions such as verbal fluency, working memory, and memory retrieval are negatively influenced by the accumulation of stroke risk factors throughout the life span. To address the issue of stroke risk factors and decline in language abilities in future work, we are currently collecting self-reported health information and objective measures of cerebrovascular disease risk in participants whom we continue to follow.

An interesting finding in our study was that individuals who had high levels of performance showed less decline over time. This result is consistent with some other findings in the literature (e.g., Rubin et al., 1998) that are often cited as supporting the cognitive reserve hypothesis (e.g., Satz, 1993; Scarmeas & Stern, 2003). The cognitive reserve hypothesis posits that some combination of high levels of intelligence, education, and a cognitively active lifestyle offer a neuroprotective benefit. That is, individuals who have these characteristics either possess more brain reserve or are better able to compensate for pathological brain abnormalities that accumulate throughout life.

Finally, there are a few limitations of this study worth noting. The fact that our sample was virtually all Caucasian suggests that these findings may not be generalizable beyond this group (see Fillenbaum, Huber, & Taussig, 1997). Further, as we aimed to screen out individuals with medical or psychological histories that might themselves diminish naming abilities, our results cannot pretend to generalize to the population of all older adults. Because we eliminated individuals with medical or psychological illnesses, our findings, more likely than not, underestimate the true extent of decline in naming abilities across the life span. A final limitation is that we used one type of object naming test, the BNT, as a measure of lexical retrieval abilities. Older adults frequently report retrieval failures in proper name retrieval at higher rates than naming of objects. Therefore, we anticipate that using other tests might reveal a more or less severe naming impairment. Nonetheless, we believe that the BNT taps an important object naming ability that adequately represents lexical retrieval abilities as a whole. These data are valuable as a first approximation because the BNT is widely used in both neuropsychology and speech-language pathology as a measure of lexical retrieval abilities.

It is also important to frame our findings within the realm of clinical experience. The decline in lexical retrieval is extremely subtle. It may be, therefore, many years before decline in individuals' lexical retrieval abilities becomes measurable by standard techniques in the laboratory, even though naming lapses may be troubling to them. Clearly the next steps are to further explore factors associated with aging, such as stroke risk, that bring about the decline in lexical retrieval abilities in most, but not all, individuals, establish whether rates of change are similar or different across measures of language functioning and among other cognitive abilities, and determine what accounts for individual differences in the timing and rate of decline.

	Acknowledgments

 
Support for this research was provided by the National Institutes of Health under Grants AG 14345 and DC 00081 and by the U.S. Department of Veterans Affairs, Medical Research Service. We thank Mira Goral, PhD, and Anna MacKay for their assistance.

	Footnotes

 
^* Evidence supporting age-related decline in lexical retrieval accuracy or speed of retrieval.

Studies reporting either no age-associated decline in lexical retrieval or a nonsignificant trend toward decline with age.

Decision Editor: Elizabeth Stine-Morrow, PhD

Received for publication July 11, 2002. Accepted for publication May 4, 2004.

	References

TOP Abstract Methods Results Discussion References

 

