Organizational Strategies Partially Account for Race-related Differences in List Recall Performance

Organizational Strategies Partially Account for Race-related Differences in List Recall Performance Abstract Objective Organizational strategies have been shown to improve one’s ability to recall items from a list. Specifically, use of semantic clustering, the tendency to group items by category when recalling them, predicts better free recall of word lists after short and long delays. The present study utilized a healthy adult sample to examine use of efficient memory strategies as a predictor of differences in neurocognitive findings between African American and white participants. Method Participants provided demographic information and completed the California Verbal Learning Test-Second Edition (CVLT-II) and Wechsler Abbreviated Scale of Intelligence-Second Edition (WASI-2). Results Groups were matched across socioeconomic status and years of education. White participants used more semantic clustering and performed better on recall measures after short and long delays than their African American peers, and semantic clustering predicted recall in both groups. Regression analyses suggested that use of semantic clustering is a significant partial mediator of the relationship between race and free recall abilities. Intelligence scores from the WASI-2 were correlated with CVLT-II measures in white participants but not African American participants. Conclusions Despite quantitatively similar backgrounds, white and African American participants differed in recall performance. However, this study showed that African American participants’ poorer recall may be partially attributed to less frequent use of semantic clustering as a strategy. These discrepancies may be rooted in inequalities in educational experiences and suggest that providing organizational strategies during early learning may be an area of intervention to mitigate racial differences seen in neuropsychological testing. Learning and memory, Cross-cultural/minority, Assessment Introduction The ways in which an individual processes and organizes information during encoding and recollection has been shown to impact recall ability. Over one hundred years of theoretical and empirical research has argued that increased organization of learned material leads to greater retention and recollection (James, 1890; Mandler, 1967; Stricker, Brown, Wixted, Baldo, & Delis, 2002), and it is with the use of certain strategies that an individual is no longer constrained to the short-term memory limitations of 7 ± 2 described over half a century ago (Miller, 1956; Ma et al., 2014). Tulving (1968) described two general methods of mental organization. Primary organization is based on the input of information, such as the order of a list of words. On the other hand, secondary organization involves an individual grouping the information by some inherent quality, for example, by mentally organizing a list of words by category. This type of secondary organization is considered more mature, as it is a strategy that develops with age in children (Vicari, Pasqualetti, Marotta & Carlesimo, 1999) but may be a deficit in clinical populations (Stricker et al., 2002). Use of semantic clustering, the tendency to group items by category when recalling them, has been shown to predict better free recall of word lists after both short and long delays (Delis, Kramer, Kaplan & Ober, 2000; Donders, 2008; Mandler, 1967; Sunderaraman, Blumen, DeMatteo, Apa, & Cosentino, 2013). Numerous studies have shown differences by race on neuropsychological test performance such that non-Hispanic white Americans perform significantly higher their African American counterparts on measures of intelligence, language, and memory (Boone, Victor, Wen, Razani, & Pont, 2007; Byrd, Jacobs, Hilton, Stern, & Manly, 2005; Manly et al., 1998a; Zahodne, Manly, Smith, Seeman, & Lachman, 2017). While differences in average socioeconomic status of races has been implicated as a basis for these discrepancies (Noble, Tottenham, & Casey, 2005; Morgan, Marsiske, & Whitfield, 2008; Manly, Jacobs, Touradji, Small & Stern, 2002; Manly, Touradji, Tang, & Stern, 2003), quality of education, health disparities, and sociocultural effects have also been proposed as factors in these differences in test performance (Kennepohl, Shore, Nabors & Hanks, 2004; Manly et al., 1998b; Morgan et al., 2008). In some cases, memory differences persisted despite consideration of socioeconomic, psychosocial, and health-related mediators (e.g., Zahodne et al., 2017), but in others, controlling for education level eliminated discrepancy between groups (e.g., Boone et al., 2007). Minimal research has been conducted regarding differences in memory strategies as a predictor of differences in performance between racial groups. However, the development of efficient test-taking strategies has been correlated with both education and literacy levels (Ardila and Rosselli, 1989; Byrd et al., 2005; Le Carret et al., 2003), suggesting that the use of strategies may follow similar patterns of disparity between races as other elements of neuropsychological testing. Much of the extant research surrounding racial differences in memory focuses on older adults and dementia risk (Mcdougall et al., 2010; Morgan et al., 2008). In those that have incorporated younger adults into their sample, strategy use was not reported (Boone et al., 2007; Dotson, Kitner-Triolo, Evans, & Alan, 2009; Norman, Evans, Miller, & Heaton, 2000; Zahodne et al., 2017). Therefore, the current study aimed to assess differences between African American participants’ and white participants’ performance on a well-established measure of verbal memory, the California Verbal Learning Test-Second Edition (CVLT-II) and whether any differences might be explained by use of a semantic clustering strategy. We hypothesized that white participants would demonstrate better recollection of items on a verbally presented list than African American participants after both a short and long delay but that these differences between groups would be mediated by an increased use of semantic clustering by white participants. Methods Participants The study was reviewed and approved by the local institutional review board. All human data included in this manuscript was obtained in compliance with IRB regulations and the Helsinki Declaration. The 104 participants (66 female) were recruited through the Georgia State University Psychology student pool and flyers posted in the Atlanta community. Fifty-seven participants were white and non-Hispanic (33 female) and 47 were African American (AA; 33 female). The broader project in which these data were collected did not exclude any racial groups. However, race was self-identified by participants, and only participants who described themselves as white/Caucasian or black/African American were included in the present study. Participants who described themselves as mixed race, biracial, or multiracial or who listed multiple races were categorized as Mixed Race for the purpose of the broader project and not included in the present study. Participants were excluded if they were under 18 years of age and ranged in age from 18 to 53 years old. Screening tests confirmed that all participants’ hearing and vision were within normal limits, and graduate students administered the Structured Clinical Interview for the DSM-IV-TR Axis I (SCID; First, Spitzer, Gibbon, & Williams, 1997) to screen for current or past psychopathology, for which participants were excluded. Participants were also excluded if their full scale IQ (FSIQ) score was less than 70. Measures Participants completed the California Verbal Learning Test—Second Edition (CVLT-II; Baldo, Delis, Kramer, & Shimamura, 2002) and the Hollingshead Four-Factor Index of Social Status as part of a larger neuropsychological test battery. The CVLT-II is a measure of list-learning involving a list (List A) of 16 items from four categories. List A is read to the examinee over five trials, and the