The Stroop Effect in Greek Healthy Population: Normative Data for the Stroop Neuropsychological Screening Test

Transcription

1 Archives of Clinical Neuropsychology 24 (2009) The Stroop Effect in Greek Healthy Population: Normative Data for the Stroop Neuropsychological Screening Test Ioannis Zalonis*, Fotini Christidi, Anastasios Bonakis, Evangelia Kararizou, Nikolaos I. Triantafyllou, Georgios Paraskevas, Elissavet Kapaki, Dimitrios Vasilopoulos Department of Neurology, Aeginition Hospital, Medical School, National University of Athens, Athens, Greece Accepted 4 December 2008 Abstract The Stroop Test is a quick and frequently used measure in screening for brain damage, dysfunction of selective attention, and cognitive flexibility. The purpose of the present study is to provide normative data for Trenerry s Stroop Neuropsychological Screening Test (SNST) in a sample of 605 healthy Greek participants (age range: years, education range: 6 18 years). Results revealed that age and education significantly contributed to SNST scores, accounting for a significant proportion of variance in time needed to complete the color task and in the interference Color Word score. Performance on most of the measures decreases with increasing age and lower levels of education. Normative data stratified by age and education for the Greek adult population are provided as a useful set of norms for clinical practice. Keywords: Stroop Neuropsychological Screening Test; Normative data; Selective attention; Cognitive flexibility Introduction The Stroop test is a brief and useful instrument for the examination of patients with dysfunction of mental activity variables, concentration effectiveness, and cognitive flexibility (Lezak, Howieson, & Loring, 2004; Spreen & Strauss, 1998). It assesses the speed of reading names of colors. It is also a measure of executive function, requiring the subject to inhibit an overlearned response in favor of an unusual one (Mitrushina, Boone, Razani, & D Elia, 1999; Spreen & Strauss, 1998). Since its original introduction, many versions have been developed (e.g. Comalli and Kaplan Stroop, 1962; Dodrill Stroop, 1978; Golden Stroop, 1978; Trenerry, Crosson, DeBoe, & Leber, 1989; Regard, 1981). Mitrushina and coworkers (1999) provide a detailed review of the different versions of the Stroop test. Despite the fact that the versions differ in many ways (e.g. the number of cards used or the number of items on each card), the Color Word interference effect called the Stroop effect remains the same. Age- and education-related influences on Stroop performance have been found, whereas sex differences are inconsistently reported (Spreen & Strauss, 1998). Most studies have provided strong evidence on age-related decrement in Stroop performance (Hameleers et al., 2000; Ivnik, Malec, Smith, Tangalos, & Petersen, 1996; Klein, Ponds, Houx, & Jolles, 1997; Trenerry et al., 1989; Van Boxtel, Ten Tusscher, Metsemakers, Willems, & Jolles, 2001), with advanced age being associated with lower performance not only in the interference task, but also in color naming (Cohn, Dustman, & Bradford, 1984). Although, to a lesser extent, education has been positively related to Stroop performance (Hameleers et al., 2000; Moering, Schinka, Mortimer, & Graves, 2003; Van Boxtel et al., 2001). However, Trenerry and coworkers (1989) did not report any significant influence of level of education on their participants scores. The effect of gender is rather controversial. Some authors have reported an absence of gender effect or only minimal influence, resulting in normative data combined for males and * Corresponding author at: Neuropsychological Laboratory, Department of Neurology, Aeginition Hospital, Medical School, National University of Athens, 72 74, Vas. Sophias Avenue, Athens, Greece. Tel.: þ ; fax: þ address: zalonis@eginitio.uoa.gr # The Author Published by Oxford University Press on behalf of the National Academy of Neuropsychology. All rights reserved. For permissions, please journals.permissions@oxfordjournals.org. doi: /arclin/acp011 Advance Access publication on 12 March 2009