• ^* Albert, M. S., Heller, H. S., Milberg, W. (1988). Changes in naming ability with age. Psychology and Aging, 3,173-178.[Medline]
• ^* Ardila, A., Rosselli, M. (1989). Neuropsychological characteristics of normal aging. Developmental Neuropsychology, 5,307-320.
• ^* Au, R., Joung, P., Nicholas, M., Obler, L. K., Kass, R., Albert, M. L. (1995). Naming ability across the adult life span. Aging and Cognition, 2,300-311.
• ^* Barresi, B. A., Nicholas, M., Connor, L. T., Obler, L. K., Albert, M. L. (2000). Semantic degradation and lexical access in age-related naming failures. Aging, Neuropsychology, and Cognition, 7,1-10.
• Béland, R., Lecours, A. R. (1990). The MT-86 Aphasia Battery: A subset of normative data in relation to age and school education. Aphasiology, 4,439-462.
• ^* Borod, J. C., Goodglass, H., Kaplan, E. (1980). Normative data on the Boston Diagnostic Aphasia Examination, Parietal Lobe Battery, and the Boston Naming Test. Journal of Clinical Neuropsychology, 2,209-215.
• Brady, C. B., Spiro, A., III, Gaziano, J. M. (2004). Effects of age and hypertension status on cognition: The VA Normative Aging Study. Manuscript under review.
• Brady, C. B., Spiro, A., III, McGlinchey-Berroth, R., Milberg, W., Gaziano, J. M. (2001). Stroke risk predicts verbal fluency decline in healthy older men: Evidence from the Normative Aging Study. Journal of Gerontology: Psychological Sciences, 56B,P340-P346.
• ^* Bowles, N. L., Obler, L. K., Albert, M. L. (1987). Naming errors in healthy aging and dementia of the Alzheimer type. Cortex, 23,519-524.[Medline]
• Burke, D. M., MacKay, D. G., Worthley, J. S., Wade, E. (1991). On the tip of the tongue: What causes word finding failures in young and older adults? Journal of Memory and Language, 30,542-579.
• Burke, D. M., Worthley, J. S., Martin, J. (1988). I'll never forget what's-her-name: Aging and tip of the tongue experiences in everyday life. In M. M. Gruneberg, P. E. Morris, & R. N. Sykes (Eds.), Practical aspects of memory: Current research and issues (pp. 113–118). Chichester, England: Wiley.
• Cruice, M. N., Worrall, L. E., Hickson, L. M. (2000). Boston Naming Test results for healthy older Australians: A longitudinal and cross-sectional study. Aphasiology, 14,143-155.
• ^* Dordain, M., Nespoulous, J. L., Bourdeau, M., Lecours, A. R. (1983). Capacités verbales d'adultes normaux soumis à un protocole linguistique de l'aphasie [Verbal capacities of normal adults on a linguistic test for aphasia]. Acta Neurologica Belgica, 83,5-16.[Medline]
• Farmer, A. (1990). Performance of normal males on the Boston Naming Test and the word test. Aphasiology, 4,293-296.
• Fastenau, P. S., Denburg, N. L., Mauer, B. A. (1998). Parallel short forms for the Boston Naming Test: Psychometric properties and norms for older adults. Journal of Clinical and Experimental Neuropsychology, 20,828-834.[Medline]
• Feyereisen, P. (1997). A meta-analytic procedure shows and age-related decline in picture naming: Comments on Goulet, Ska, and Kahn (1994). Journal of Speech, Language, and Hearing Research, 40,1328-1333.[Abstract/Free Full Text]
• Fillenbaum, G. G., Huber, M., Taussig, I. M. (1997). Performance of elderly White and African American community residents on the abbreviated CERAD Boston Naming Test. Journal of Clinical and Experimental Neuropsychology, 19,204-210.[Medline]
• Flicker, C., Ferris, S. H., Crook, T., Bartus, R. T. (1987). Implications of memory and language dysfunction in the naming deficit of senile dementia. Brain and Language, 31,187-200.[Medline]
• Goodglass, H. (1980). Naming disorders in aphasia and aging. In L. K. Obler & M. L. Albert (Eds.), Language and communication in the elderly (pp. 37–45). Toronto: Lexington Books.
• Goulet, P., Ska, B., Kahn, H. J. (1994). Is there a decline in picture naming with advancing age? Journal of Speech and Hearing Research, 37,629-644.
• Hassing, L. B., Johansson, B., Berg, S., Nilsson, S. E., Pedersen, N. L., Hofer, S. M., McClearn, G. E. (2002). Terminal decline and markers of cerebro- and cardiovascular disease: Findings from a longitudinal study of the oldest old. Journal of Gerontology: Psychological Sciences, 57B,P268-P276.
• Hertzog, C., Nesselroade, J. R. (2003). Assessing psychological change in adulthood: An overview of methodological issues. Psychology and Aging, 18,639-657.[Medline]
• Hultsch, D. F., Hertzog, C., Small, B. J., McDonald-Miszczak, L., Dixon, R. A. (1992). Short-term longitudinal change in cognitive performance in later life. Psychology and Aging, 7,571-584.[Medline]
• Kaplan, E., Goodglass, H., Weintraub, S. (1976). The Boston Naming Test: Experimental Edition. Unpublished manuscript.