examinee is instructed to recall as many items as possible after each trial. Following the fifth trial, the examinee is presented with List B, another list of 16 items, which they are asked to recall. They are then required to recall List A, and this is the short delay free recall (SDFR). This is followed by the short delay cued recall (SDCR) where they are cued with each of the categories of words from List A. Following a 20 min delay, the examinee is again prompted to recall items from List A without cues (Long Delay Free Recall; LDFR) then with the category cues (Long Delay Cued Recall; LDCR). This is followed by a Recognition measure in which the examinee is read 40 words and asked to indicate whether each was on List A. Internal consistency across measures within the CVLT-II ranges from 0.78 to 0.94, and the sample used for normalization is considered more representative of the U.S. population than the original CVLT (Delis et al., 2000). The four subtest version of the Wechsler Abbreviated Scale of Intelligence—Second Edition (WASI-2; Wechsler, 2011) included Vocabulary, Similarities, Block Design, and Matrix Reasoning subtests and was used to estimate full scale IQ (FSIQ). Vocabulary and Similarities subtest scores were used to generate a verbal IQ (VIQ) score per the manual. The Letter-Word Identification subtest from the Woodcock-Johnson Tests of Achievement—Third Edition (WJ-III; Mather & Woodcock, 2001) was administered as a measure of word-reading ability and a proxy for academic achievement. The Digit Span subtest of the Wechsler Memory Scale—Third Edition (WMS-III; Wechsler, 1997) was also administered and reliable digit span was calculated to assess effort. Socioeconomic status (SES) was quantified using the Hollingshead Four-Factor Index of Social Status (Hollingshead, 1975). This measure estimates SES on a scale of 1 through 5 based on years of education and type of occupation, with 1 representing the highest SES bracket and 5 representing the lowest. Values were dichotomized into mid-high SES (1–3) and low SES (4–5). For participants who reported independent tax filing status, their occupation and education were used to calculate SES, and for those who reported dependency, their parents’ education and occupation was used. Analyses The CVLT-II scoring software (Delis & Fridlund, 2000) generates age-normed z-scores for performance across the task. These include scores for number of items accurately recalled for each trial as well as SDFR, SDCR, LDFR, and LDCR. Semantic clustering and serial clustering ratios and their respective z-scores are calculated as well (Stricker et al., 2002). t-Tests and chi-square analyses were first run to compare demographics, CVLT-II free recall, and CVLT-II memory strategies, WASI-2 measures, and WJ-III Letter-Word Identification between AA and white participants. These were followed by correlation and stepwise multiple regression analyses to evaluate the contribution of strategy use as a potential underlying factor of SDFR and LDFR performance. Sobel tests were used to assess significance of mediation effects. Results AA and white groups did not differ in mean years of education, t(102) = 0.72, p = .472, dichotomized socioeconomic status (SES) level, X2(1, N = 95) = 0.33, p = .563, gender, X2(1, N = 104) = 1.69, p = .194, or dependency on parents, X2(1, N = 101) = 0.45, p = .504 (see Table 1). Therefore, these variables were not controlled for in analyses. Age was used for descriptive purposes only, as normative values for the CVLT-II and WASI-2 incorporate age. Means (MAA = 24.21 years, MW = 23.56 years) and ranges (RangeAA = 18–53 years, RangeW = 18–49 years) were similar between groups. See Table 1 for demographic information. Table 1. Demographic variables by racial group White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 Note: AA = African American; M = mean; SD = standard deviation; SES = socioeconomic status; mid to high SES = 1–3 on Hollingshead Four-Factor Index of Social Status. *p < .05, **p < .01, ***p < .001. Table 1. Demographic variables by racial group White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 Note: AA = African American; M = mean; SD = standard deviation; SES = socioeconomic status; mid to high SES = 1–3 on Hollingshead Four-Factor Index of Social Status. *p < .05, **p < .01, ***p < .001. On the WASI-2, FSIQ, and VIQ scores were higher in white participants than AA participants, tFSIQ(96) = 5.40, pFSIQ < .001, tVIQ(97) = 2.98, pVIQ = .004. White participants also scored higher on the WJ-III Letter-Word Identification subtest, t(96) = 2.94, p = .004. See Table 2 for IQ and achievement scores. Table 2. WASI-2 and WJ-III scores by racial group Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Note: AA = African American; M = mean; SD= standard deviation; WASI-2 = Wechsler Abbreviate Scales of Intelligence—Second Edition; WJ-III = Woodcock-Johnson Tests of Achievement—Third Edition; FSIQ = full scale IQ; VIQ = verbal IQ. *p < .05, **p < .01, ***p < .001. Table 2. WASI-2 and WJ-III scores by racial group Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Note: AA = African American; M = mean; SD= standard deviation; WASI-2 = Wechsler Abbreviate Scales of Intelligence—Second Edition; WJ-III = Woodcock-Johnson Tests of Achievement—Third Edition; FSIQ = full scale IQ; VIQ = verbal IQ. *p < .05, **p < .01, ***p < .001. On the CVLT-II, white participants (MZ = 0.23, SDZ = 1.00) recalled significantly more words on the SDFR measure than AA participants (MZ = −0.54, SDZ = 1.00), t(102) = 3.90, p < .001. On the LDFR measure, white participants (MZ = 0.10, SDZ = 1.01) also recalled more words than their AA counterparts (MZ = −0.51, SDZ = 0.96), t(102) = 3.11, p = .002. Furthermore, white participants (MZ = 0.47, SDZ = 1.33) utilized semantic clustering when recalling items from lists more than AA participants (MZ = −0.06, SDZ = 1.00), t(102) = 2.34, p = .021. Use of serial clustering did not vary by group, t(102) = 1.20, p = .233 (see Table 3). Table 3. CVLT-II performance by racial group White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 Note: AA = African American; M and SD values are z-scores. M = mean;SD = standard deviation; n.c. = not computable due to zero participants being impaired. *p < .05, **p < .01, ***p < .001. Table 3. CVLT-II performance by racial group White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 Note: AA = African American; M and SD values are z-scores. M = mean;SD = standard deviation; n.c. = not computable due to zero participants being impaired. *p < .05, **p < .01, ***p < .001. Greater use of semantic clustering predicted better SDFR (R2= 0.22, p < .001) and LDFR (R2 = 0.24, p < .001) across both groups (see Fig. 1a and 1b). With this knowledge and the finding that short and long delay recall scores differed by group, a multiple linear regression mediation model was developed to evaluate the possibility that semantic clustering tendency was a mediator between race and memory performance, as literature indicates that semantic clustering is a plausible contributor to memory differences. Fig. 1 View largeDownload slide (a) Correlations between semantic clustering and short delay free recall. R2white participants = 0.216**; R2AA participants = 0.154**. (b) Correlations between semantic clustering and long delay free recall. R2white participants = 0.229**; R2AA participants = 0.191**. Note. AA = African American; CVLT-II = California Verbal Learning Test—Second Edition. *p < .05, **p < .01, ***p < .001. Fig. 1 View largeDownload slide (a) Correlations between semantic clustering and short delay free recall. R2white participants = 0.216**; R2AA participants = 0.154**. (b) Correlations between semantic clustering and long delay free recall. R2white participants = 0.229**; R2AA participants = 0.191**. Note. AA = African American; CVLT-II = California Verbal Learning Test—Second Edition. *p < .05, **p < .01, ***p < .001. Fig. 2 View largeDownload slide Mediation models demonstrating semantic clustering as a mediator of the relationships between race and