2 82 I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) females (Houx, Vreeling, & Jolles, 1993; Ivnik et al., 1996; Klein et al., 1997; Trenerry et al., 1989), while others have found differences which particularly favor females (Hameleers et al., 2000; Moering et al., 2003; Van Boxtel et al., 2001). The Stroop test is particularly useful in clinical neurological samples, such as patients with brain injury (Seignourel, Robins, Larson, Demery, Cole, & Perlstein, 2005), demyelinating diseases (Jønsson, Andresen, Storr, Tscherning, Soelberg, & Ravnborg, 2006), and degenerative syndromes (Collette et al., 2007). Trenerry and coworkers (1989) developed the Stroop Neuropsychological Screening Test (SNST) to replace the Stroop Color and Word Test developed by Golden (1978). The SNST, based on previous research of Nehemkis and Lewinsohn (1972) incorporates only the Color and Color Word tasks of the traditional Stroop procedure, which provide the most sensitive measures in discriminating subjects with brain damage from normal controls (Trenerry et al., 1989). In the present study, we evaluate the influence of demographic variables on Trenerry s Stroop version (SNST; Trenerry et al., 1989) in order to establish the normal range of performance data for the Greek adult population. Materials and Methods Participants Seven hundred volunteer participants were recruited; all of whom reported that Greek was their dominant language. Accompanying friends and family members of patients undergoing day neuropsychological assessment at Aeginition Hospital, employees in public and private sector organizations, college students, as well as subjects informed by word of mouth were interviewed. Exclusion criteria (based on self-reports and medical history of each participant) were a history of probable neurological or psychiatric problems, a closed head injury, medical treatment that could impair cognitive functions, and/or drug or alcohol abuse, color-blindness, visual disturbances, or effortful verbal response that may affect verbal speech production. Before SNST administration, participants who scored below the 2nd percentile rank on Trail Making Test (Zalonis et al., 2008) and those over 50 years of age with a Mini-Mental State Examination score lower than 24 (Folstein, Folstein, & McHugh, 1975) were excluded too. Moreover, during the pre-examination interview, the participants were asked for any visual problems and the use of proper eye glass-correction. Ninety-five subjects were excluded: 24 treated for coronary diseases, 11 with drug or insulin-dependent diabetes mellitus, 9 who did not bring their glasses, 9 with less than 24 score on Mini-Mental State Examination, 8 with a history of head injuries, 7 treated with antidepressant medication, 5 who reported heart attack, 5 with scores lower than the 2nd percentile rank on Trail Making Test, 4 with a history of transient ischemic attack, 4 with a history of alcohol abuse, 3 with mild extrapyramidal symptoms, 3 with impaired color vision (dyschromatopsia), 2 with a history of epileptic episodes, and one subject with abnormal speech and prominent dysarthria. Thus, the study included 605 participants (337 men, or 55.7%), aged between 18 and 84 years (M ¼ 53.72, SD ¼ 16.27) and with a level of education achieved between 6 and 18 years duration (M ¼ 11.69, SD ¼ 3.64). Our total sample was finally divided into 15 subgroups (Table 1). In our sample, 94.05% of the participants were right-handed (n ¼ 569), 4.13% left-handed (n ¼ 25), and 1.82% were ambidextrous (n ¼ 11). The majority of them (78%) came from urban and suburban centers, while a small percentage (22%) came from rural areas of Greece. All participants were first informed and then provided a written informed consent for participation in the study, whose protocol was approved by the local area ethics committee. Materials and Procedure All participants were tested individually during morning hours at the Neuropsychological Laboratory of Aeginition Hospital. They were administered the Trenerry s SNST, which consists of two tasks, namely Color and Color Word (Trenerry et al., 1989). The consists of a sheet of 112 printed Color names (red, green, blue, tan) arranged in four columns of 28 names. The names are printed in one of the four different Colors of ink (red, green, blue, tan), but no name is printed in a matching Color (e.g. the name RED is never printed in red ink). The participant is required to read the words aloud as quickly as possible (irrespective of the Color of ink in which they are printed), within a time limit of 120 s. According to Trenerry and coworkers (1989), the ends once the time of 120 s is elapsed. The sheet is similar to that of the, except for the rearrangement of the Color names. In the, the participant is required to name aloud, as quickly as possible, the Color of the ink in which the word is printed, and is allowed up to 120 s. In case of successful completion of the before the time of 120 s has been elapsed, the total number of items completed is recorded. The Greek words for red, green, blue, and tan are presented in Appendix. The participants were instructed to read the words as quickly as possible in the and then, to name the Color of the printed words as quickly and as accurately as they can in the Color Word task. Following the instructions of Trenerry and coworkers (1989), before administering the two tasks, we examined the participants ability to identify accurately the four