• ^* Kaplan, E., Goodglass, H., Weintraub, S. (1983). The Boston Naming Test. Philadelphia: Lea & Febiger.
• LaBarge, E., Edwards, D., Knesevich, J. W. (1986). Performance of normal elderly on the Boston Naming Test. Brain and Language, 27,380-384.[Medline]
• Laird, N. M., Ware, J. H. (1982). Random effects models for longitudinal data. Biometrics, 38,963-974.[Medline]
• ^* Le Dorze, G., Durocher, J. (1992). The effects of age, educational level, and stimulus length on naming in normal subjects. Journal of Speech-Language Pathology and Audiology, 16,21-29.
• MacDonald, S. W., Dixon, R. A., Cohen, A. L., Hazlitt, J. E. (2004). Biological age and 12-year cognitive change in older adults: Findings from the Victoria Longitudinal Study. Gerontology, 50,64-81.[Medline]
• ^* MacKay, A. J., Connor, L. T., Albert, M. L., Obler, L. K. (2002). Noun and verb retrieval in healthy aging. Journal of the International Neuropsychological Society, 8,764-770.
• ^* MacKay, A. J., Connor, L. T., Storandt, M., in press Dementia does not explain correlation between age and scores on Boston Naming Test. Archives of Clinical Neuropsychology..
• McCarty, S. M., Siegler, I. C., Logue, P. E. (1982). Cross-sectional and longitudinal patterns of three Wechsler Memory Scale subtests. Gerontology, 37,169-175.
• Neils, J., Baris, J. M., Carter, C., Dell'aira, A. L., Nordloh, S. J., Weiler, E., et al. (1995). Effects of age, education, and living environment on Boston Naming Test performance. Journal of Speech and Hearing Research, 38,1143-1149.
• Nicholas, L. E., Brookshire, R. H., MacLennan, D. L., Schumacher, J.G., Porrazzo, S. A. (1989). Revised administration and scoring procedures for the Boston Naming Test and norms for non-brain-damaged adults. Aphasiology, 3,569-580.
• Nicholas, M., Barth, C., Obler, L. K., Au, R., Albert, M. L. (1997). Naming in normal aging and dementia of the Alzheimer's type. In H. Goodglass & A. Wingfield (Eds.), Anomia: Neuroanatomical and cognitive correlates (pp. 166–188). San Diego, CA: Academic Press.
• ^* Nicholas, M., Obler, L. K., Albert, M. L., Goodglass, H. (1985). Lexical retrieval in healthy aging. Cortex, 21,595-606.[Medline]
• ^* Ramsay, C. B., Nicholas, M., Au, R., Obler, L. K., Albert, M. L. (1999). Verb naming in normal aging. Applied Neuropsychology, 6,57-67.[Medline]
• Rabbitt, P., Watson, P., Donlan, C., McInnes, L., Horan, M., Pendleton, N., Clague, J. (2002). Effects of death within 11 years on cognitive performance in old age. Psychology and Aging, 17,468-481.[Medline]
• Rubin, E. H., Storandt, M., Miller, J. P., Kinscherf, D. A., Grant, E. A., Morris, J. C., et al. (1998). A prospective study of cognitive function and onset of dementia in cognitively healthy elders. Archives of Neurology, 55,395-401.[Abstract/Free Full Text]
• Salthouse, T. A., Fristoe, N., Rhee, S. H. (1996). How localized are age-related effects on neuropsychological measures? Neuropsychology, 10,272-285.
• Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. Neuropsychology, 7,273-295.
• Scarmeas, N., Stern, Y. (2003). Cognitive reserve and lifestyle. Journal of Clinical and Experimental Neuropsychology, 25,625-633.[Medline]
• ^* Thomas, J. C., Fozard, J. L., Waugh, N.C. (1977). Age-related differences in naming latency. American Journal of Psychology, 90,499-509.
• ^* Thuillard-Colombo, F., Assal, G. (1992). Adaptation française du test de dénomination de Boston. Versions abrégées. [French adaptation of the Boston Naming Test: Abridged version]. Revue Européene de Psychologie Appliquée, 42,67-71.
• Van Gorp, W. G., Satz, P., Kiersch, M. E., Henry, R. (1986). Normative data on the Boston Naming Test for a group of normal older adults. Journal of Clinical and Experimental Neuropsychology, 8,702-705.[Medline]
• Verbeke, G., Molenberghs G. (2000). Linear mixed models for longitudinal data. New York: Springer.
• Villardita, C., Cultrera, S., Cupone, V., Mejia, R. (1985). Neuropsychological test performances and normal aging. Archives of Gerontology and Geriatry, 4,311-319.
• ^* Welch, L. W., Doineau, D., Johnson, S., King, D. (1996). Educational and gender normative data for the Boston Naming Test in a group of older adults. Brain and Language, 53,260-266.[Medline]
• ^* Worrall, L. E., Yiu, E., Hicksson, L. M. H., Barnett, H. M. (1995). Normative data for the Boston Naming Test for Australian elderly. Aphasiology, 9,541-551.
• Zelinski, E. M., Burnight, K. P. (1997). Sixteen-year longitudinal and time lag changes in memory and cognition in older adults. Psychology and Aging, 12,503-513.[Medline]

This Article Abstract Full Text (PDF) Services Download to citation manager PubMed PubMed Citation