recall performance. Note. AA = African American; SDFR = short delay free recall; LDFR = long delay free recall. *p < .05, **p < .01, ***p < .001. Fig. 2 View largeDownload slide Mediation models demonstrating semantic clustering as a mediator of the relationships between race and recall performance. Note. AA = African American; SDFR = short delay free recall; LDFR = long delay free recall. *p < .05, **p < .01, ***p < .001. In order to evaluate semantic clustering as a potential mediator between race and recall measures, all three paths of the mediation models for both SDFR and LDFR output were assessed (see Fig. 2). Race had a significant effect on SDFR (path c: B = −0.77, SE = 0.20, p < .001), such that white participants showed higher scores on the measure. Semantic clustering was significant when regressed on race (path a: B = −0.54, SE = 0.24, p = .025), where white participants used the strategy more. The effect of semantic clustering on SDFR performance was also significant (path b: B = 0.41, SE = 0.08, p < .001), where greater use of semantic clustering was related to higher SDFR scores. LDFR was also significantly predicted by race (path c: B = −0.61, SE = 0.20, p = .002) and semantic clustering (path b: B = 0.42, SE = 0.07, p < .001). The statistical significance of each full model of the indirect effect of race on SDFR/LDFR through semantic clustering was testing with bootstrapping with 5,000 samples. The SDFR model was significant (B = −0.18, SE = 0.08, 95% CI: −0.39, −0.04), as was the LDFR model (B = −0.20, SE = 0.09, 95% CI: −0.41, −0.05). The mediation model was also evaluated with stepwise multiple regression. With regard to SDFR performance (Table 4a), Model 1 indicated that race alone significantly predicted one’s memory for items on the SDFR with white participants performing better than AA participants. Race accounted for 13% of the variance in SDFR performance, F(1, 103) = 15.23, R2 = 0.13, p < .001. When both race and semantic clustering were included in Model 2, the model was significantly improved (ΔR2 = 0.16, p < .001), and 29% of variance in SDFR performance was accounted for, F(2, 103) = 20.94, R2 = 0.29, p < .001. Race continued to contribute a significant amount of unique variance, sr2 = 0.07, p = .002, and semantic clustering contributed a significant amount of total variance, r2 = 0.224, and unique variance, sr2 = 0.16, p < .001. However, 6% of the variance of in SDFR performance was shared by the two predictors, and the Sobel test indicated that although the contribution of race remained significant, the mediation effect of semantic clustering was significant, z’ = −2.04, p = .04. Table 4a. Hierarchical regression model of predictors of short delay free recall performance β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. Table 4a. Hierarchical regression model of predictors of short delay free recall performance β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. Concerning LDFR performance (Table 4b), race alone significantly predicted recall with white participants performing above AA participants in Model 1, F(1, 103) = 9.69, R2 = 0.87, p = .002. Race accounted for 9% of the variance in this model, r2 = 0.09. Adding semantic clustering as a mediating factor made for a significantly stronger Model 2 (ΔR2 = 0.19, p < .001), which then accounted for 28% of the variance in long delay performance, F(2, 103) = 19.66, p < .001. Semantic clustering accounted for 24% of the variance, 19% of which was unique, r2 = 0.24, sr2 = 0.19. Race continued to contribute a significant amount of unique variance in Model 2, sr2 = 0.04, p = .026, but 5% of the variance in LDFR performance was shared by race and semantic clustering. The Sobel test verified that the mediation effect of semantic clustering was significant, z’=−2.21, p = .03. Table 4b. Hierarchical regression model of predictors of long delay free recall performance β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. Table 4b. Hierarchical regression model of predictors of long delay free recall performance β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. At the recommendation of expert reviewers, multiple regression analyses were also run controlling for participants’ WASI-2 FSIQ scores. With regard to SDFR performance (Table 4c), FSIQ significantly predicted SDFR independently in Model 1, F(1, 97) = 19.59, R2 = 0.17, p < .001, and adding race significantly improved Model 2’s strength (ΔR2 = 0.04, p = .030), with both FSIQ and race significantly contributing to its significance, F(2, 97) = 12.60, R2 = 0.19, p < .001. Adding semantic clustering in Model 3 accounted for further significant variance (ΔR2 = 0.10, p < .001), with race no longer accounting for significant amounts of variance, though FSIQ and semantic clustering each did, resulting in a significant overall model F(3, 97) = 14.04, R2 = 0.19, p < .001. With respect to LDFR performance (Table 4d), FSIQ alone was a significant predictor of variance in Model 1, F(1, 97) = 19.48, R2 = 0.17, p < .001. Adding race in Model 2 did not significantly change the model (ΔR2 = 0.01, p = .197), and race itself was not a significant contributor to the overall significant model, F(2, 97) = 10.65, R2 = 0.18, p < .001. Adding semantic clustering in Model 3 did make a significant change (ΔR2 = 0.13, p < .001), with FSIQ and semantic clustering accounting for significant amounts of variance in the overall significant model, though race did not, F(3, 97) = 14.36, R2 = 0.31, p < .001. Table 4c. Hierarchical regression model of predictors of short delay free recall performance controlling for FSIQ β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. Table 4c. Hierarchical regression model of predictors of short delay free recall performance controlling for FSIQ β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. Table 4d. Hierarchical regression model of predictors of long delay free recall performance controlling for FSIQ. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. Table 4d. Hierarchical regression model of predictors of long delay free recall performance controlling for FSIQ. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. Within white participants, FSIQ and VIQ scores showed the same pattern of correlations with CVLT-II scores; both were positively correlated with SDFR scores, LDFR scores, and semantic clustering usage but not serial clustering usage. Letter-Word Identification scores did not significantly correlate with any CVLT-II performance measures in this group. In contrast, in the AA group, Letter-Word Identification scores correlated positively with use of serial clustering. However, IQ and other CVLT-II measures were not associated with each other in this group. See Table 5 for correlations. Table 5. Correlations between IQ/achievement scores and CVLT-II scores by racial group White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 Note: AA = African American; SDFR = short delay free recall; LDFR = long delay free recall; FSIQ = full scale IQ; VIQ = verbal IQ; ID = identification. Table 5. Correlations between IQ/achievement scores and CVLT-II scores by racial group White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 Note: AA = African American; SDFR = short delay free recall; LDFR = long delay free recall; FSIQ = full scale IQ; VIQ = verbal IQ; ID = identification. Discussion Group comparisons of performance on the CVLT-II were commensurate with previous research on the measure (Norman et al., 2000) with white participants performing better on SDFR, LDFR, and semantic clustering than AA participants. These findings indicated that during list learning, white individuals were more frequently reciting items by category as they recalled them, and they recalled a greater number of words from the list after both a short delay and a long delay. Increased use of semantic clustering also predicted improved short and long delay free recall. Groups were comparable on serial clustering tendencies, i.e., the strategy of recalling items in the order they were presented by the examiner, and no