3 I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) Table 1. Normative data for the Stroop Neuropsychological Screening Test stratified by age and level of education Years of education M SD M SD M SD Age group n Age Education Time (s) Errors Self-corrections Interference score Age group n Age Education Time (s) Errors Self-corrections Interference score Age group n Age Education Time (s) Errors Self-corrections Interference score Age group n Age Education Time (s) Errors Self-corrections Interference score Age group n Age Education Time (s) Errors Self-corrections Interference score colors by asking them to identify the color of common objects in the testing setting. Four variables were measured: in the Color task (i) the time needed to read the 112 items; and in the (ii) the number of errors; (iii) the number of selfcorrections; and (iv) the interference score, calculated by subtracting the number of errors from the total number of items completed in 120 s. The Color Score is the number of items completed minus the incorrect responses on the Color Task. Trenerry and coworkers (1989) provided mean scores and standard deviations for items completed, incorrect responses and color score

4 84 I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) on the Color Task. Unlike Trenerry, on the Color Task we have reported only the time needed to read the 112 items, as none of our participants failed to complete the and none of them made errors and self-corrections. All tests were administered and scored by licensed psychologists of Graduate level, who had been trained in the administration and interpretation of several neuropsychological tests (including SNST) by a doctoral level clinical neuropsychologist. Results The SNST variables and demographic characteristics (age, education, and sex) were examined in the statistical analysis. The Kolmogorov Smirnov test for normality was applied to determine whether our data met normality requirements. Mainly nonparametric tests were employed, because most of our variables did not reach normality. To assess the degree of association between SNST variables, a Spearman rank order coefficient was calculated. A series of linear regression analyses (enter method) were used to more directly examine the relative contribution of demographic variables (age, sex, years of education) on each SNST measure. Kruskal Wallis test followed by individual Mann Whitney U-tests with Bonferroni correction for multiple comparisons was conducted to determine the equality of means of SNST measures at different levels of age and education. We also employed Levene s test in order to investigate the equality of variances. The level of statistical significance was set at a ¼.05. All analyses were conducted using the SPSS 16.0 software. Correlation analyses among SNST variables revealed that increasing time to complete the was associated with more errors (r ¼.261, p,.01) and self-corrections (r ¼.114, p,.01) in the Color Word task, as well as decreasing interference score (r ¼ 2.665, p,.01). Moreover, as interference score increases, the Color Word scores for errors (r ¼ 2.346, p,.01) and self-corrections (r ¼ 2.189, p,.01) decreases. A series of linear regression analyses (Table 2) showed that age and education contributed to the time needed to complete the [F(3, 601) ¼ , p,.001], and the interference score [F(3, 601) ¼ , p,.001]. Those participants who were younger and had spent more years in education, performed better in the earlier measures. Age and education also significantly affected the errors made in the Color Word task [F(3, 601) ¼ , p,.001]. Only age contributed significantly to the self-corrections [F(3, 601) ¼ 5.714, p,.001], with younger participants having fewer self-corrections in the. Since gender did not influence the SNST performance, we combined the data of men and women in subsequent analyses. These results for the contribution of demographic variables indicate that age and education, but not gender, need to be considered in an accurate interpretation of SNST performance. For the potential age effect on SNST performance, we adopted the procedure of Trenerry and coworkers (1989). In accordance with Trenerry, we first broke down our sample into subgroups by decade of age. We observed a relatively consistent level of performance in participants aged years, as well as those aged years in the interference score (Fig. 1). However, Mann Whitney test showed that the and age groups did not differ regarding the mean time needed to complete the (p..05) nor their mean interference performance (p..05). Non-parametric post hoc analyses revealed a significant difference for both and Color Word task (interference) scores between any of the two groups among 40 49, 50 59, 60 69, and group (p,.01). Thus, we included all participants between 18 and 39 years of age in one group (n ¼ 131), and consider the other four groups as independent age groups: (n ¼ 103), 50 Table 2. Contribution of age, education, and gender to Stroop Neuropsychological Screening Test (SNST) performance SNST Independent variables Standardized beta t p R 2 Time age ,.001 education ,.001 gender Errors age ,.001 education gender Self-corrections age ,.001 education gender Interference score age ,.001 education ,.001 gender