relationships were observed between serial clustering and recall abilities. A lack of quantifiable demographic differences between groups suggested that these factors were not responsible for group differences. Number of years of education and SES did not differ between AA and white participants, nor was there a significant difference in gender ratios. Prior research indicated the necessity of this verification, as women have been shown to outperform men on list-learning tasks (Delis, Kramer, Kaplan, & Ober, 1987; Lowe, Mayfield, & Reynolds, 2003; Norman et al., 2000). Instead, we considered that semantic clustering ability might be a mediator of the effect of race on SDFR and LDFR performance. A multiple regression model predicting SDFR indicated that race alone was a significant predictor of SDFR performance, accounting for 13% of variance when it was the only predictor. However, adding semantic clustering to the model made it significantly stronger, with 29% of all variance accounted for by the two predictors. While semantic clustering did not fully mediate the contribution of race, it was found to be a significant partial mediator; almost half of race’s contribution to SDFR was shared with semantic clustering. Similar results emerged with LDFR performance; while race alone accounted for 9% of variance in recollection after a long delay, semantic clustering made a significant contribution, with the final model accounting for 28% of all variance. In this case, over half of the variance contributed by race was shared with semantic clustering, and semantic clustering was again found to be a significant partial mediator by the Sobel test. Furthermore, when mediation models were run with bootstrapping, both the SDFR and LDFR outcome models were found to be significant. At reviewers’ recommendations, multiple regression analyses controlling for participants’ FSIQ scores were also conducted. Notably, findings regarding the role of semantic clustering as a predictor of recall abilities remained robust; semantic clustering continued to significantly contribute to the variance in short- and long-delay free recall performance beyond IQ and race. However, IQ remained a significant contributor in both final models. Race was also unable to account for variance beyond that accounted for by IQ in long delay free recall, and collinearity remains a concern here. Furthermore, while IQ is an important consideration in the context of the broad neuropsychological literature, these specific findings should be interpreted with caution. IQ testing has a longstanding history of racial bias (Reynolds & Suzuki, 2013), and such an imbalance was noted between the white and AA participants in the present study despite similarities across aforementioned demographic domains. Overall, the present findings indicate that differences in recollection of a verbally presented list between African American and white adults persist between groups with similar basic demographic information. However, much of this difference can be attributed to differences in use of a specific strategy, i.e., mental organization of items by category. Although the use of these more advanced strategies is expected to develop over time in healthy populations, formal education may facilitate elements of cognitive growth in executive functions such as organization and planning despite these abilities being frequently attributed to normal development (Carr, Kurtz, Schneider, Turner, & Borkowski, 1989; Diamond, 2013). The standard measure of education within neuropsychological testing is years of education, and studies have documented its influence on performance across neuropsychological measures (Lezak, Howieson, & Loring, 2004; Norman et al., 2000). Quantitatively, this measure did not differ between our AA and white participants. However, this does not take into account the wide range of quality of schooling across the United States, and research indicates that these straightforward numbers may not be a reflection of the education participants received. Across numerous measures of quality, majority–minority public schools lag behind majority-white schools. These measures include class size, the proportion of novice teachers, and the rate of faculty turnover (Gagnon & Mattingly, 2015; Logan, Minca, & Adar, 2012). In turn, racial disparities are observed on measures administered to same-grade students, such as national exams (Logan et al., 2012). Empirical studies have indicated that African Americans perform below their reported education level on measures of reading abilities (Manly et al., 2002; Bryant et al. 2005), suggesting measurable gaps in the quality of education received between races. As expected based on the literature (Lezak et al., 2004; Manly et al., 1998a, 1998b; Sisco et al., 2013), there were significant discrepancies between AA and white participants’ IQ and reading achievement measures within our study. Particularly remarkable about these scores, however, were the correlations or lack thereof with CVLT-II performance. Both full scale and verbal IQ scores predicted a higher recollection of words and greater use of semantic clustering in white participants, indicating that intelligence predicts both recall ability and strategy use in this group. However, Letter-Word Identification scores, our proxy for academic achievement in reading, showed no correlations with CVLT-II performance in white participants. In contrast, intelligence measures showed no correlation with CVLT-II scores in AA participants. This suggests that even though the group earned lower scores across these measures than their white peers, intelligence as measured by the WASI-2 is not a predictor of their memory abilities or strategy use. However, that Letter-Word Identification performance predicted use of serial but not semantic clustering indicates that those AA individuals who demonstrate better word reading performance use serial recall more often. Reading measures such as our Letter-Word Identification task are often used as a proxy for quality of education. While these measures often correlate with increased scores on cognitive measures, in this case, it appears that the students with higher educational quality as measured by word reading performance are using a less efficient and more cognitively demanding approach to recalling word lists. Development of cognitive strategies may be rooted in one’s education. Reading ability has been found to mediate the relationship between years of education and executive functioning skills such as sequencing, word generation, and pattern identification in older adults (Johnson, Flicker, & Lichtenberg, 2006). Byrd and colleagues found that problem solving abilities on visual tasks could be predicted by reading ability in an older African American sample (Byrd et al., 2005). Many learned elements of task-taking may be attributed to success in school, whether they are expressly learned once the basics are mastered or they are a component of simultaneous development. Fortunately, literature suggests that semantic clustering may be a skill that can be taught in the classroom and subsequently utilized in a way that improves recall as assessed by neuropsychological measures (Carr et al., 1989; Frank, Keene, & Taylor, 1993). As such, our findings suggest that this may be a target area of intervention within the education system; if students of any race are struggling to memorize information, being provided with this strategy may bolster their performance. In a group similar to our healthy sample without memory impairments, learning this strategy may facilitate performance on other tests similarly. That semantic clustering was a significant partial mediator between race and both short and long delay free recall suggests that instruction on semantic clustering approach to recall with African American students may help to improve learning and memory efficiency and performance. It will be necessary for future studies to empirically examine the relationship between quality of schooling and individuals’ strategy use. The present studied included the WJ-III Letter-Word Identification subtest as a measure of academic achievement, but it is not necessarily a reflection of broader education or academic success. Instead, future studies may choose to use measures such as the Wide Range Achievement Test (WRAT-4; Wilkinson & Robertson, 2006) that include elements of multiple academic domains, as has been used by Manly and colleagues (Manly et al., 2002, 2003). Researchers may also consider assessing explicit school-related variables such as student-to-teacher ratio, average number of school days attended, or length of the school term as possible predictors of neuropsychological outcomes similar to Sisco and colleagues (2013). Notably, some variability in free recall remained accounted for by race alone and not by semantic clustering tendencies. Research has suggested that differences in neuropsychological performance between African American and white participants may be rooted in other disparities; aside from differences in school quality, performance may be affected by physical health challenges (Schwartz et al., 2004) and psychosocial factors (Macintyre, Ellaway, & Cummins, 2002). Morgan and colleagues (2008) found that education quality also did not fully mediate racial differences in performance on some cognitive measures, further indicating that it will be necessary to evaluate the effects of these different domains. Future studies may also consider assessing socioeconomic status in a different manner than was presently used. The Hollingshead Four-Factor Index of Social Status was developed in 1975 and may not accurately map onto some of today’s occupations and their level of status. Its use of education level or parent education level in its calculations holds the same limitations as the present study in that it does not consider quality of education. Furthermore, dichotomizing the group into high-mid and low SES segments was done in order to ameliorate these potential small differences in categorization and create larger groups, but it narrowed variability across individuals and limited analysis options. Although data collection in a university is often considered a limiting factor with respect to generalizability, the present study benefited from the diversity of its setting. The majority of participants were enrolled in undergraduate coursework at a 4-year university, but our sample is notably more reflective of the general population than a traditional university sample. The large urban university out of which the study was based consists of 30% first-generation college students and 26% adult learners (U.S. News & World Report, 2013). Over 60% of the undergraduate population at the university are considered underrepresented minority students. Of our participants who provided the data, 74 came from mid to very high socioeconomic status households, and 21 came from low to very low socioeconomic households. Paternal education information was not collected, but participants’ mothers’ education level ranged from a high school degree or less (N = 29) to some college (N = 27) to a college degree or more (N = 40). Therefore, the findings of this study should be considered relatively generalizable to the broader population. The present study is further served by its use of a widely validated neuropsychological measure, the CVLT-II, and its well-defined assessment of strategy use, specifically, serial and semantic clustering (Delis et al., 2010). This quantification of a technique allows researchers to begin to parse apart the root of broader discrepancies in performance between groups, as we have aimed to do in this study. The findings of the current study suggest that semantic clustering, a strategy that was found to lead to increased recall of a list after both a short and long delay, is utilized more by white adults than African American adults. It is also a significant partial mediator of the relationship between race and recall ability, indicating that it may be an area of intervention to mitigate racial differences in cognitive abilities that often emerges in neuropsychological testing. As research suggests that test-taking strategies may be developed alongside educational development, education disparities should be considered as a possible driver of these abilities, although additional health and psychosocial effects should be evaluated as well. Funding This work was supported by a Research Scholar Grant from the American Cancer Society awarded to T.Z.K. (#RSGPB-CPPB-114044), a Georgia State University Brains & Behavior Initiative graduate student fellowship to M.E.F., and a Georgia State University Honors Undergraduate University Assistantship to T.F.P. Conflict of Interest No conflicts of interest exist. 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Organizational Strategies Partially Account for Race-related Differences in List Recall Performance

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Abstract

Abstract Objective Organizational strategies have been shown to improve one’s ability to recall items from a list. Specifically, use of semantic clustering, the tendency to group items by category when recalling them, predicts better free recall of word lists after short and long delays. The present study utilized a healthy adult sample to examine use of efficient memory strategies as a predictor of differences in neurocognitive findings between African American and white participants. Method Participants provided demographic information and completed the California Verbal Learning Test-Second Edition (CVLT-II) and Wechsler Abbreviated Scale of Intelligence-Second Edition (WASI-2). Results Groups were matched across socioeconomic status and years of education. White participants used more semantic clustering and performed better on recall measures after short and long delays than their African American peers, and semantic clustering predicted recall in both groups. Regression analyses suggested that use of semantic clustering is a significant partial mediator of the relationship between race and free recall abilities. Intelligence scores from the WASI-2 were correlated with CVLT-II measures in white participants but not African American participants. Conclusions Despite quantitatively similar backgrounds, white and African American participants differed in recall performance. However, this study showed that African American participants’ poorer recall may be partially attributed to less frequent use of semantic clustering as a strategy. These discrepancies may be rooted in inequalities in educational experiences and suggest that providing organizational strategies during early learning may be an area of intervention to mitigate racial differences seen in neuropsychological testing. Learning and memory, Cross-cultural/minority, Assessment Introduction The ways in which an individual processes and organizes information during encoding and recollection has been shown to impact recall ability. Over one hundred years of theoretical and empirical research has argued that increased organization of learned material leads to greater retention and recollection (James, 1890; Mandler, 1967; Stricker, Brown, Wixted, Baldo, & Delis, 2002), and it is with the use of certain strategies that an individual is no longer constrained to the short-term memory limitations of 7 ± 2 described over half a century ago (Miller, 1956; Ma et al., 2014). Tulving (1968) described two general methods of mental organization. Primary organization is based on the input of information, such as the order of a list of words. On the other hand, secondary organization involves an individual grouping the information by some inherent quality, for example, by mentally organizing a list of words by category. This type of secondary organization is considered more mature, as it is a strategy that develops with age in children (Vicari, Pasqualetti, Marotta & Carlesimo, 1999) but may be a deficit in clinical populations (Stricker et al., 2002). Use of semantic clustering, the tendency to group items