5 I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) Fig. 1. Scatter plot demonstrating Stroop Neuropsychological Screening Test Color Word total score by age. 59 (n ¼ 134), (n ¼ 116); (n ¼ 120). Although the normality assumptions were not met, analysis of variance followed by Newman Keuls post hoc tests gave essentially the same results. The total sample was also divided into three strata of educational levels, as this stratification reflects the levels of education in Greece: 6 9 (compulsory education in Greece is 9 years), (high school), and 13 years and above (higher education, technological, or other university level education). Statistical analysis revealed significant differences between any of the two groups among the 6 9, 10 12, and group for the interference score in the (p,.005). Regarding the time needed to read the 112 items in the, all but one comparison reached significance at p,.05; the secondary level group (10 12 years of education) did not differ significantly from the university level group (13 18 years of education). As the most important score in clinical neurological practice is the interference score, we decided to stratify our sample into the abovementioned levels of education: 6 9 years of education (n ¼ 190); years of education (n ¼ 195); years of education (n ¼ 219). Table 1 presents normative data for SNST measures stratified by age and level of education. Discussion We provide normative data for a large sample of healthy Greek adults using Trenerry s version of the Stroop test, stratified by the demographic variables that contributed to the SNST performance. Between the different scores in SNST, the more time needed to read the 112 items in the, the lower the interference score in the Color Word task. Moreover, the more errors and self-corrections made in the Color Word task, the lower the score that represents the interference effect. The results of our study reveal that age and education significantly influenced performance on most SNST scores, as they accounted for a significant proportion of the variance on score (time to complete the task) and Color Word interference score. Age appears to be the most influential demographic variable, as it contributed to most of the SNST scores, and to a greater extent than did education. As age increases, participants require more time to read the words in the and show a poorer performance on the Stroop interference score. Our age-related influence is in line with that of Trenerry and coworkers (1989), who reported that younger participants outperformed older ones. Nevertheless, our stratification is based on different subgroups from Trenerry s ones; we observed that participants aged between 18 and 39 years performed in a similar way on score and interference score on the Color Word task, while mean performance significantly declined

6 86 I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) decade-by-decade for the rest of the age subgroups. The mean scores of our participants are partially comparable with those reported by Trenerry and coworkers (1989); using the same age-dependent groups, mean performance of our group on the Color Word task (interference score; M ¼ , SD ¼ 9.02) is comparable with the performance of Trenerry s group (M ¼ , SD ¼ 10.22), with small effect size (Cohen s d ¼ 2.29). However, in the original group aged over 50 years (n ¼ 50), the mean interference score is (SD ¼ 18.41), whereas among our healthy controls aged over 50 (n ¼ 371) the mean interference score is (SD ¼ 16.89). This difference between two groups (Cohen s d ¼ 21.61, which reflects a large effect size) may be discussed under the view of the number of Greek participants over 50 years old. However, any definitive interpretation of this difference would be made only with well-matched samples. In accordance with Trenerry s results, none of our participants failed to complete the 112 items on the, while the occurrence of incorrect responses in the Color Word task was rare. Previous standardization studies on various versions of the Stroop test have reported that age is a strong predictor of Stroop performance (Hameleers et al., 2000; Ivnik et al., 1996; Klein et al., 1997; Moering et al., 2003; Van