by category when recalling them, has been shown to predict better free recall of word lists after both short and long delays (Delis, Kramer, Kaplan & Ober, 2000; Donders, 2008; Mandler, 1967; Sunderaraman, Blumen, DeMatteo, Apa, & Cosentino, 2013). Numerous studies have shown differences by race on neuropsychological test performance such that non-Hispanic white Americans perform significantly higher their African American counterparts on measures of intelligence, language, and memory (Boone, Victor, Wen, Razani, & Pont, 2007; Byrd, Jacobs, Hilton, Stern, & Manly, 2005; Manly et al., 1998a; Zahodne, Manly, Smith, Seeman, & Lachman, 2017). While differences in average socioeconomic status of races has been implicated as a basis for these discrepancies (Noble, Tottenham, & Casey, 2005; Morgan, Marsiske, & Whitfield, 2008; Manly, Jacobs, Touradji, Small & Stern, 2002; Manly, Touradji, Tang, & Stern, 2003), quality of education, health disparities, and sociocultural effects have also been proposed as factors in these differences in test performance (Kennepohl, Shore, Nabors & Hanks, 2004; Manly et al., 1998b; Morgan et al., 2008). In some cases, memory differences persisted despite consideration of socioeconomic, psychosocial, and health-related mediators (e.g., Zahodne et al., 2017), but in others, controlling for education level eliminated discrepancy between groups (e.g., Boone et al., 2007). Minimal research has been conducted regarding differences in memory strategies as a predictor of differences in performance between racial groups. However, the development of efficient test-taking strategies has been correlated with both education and literacy levels (Ardila and Rosselli, 1989; Byrd et al., 2005; Le Carret et al., 2003), suggesting that the use of strategies may follow similar patterns of disparity between races as other elements of neuropsychological testing. Much of the extant research surrounding racial differences in memory focuses on older adults and dementia risk (Mcdougall et al., 2010; Morgan et al., 2008). In those that have incorporated younger adults into their sample, strategy use was not reported (Boone et al., 2007; Dotson, Kitner-Triolo, Evans, & Alan, 2009; Norman, Evans, Miller, & Heaton, 2000; Zahodne et al., 2017). Therefore, the current study aimed to assess differences between African American participants’ and white participants’ performance on a well-established measure of verbal memory, the California Verbal Learning Test-Second Edition (CVLT-II) and whether any differences might be explained by use of a semantic clustering strategy. We hypothesized that white participants would demonstrate better recollection of items on a verbally presented list than African American participants after both a short and long delay but that these differences between groups would be mediated by an increased use of semantic clustering by white participants. Methods Participants The study was reviewed and approved by the local institutional review board. All human data included in this manuscript was obtained in compliance with IRB regulations and the Helsinki Declaration. The 104 participants (66 female) were recruited through the Georgia State University Psychology student pool and flyers posted in the Atlanta community. Fifty-seven participants were white and non-Hispanic (33 female) and 47 were African American (AA; 33 female). The broader project in which these data were collected did not exclude any racial groups. However, race was self-identified by participants, and only participants who described themselves as white/Caucasian or black/African American were included in the present study. Participants who described themselves as mixed race, biracial, or multiracial or who listed multiple races were categorized as Mixed Race for the purpose of the broader project and not included in the present study. Participants were excluded if they were under 18 years of age and ranged in age from 18 to 53 years old. Screening tests confirmed that all participants’ hearing and vision were within normal limits, and graduate students administered the Structured Clinical Interview for the DSM-IV-TR Axis I (SCID; First, Spitzer, Gibbon, & Williams, 1997) to screen for current or past psychopathology, for which participants were excluded. Participants were also excluded if their full scale IQ (FSIQ) score was less than 70. Measures Participants completed the California Verbal Learning Test—Second Edition (CVLT-II; Baldo, Delis, Kramer, & Shimamura, 2002) and the Hollingshead Four-Factor Index of Social Status as part of a larger neuropsychological test battery. The CVLT-II is a measure of list-learning involving a list (List A) of 16 items from four categories. List A is read to the examinee over five trials, and the examinee is instructed to recall as many items as possible after each trial. Following the fifth trial, the examinee is presented with List B, another list of 16 items, which they are asked to recall. They are then required to recall List A, and this is the short delay free recall (SDFR). This is followed by the short delay cued recall (SDCR) where they are cued with each of the categories of words from List A. Following a 20 min delay, the examinee is again prompted to recall items from List A without cues (Long Delay Free Recall; LDFR) then with the category cues (Long Delay Cued Recall; LDCR). This is followed by a Recognition measure in which the examinee is read 40 words and asked to indicate whether each was on List A. Internal consistency across measures within the CVLT-II ranges from 0.78 to 0.94, and the sample used for normalization is considered more representative of the U.S. population than the original CVLT (Delis et al., 2000). The four subtest version of the Wechsler Abbreviated Scale of Intelligence—Second Edition (WASI-2; Wechsler, 2011) included Vocabulary, Similarities, Block Design, and Matrix Reasoning subtests and was used to estimate full scale IQ (FSIQ). Vocabulary and Similarities subtest scores were used to generate a verbal IQ (VIQ) score per the manual. The Letter-Word Identification subtest from the Woodcock-Johnson Tests of Achievement—Third Edition (WJ-III; Mather & Woodcock, 2001) was administered as a measure of word-reading ability and a proxy for academic achievement. The Digit Span subtest of the Wechsler Memory Scale—Third Edition (WMS-III; Wechsler, 1997) was also administered and reliable digit span was calculated to assess effort. Socioeconomic status (SES) was quantified using the Hollingshead Four-Factor Index of Social Status (Hollingshead, 1975). This measure estimates SES on a scale of 1 through 5 based on years of education and type of occupation, with 1 representing the highest SES bracket and 5 representing the lowest. Values were dichotomized into mid-high SES (1–3) and low SES (4–5). For participants who reported independent tax filing status, their occupation and education were used to calculate SES, and for those who reported dependency, their parents’ education and occupation was used. Analyses The CVLT-II scoring software (Delis & Fridlund, 2000) generates age-normed z-scores for performance across the task. These include scores for number of items accurately recalled for each trial as well as SDFR, SDCR, LDFR, and LDCR. Semantic clustering and serial clustering ratios and their respective z-scores are calculated as well (Stricker et al., 2002). t-Tests and chi-square analyses were first run to compare demographics, CVLT-II free recall, and CVLT-II memory strategies, WASI-2 measures, and WJ-III Letter-Word Identification between AA and white participants. These were followed by correlation and stepwise multiple regression analyses to evaluate the contribution of strategy use as a potential underlying factor of SDFR and LDFR performance. Sobel tests were used to assess significance of mediation effects. Results AA and white groups did not differ in mean years of education, t(102) = 0.72, p = .472, dichotomized socioeconomic status (SES) level, X2(1, N = 95) = 0.33, p = .563, gender, X2(1, N = 104) = 