Boxtel et al., 2001). In adults, aging appears to be linked to a slowing of speed in color-naming, and an increase in the Stroop interference effect (Spreen & Strauss, 1998). Some authors support the hypothesis that the difficulty experienced by older participants with regard to inhibitory control, namely the interference effect, reflects an age-related generalized slowing (Uttl & Graf, 1997), while others report that an age-related deficit in inhibitory control produces a greater Stroop interference as age increases (Spieler, Balota, & Faust, 1996). A third group supports both the hypotheses mentioned earlier (Bugg, DeLosh, Davalos, & Davis, 2007). In the present study, education was also an important factor in all but one SNST score, contrary to Trenerry s findings. According to our data, in all age-dependent groups, less-educated healthy participants showed slower color-naming rates and need more time to complete the. They also presented a greater interference effect, as they provided poorer performances on the Color Word task than those who were highly educated. Our findings are similar to those of other studies that documented a significant relationship between years of education and Stroop performance (Houx et al., 1993; Moering et al., 2003; Van Boxtel et al., 2001). Among participants aged over 70, those who are highly educated still performed better than those who have attained a lesser degree of education. This finding may be explained by the cognitive reserve hypothesis, according to which, some factors such as a high level of education can render a person less vulnerable to age-related cognitive decline and pathological brain processes (Van der Elst, Van Boxtel, Van Breukelen, & Jolles, 2006). Despite the age- and education-related influence, we found no correlation between gender and SNST performance. Our present finding, regarding the absence of sex differences, is in line with past studies, which have provided similar results (Houx et al., 1993; Ivnik et al., 1996). Moreover, despite the reported fact that women tend to have superior color naming or word reading (Strickland, D Elia, James, & Stein, 1997), in our study we did not find any significant differences that favored females. In our study, given that the resulting normative cells included an adequate sample size (52 n 31), we decided not to adopt the stratification method of overlapping cell tables. The use of overlapping cell norm tables has been proposed in case of constructing norm tables that have a relatively large n in each subcategory cell, but for which the limited total sample size limits the number of cells and dictates for each cell a very wide range of the modifier variables. It is also useful in case of constructing norm tables in which the ranges of the modifier variables are narrower, but for which the limited total sample size severely limits the size of the n for each cell (Pauker, 1988). Furthermore, we did not examine the relative effect of IQ on Stroop performance, the reason being that in a recent Greek standardization study of Trail Making Test [a sensitive instrument for the assessment of complex attention, information processing speed, and executive function (Lezak et al., 2004)], significant but small correlations emerged between the general level of intelligence and performance on the Trail Making Test, parts A and B (Zalonis et al., 2008). Some authors supported that IQ is a predictor of Stroop performance (Ivnik et al., 1996; Trenerry et al., 1989), whereas others reported in a young sample of individuals that the amount of the variance contributed by intelligence to some measures of executive function, among which is the Stroop test, is statistically significant but modest (Arffa, 2007). Given that the Stroop Color and s seem to be the most sensitive measures of brain damage (Trenerry et al., 1989), we attempted to provide normative data for Trenerry s Stroop version, thus overcoming certain limitations of the original study. For this reason, we included a large sample of healthy male and female participants covering a broad age and educational range, so as to have a representative group of the Greek population and an adequate sample size in each cell after stratification. We also adopted clear and specified exclusion criteria in order to minimize any effect on SNST performance resulting from factors other than demographics. Finally, despite the fact that no significant language differences in the Stroop effect have been reported by others, for example between Chinese or English language (Lee & Chan, 2000), norms of the Greek version of the SNST stratified by age and education, increase the sensitivity and reliability of the test, as well as its utility for the examination of Greek native speakers.

7 Some potential limitations need to be highlighted. Despite the fact that we tried to have a representative proportion of participants came from urban, suburban, and rural areas of Greece, one caveat might concern the relatively small percentage of participants (22%) from rural areas. On the other hand, significant efforts were made in order to exclude all those participants with health problems which might possibly affect brain function and consequently, cognitive performance. Examiners were aware of any participants reference on their health status. Given that vision ability for normal reading is essential for valid interpretation of SNST scores, we tried to ensure that none of our participants had vision problems, which were not corrected with glasses. However, our efforts were based on self-reports during the pre-examination interview and the identification of colors of common objects in the test setting. Thus, a potential limitation would concern the absence of an objective measurement of visual acuity, especially for the elderly subjects. In conclusion, the results of our study revealed that age and education significantly contributed to SNST scores, and the performance on most of the measures decreases with increasing age and lower levels of education. Normative data for SNST stratified by age and education for the Greek adult population is provided as a useful set of norms for clinical interpretation of brain damage and dysfunction of selective attention and cognitive flexibility as well as for research application. Apart from application on patients examination, future research on normal participants could be directed on the nature of age-related decline in Stroop performance to better clarify and allow more reliable conclusions about the effect of a generalized cognitive slowing or an age-related impairment of cognitive flexibility. Conflict of Interest None declared. I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) Appendix Table 1A. The words for the English version of the SNST and the adapted words in the Greek version. SNST version SNST items English RED GREEN BLUE TAN Greek KOKKINO PRASINO BLE KAFE KOKKINO PPASINO MPLE KAFE References Arffa, S. (2007). The relationship of intelligence to executive function and non-executive function measures in a sample of average, above average, and gifted youth. Archives of Clinical Neuropsychology, 22, Bugg, J. M., DeLosh, E. L., Davalos, D. B., & Davis, H. P. (2007). Age differences in Stroop interference: contributions of general slowing and task-specific deficits. Neuropsychology, Development, and Cognition. Section B, Aging Neuropsychology and Cognition, 14, Cohn, N. B., Dustman, R. E., & Bradford, D. C. (1984). Age-related decrements in Stroop Color Test performance. Journal of Clinical Psychology, 40, Collette, F., Amieva, H., Adam, S., Hogge, M., Van der Linden, M., & Fabrigoule, C.et al. (2007). Comparison of inhibitory functioning in mild Alzheimer s disease and frontotemporal dementia. Cortex, 43, Comalli, P. E., Jr., Wapner, S., & Werner, H. (1962). Interference effects of Stroop color-word test in childhood, adulthood and aging. Journal of Genetic Psychology, 100, Dodrill, C. B. (1978). A neuropsychological battery for epilepsy. Epilepsia, 19, Folstein, M. F., Fostein, S. E, & McHugh, P. R. (1975). Mini-mental state. Journal of Psychiatric Research, 12, Golden, J. C. (1978). Stroop Color and Word Test. Chicago, IL: Stoelting Co. Hameleers, P. A. H. M, Van Boxtel, M. P. J., Hogervorst, E., Riedel, W. J., Houx, P. J., & Buntinx, F.et al. (2000). Habitual caffeine consumption and its relation to memory, planning capacity and psychomotor performance across multiple age groups. Human Psychopharmacology: Clinical and Experimental, 15, Houx, P. J., Vreeling, F. W., & Jolles, J. (1993). Stroop interference: aging effects assessed with the Stroop color-word test. Experimental Aging Research, 19, Ivnik, R. J., Malec, J. F., Smith, G. E., Tangalos, E. G., & Petersen, R. C. (1996). Neuropsychological tests norms above 55: COWAT, BNT, MAE Token, WRAT-R Reading, AMNART, STROOP, TMT, and JLO. The Clinical Neuropsychologist, 10, Jønsson, A., Andersen, J., Storr, L., Tscherning, T., Soelberg Sørensen, P., & Ravnborg, M. (2006). Cognitive impairment in newly diagnosed multiple sclerosis patients: a 4-year follow-up study. Journal of the Neurological Sciences, 245, Klein, M., Ponds, R. W., Houx, P. J., & Jolles, J. (1997). Effect of test duration on age-related differences in Stroop interference. Journal of Clinical and Experimental Neuopsychology, 19,

8 88 I. Zalonis et al. / Archives of Clinical Neuropsychology 24 (2009) Lee, T. M., & Chan, C. C. (2000). Stroop interference in Chinese and English. Journal of Clinical and Experimental Neuropsychology, 22, Lezak, M. D., Howieson, D. B., & Loring, D. W. (2004). Neuropsychological Assessment (4th ed.). New York: Oxford University Press. Mitrushina, M. N., Boone, K. B., Razani, J., & D Elia, L. F. (1999). Handbook of Normative Data for Neuropsychological Assessment. New York: Oxford University Press. Moering, R. G., Schinka, J. A., Mortimer, J. A., & Graves, A. B. (2003). Normative data for elderly African Americans for the Stroop Color and Word Test. Archives of Clinical Neuropsychology, 607, Nehemkis, A. M., & Lewinsohn, P. M. (1972). Effects of left and right cerebral lesions on the naming process. Perceptual and Motor Skills, 35, Pauker, J. D. (1988). Constructing overlapping cell tablets to maximize the clinical usefulness of normative test data: rationale and an example from neuropsychology. Journal of Clinical Psychology, 44, Regard, M. (1981). Cognitive rigidity and flexibility: A neuropsychological study. Unpublished Ph.D. Dissertation, University of Victoria. Seignourel, P. J., Robins, D. L., Larson, M. J., Demery, J. A., Cole, M., & Perlstein, W. M. (2005). Cognitive control in closed head injury: context maintenance dysfunction or prepotent response inhibition deficit? Neuropsychology, 19, Spieler, D. H., Balota, D. A., & Faust, M. E. (1996). Stroop performance in healthy younger and older adults and in individuals with dementia of the Alzheimer s type. Journal of Experimental Psychology: Human Perception and Performance, 22, Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests: Administration, norms and commentary. New York: Oxford University Press Inc. Strickland, T. L., D Elia, L. F., James, R., & Stein, R. (1997). Stroop color-word performance of African Americans. Clinical Neuropsychologist, 11, Trenerry, M. R., Crosson, B., DeBoe, J., & Leber, W. R. (1989). Stroop Neuropsychological Screening Test. Odessa, FL: Psychological Assessment Resources. Uttl, B., & Graf, P. (1997). Color-Word Stroop test performance across the adult life span. Journal of Clinical and Experimental Neuropsychology, 19, Van Boxtel, M. P. J., Ten Tusscher, M. P. M., Metsemakers, J. F. M., Willems, B., & Jolles, J. (2001). Visual determinants of reduced performance on the Stroop Color-Word Test in normal aging individuals. Journal of Clinical and Experimental Neuropsychology, 23, Van der Elst, W., Van Boxtel, M. P. J., Van Breukelen, G. J. P., & Jolles, J. (2006). The Stroop color-word test: influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment, 13, Zalonis, I., Kararizou, E., Triantafyllou, N. I., Kapaki, E., Papageorgiou, S., & Sgouropoulos, P. et al. (2008). A normative study of the Trail Making Test A and B in Greek adults. The Clinical Neuropsychologist, 22,