1.69, p = .194, or dependency on parents, X2(1, N = 101) = 0.45, p = .504 (see Table 1). Therefore, these variables were not controlled for in analyses. Age was used for descriptive purposes only, as normative values for the CVLT-II and WASI-2 incorporate age. Means (MAA = 24.21 years, MW = 23.56 years) and ranges (RangeAA = 18–53 years, RangeW = 18–49 years) were similar between groups. See Table 1 for demographic information. Table 1. Demographic variables by racial group White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 Note: AA = African American; M = mean; SD = standard deviation; SES = socioeconomic status; mid to high SES = 1–3 on Hollingshead Four-Factor Index of Social Status. *p < .05, **p < .01, ***p < .001. Table 1. Demographic variables by racial group White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 White participants AA participants t Cohen’s d Χ2 φ N (female, % female) 57 (33, 58%) 47 (33, 70%) — — 1.69 0.13 % mid to high SES 75.5% 80.4% — — 0.22 −0.06 Years of education (M ± SD) 14.26 ± 1.42 14.04 ± 1.69 0.72 0.14 — — % dependent on parents 64% 58% — — 0.45 0.07 Note: AA = African American; M = mean; SD = standard deviation; SES = socioeconomic status; mid to high SES = 1–3 on Hollingshead Four-Factor Index of Social Status. *p < .05, **p < .01, ***p < .001. On the WASI-2, FSIQ, and VIQ scores were higher in white participants than AA participants, tFSIQ(96) = 5.40, pFSIQ < .001, tVIQ(97) = 2.98, pVIQ = .004. White participants also scored higher on the WJ-III Letter-Word Identification subtest, t(96) = 2.94, p = .004. See Table 2 for IQ and achievement scores. Table 2. WASI-2 and WJ-III scores by racial group Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Note: AA = African American; M = mean; SD= standard deviation; WASI-2 = Wechsler Abbreviate Scales of Intelligence—Second Edition; WJ-III = Woodcock-Johnson Tests of Achievement—Third Edition; FSIQ = full scale IQ; VIQ = verbal IQ. *p < .05, **p < .01, ***p < .001. Table 2. WASI-2 and WJ-III scores by racial group Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Assessment measure White participants M ± SD AA participants M ± SD t Cohen’s d WASI-2 FSIQ (standard score) 112.48 ± 9.45 102.10 ± 9.40 5.40*** 1.10 WASI-2 VIQ (standard score) 110.52 ± 11.08 104.33 ± 9.09 2.98** 0.61 WJ-III Letter-Word ID (z-score) 0.30 ± 0.58 −0.05 ± 0.60 2.94** 0.59 Note: AA = African American; M = mean; SD= standard deviation; WASI-2 = Wechsler Abbreviate Scales of Intelligence—Second Edition; WJ-III = Woodcock-Johnson Tests of Achievement—Third Edition; FSIQ = full scale IQ; VIQ = verbal IQ. *p < .05, **p < .01, ***p < .001. On the CVLT-II, white participants (MZ = 0.23, SDZ = 1.00) recalled significantly more words on the SDFR measure than AA participants (MZ = −0.54, SDZ = 1.00), t(102) = 3.90, p < .001. On the LDFR measure, white participants (MZ = 0.10, SDZ = 1.01) also recalled more words than their AA counterparts (MZ = −0.51, SDZ = 0.96), t(102) = 3.11, p = .002. Furthermore, white participants (MZ = 0.47, SDZ = 1.33) utilized semantic clustering when recalling items from lists more than AA participants (MZ = −0.06, SDZ = 1.00), t(102) = 2.34, p = .021. Use of serial clustering did not vary by group, t(102) = 1.20, p = .233 (see Table 3). Table 3. CVLT-II performance by racial group White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 Note: AA = African American; M and SD values are z-scores. M = mean;SD = standard deviation; n.c. = not computable due to zero participants being impaired. *p < .05, **p < .01, ***p < .001. Table 3. CVLT-II performance by racial group White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 White participants M ± SD (% impaired) AA participants M ± SD (% impaired) t Cohen’s d Χ2 impairment (z<−1.5) Short delay free recall 0.23 ± 1.00 (4%) −0.54 ± 1.00 (15%) 3.90*** 0.77 4.22* Long delay free recall 0.10 ± 1.01 (4%) −0.51 ± 0.96 (9%) 3.11** 0.62 1.18 Semantic clustering 0.47 ± 1.33 (0%) −0.06 ± 1.00 (0%) 2.34* 0.45 n.c. Serial clustering 0.08 ± 1.10 (4%) −0.15 ± 0.77 (2%) 1.20 0.24 0.18 Note: AA = African American; M and SD values are z-scores. M = mean;SD = standard deviation; n.c. = not computable due to zero participants being impaired. *p < .05, **p < .01, ***p < .001. Greater use of semantic clustering predicted better SDFR (R2= 0.22, p < .001) and LDFR (R2 = 0.24, p < .001) across both groups (see Fig. 1a and 1b). With this knowledge and the finding that short and long delay recall scores differed by group, a multiple linear regression mediation model was developed to evaluate the possibility that semantic clustering tendency was a mediator between race and memory performance, as literature indicates that semantic clustering is a plausible contributor to memory differences. Fig. 1 View largeDownload slide (a) Correlations between semantic clustering and short delay free recall. R2white participants = 0.216**; R2AA participants = 0.154**. (b) Correlations between semantic clustering and long delay free recall. R2white participants = 0.229**; R2AA participants = 0.191**. Note. AA = African American; CVLT-II = California Verbal Learning Test—Second Edition. *p < .05, **p < .01, ***p < .001. Fig. 1 View largeDownload slide (a) Correlations between semantic clustering and short delay free recall. R2white participants = 0.216**; R2AA participants = 0.154**. (b) Correlations between semantic clustering and long delay free recall. R2white participants = 0.229**; R2AA participants = 0.191**. Note. AA = African American; CVLT-II = California Verbal Learning Test—Second Edition. *p < .05, **p < .01, ***p < .001. Fig. 2 View largeDownload slide Mediation models demonstrating semantic clustering as a mediator of the relationships between race and recall performance. Note. AA = African American; SDFR = short delay free recall; LDFR = long delay free recall. *p < .05, **p < .01, ***p < .001. Fig. 2 View largeDownload slide Mediation models demonstrating semantic clustering as a mediator of the relationships between race and recall performance. Note. AA = African American; SDFR = short delay free recall; LDFR = long delay free recall. *p < .05, **p < .01, ***p < .001. In order to evaluate semantic clustering as a potential mediator between race and recall measures, all three paths of the mediation models for both SDFR and LDFR output were assessed (see Fig. 2). Race had a significant effect on SDFR (path c: B = −0.77, SE = 0.20, p < .001), such that white participants showed higher scores on the measure. Semantic clustering was significant when regressed on race (path a: B = −0.54, SE = 0.24, p = .025), where white participants used the strategy more. The effect of semantic clustering on SDFR performance was also significant (path b: B = 0.41, SE = 0.08, p < .001), where greater use of semantic clustering was related to higher SDFR scores. LDFR was also significantly predicted by race (path c: B = −0.61, SE = 0.20, p = .002) and semantic clustering (path b: B = 0.42, SE = 0.07, p < .001). The statistical significance of each full model of the indirect effect of race on SDFR/LDFR through semantic clustering was testing with bootstrapping with 5,000 samples. The SDFR model was significant (B = −0.18, SE = 0.08, 95% CI: −0.39, −0.04), as was the LDFR model (B = −0.20, SE = 0.09, 95% CI: −0.41, −0.05). The mediation model was also evaluated with stepwise multiple regression. With regard to SDFR performance (Table 4a), Model 1 indicated that race alone significantly predicted one’s memory for items on the SDFR with white participants performing better than AA participants. Race accounted for 13% of the variance in SDFR performance, F(1, 103) = 15.23, R2 = 0.13, p < .001. When both race and semantic clustering were included in Model 2, the model was significantly improved (ΔR2 = 0.16, p < .001), and 29% of variance in SDFR performance was accounted for, F(2, 103) = 20.94, R2 = 0.29, p < .001. Race continued to contribute a significant amount of unique variance, sr2 = 0.07, p = .002, and semantic clustering contributed a significant amount of total variance, r2 = 0.224, and unique variance, sr2 = 0.16, p < .001. However, 6% of the variance of in SDFR performance was shared by the two predictors, and the Sobel test indicated that although the contribution of race remained significant, the mediation effect of semantic clustering was significant, z’ = −2.04, p = .04. Table 4a. Hierarchical regression model of predictors of short delay free recall performance β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. Table 4a. Hierarchical regression model of predictors of short delay free recall performance β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. β t p r2 sr2 F R2 p Model 1 15.23 0.13 <.001 Race −0.36 −3.90 <.001 0.13 0.13 Model 2 20.94 0.29 <.001 Race −0.27 −3.14 .002 0.13 0.07 Semantic clustering 0.41 4.83 <.001 0.22 0.16 ΔR2 = 0.16, ΔF = 23.31, p < .001. Concerning LDFR performance (Table 4b), race alone significantly predicted recall with white participants performing above AA participants in Model 1, F(1, 103) = 9.69, R2 = 0.87, p = .002. Race accounted for 9% of the variance in this model, r2 = 0.09. Adding semantic clustering as a mediating factor made for a significantly stronger Model 2 (ΔR2 = 0.19, p < .001), which then accounted for 28% of the variance in long delay performance, F(2, 103) = 19.66, p < .001. Semantic clustering accounted for 24% of the variance, 19% of which was unique, r2 = 0.24, sr2 = 0.19. Race continued to contribute a significant amount of unique variance in Model 2, sr2 = 0.04, p = .026, but 5% of the variance in LDFR performance was shared by race and semantic clustering. The Sobel test verified that the mediation effect of semantic clustering was significant, z’=−2.21, p = .03. Table 4b. Hierarchical regression model of predictors of long delay free recall performance β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. Table 4b. Hierarchical regression model of predictors of long delay free recall performance β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. β t p r2 sr2 F R2 p Model 1 9.59 0.09 .002 Race −0.29 −3.11 .002 0.09 0.09 Model 2 19.66 0.28 <.001 Race −0.20 −2.26 .026 0.09 0.04 Semantic clustering 0.45 5.21 <.001 0.24 0.19 ΔR2 = 0.19, ΔF = 27.15, p < .001. At the recommendation of expert reviewers, multiple regression analyses were also run controlling for participants’ WASI-2 FSIQ scores. With regard to SDFR performance (Table 4c), FSIQ significantly predicted SDFR independently in Model 1, F(1, 97) = 19.59, R2 = 0.17, p < .001, and adding race significantly improved Model 2’s strength (ΔR2 = 0.04, p = .030), with both FSIQ and race significantly contributing to its significance, F(2, 97) = 12.60, R2 = 0.19, p < .001. Adding semantic clustering in Model 3 accounted for further significant variance (ΔR2 = 0.10, p < .001), with race no longer accounting for significant amounts of variance, though FSIQ and semantic clustering each did, resulting in a significant overall model F(3, 97) = 14.04, R2 = 0.19, p < .001. With respect to LDFR performance (Table 4d), FSIQ alone was a significant predictor of variance in Model 1, F(1, 97) = 19.48, R2 = 0.17, p < .001. Adding race in Model 2 did not significantly change the model (ΔR2 = 0.01, p = .197), and race itself was not a significant contributor to the overall significant model, F(2, 97) = 10.65, R2 = 0.18, p < .001. Adding semantic clustering in Model 3 did make a significant change (ΔR2 = 0.13, p < .001), with FSIQ and semantic clustering accounting for significant amounts of variance in the overall significant model, though race did not, F(3, 97) = 14.36, R2 = 0.31, p < .001. Table 4c. Hierarchical regression model of predictors of short delay free recall performance controlling for FSIQ β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. Table 4c. Hierarchical regression model of predictors of short delay free recall performance controlling for FSIQ β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. β t p r2 sr2 F R2 p Model 1 19.59 0.17 <.001 FSIQ 0.41 4.43 <.001 0.17 0.17 Model 2 12.60 0.19 <.001 FSIQ 0.30 2.89 .005 0.17 0.07 Race −0.23 −1.20 .030 0.14 0.04 ΔR2 = 0.04, ΔF = 4.83, p = .030 Model 3 14.04 0.31 <.001 FSIQ 0.22 2.24 .027 0.17 0.04 Race −0.18 −1.80 .075 0.14 0.02 Semantic clustering 0.33 3.69 <.001 0.20 0.10 ΔR2 = 0.10, ΔF = 13.59, p < .001. Table 4d. Hierarchical regression model of predictors of long delay free recall performance controlling for FSIQ. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. Table 4d. Hierarchical regression model of predictors of long delay free recall performance controlling for FSIQ. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. β t p r2 sr2 F R2 p Model 1 19.48 0.17 <.001 FSIQ 0.41 4.41 <.001 0.17 0.17 Model 2 10.65 0.18 <.001 FSIQ 0.34 3.25 .002 0.17 0.09 Race −0.14 −1.30 .197 0.09 0.01 ΔR2 = 0.01, ΔF = 1.68, p = .197 Model 3 14.36 0.31 <.001 FSIQ 0.26 2.57 .012 0.17 0.04 Race −0.08 −0.80 .424 0.09 0.00 Semantic clustering 0.38 4.24 <.001 0.23 0.13 ΔR2 = 0.13, ΔF = 17.96, p < .001. Within white participants, FSIQ and VIQ scores showed the same pattern of correlations with CVLT-II scores; both were positively correlated with SDFR scores, LDFR scores, and semantic clustering usage but not serial clustering usage. Letter-Word Identification scores did not significantly correlate with any CVLT-II performance measures in this group. In contrast, in the AA group, Letter-Word Identification scores correlated positively with use of serial clustering. However, IQ and other CVLT-II measures were not associated with each other in this group. See Table 5 for correlations. Table 5. Correlations between IQ/achievement scores and CVLT-II scores by racial group White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 Note: AA = African American; SDFR = short delay free recall; LDFR = long delay free recall; FSIQ = full scale IQ; VIQ = verbal IQ; ID = identification. Table 5. Correlations between IQ/achievement scores and CVLT-II scores by racial group White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 White participants AA participants Correlate R2 p R2 p FSIQ SDFR 0.12 .008 0.04 .220 LDFR 0.14 .004 0.05 .142 Semantic clustering 0.07 .044 0.01 .576 Serial clustering 0.00 .880 0.00 .765 VIQ SDFR 0.08 .030 0.01 .628 LDFR 0.08 .039 0.00 .896 Semantic clustering 0.07 .046 0.01 .495 Serial clustering 0.00 .712 0.01 .546 Letter-Word ID SDFR 0.05 .121 0.01 .627 LDFR 0.04 .127 0.00 .999 Semantic clustering 0.00 .932 0.07 .080 Serial clustering 0.04 .148 0.11 .025 Note: AA = African American; SDFR = short delay free recall; LDFR = long delay free recall; FSIQ = full scale IQ; VIQ = verbal IQ; ID = identification. Discussion Group comparisons of performance on the CVLT-II were commensurate with previous research on the measure (Norman et al., 2000) with white participants performing better on SDFR, LDFR, and semantic clustering than AA participants. These findings indicated that during list learning, white individuals were more frequently reciting items by category as they recalled them, and they recalled a greater number of words from the list after both a short delay and a long delay. Increased use of semantic clustering also predicted improved short and long delay free recall. Groups were comparable on serial clustering tendencies, i.e., the strategy of recalling items in the order they were presented by the examiner, and no relationships were observed between serial clustering and recall abilities. A lack of quantifiable demographic differences between groups suggested that these factors were not responsible for group differences. Number of years of education and SES did not differ between AA and white participants, nor was there a significant difference in gender ratios. Prior research indicated the necessity of this verification, as women have been shown to outperform men on list-learning tasks (Delis, Kramer, Kaplan, & Ober, 1987; Lowe, Mayfield, & Reynolds, 2003; Norman et al., 2000). Instead, we considered that semantic clustering ability might be a mediator of the effect of race on SDFR and LDFR performance. A multiple regression model predicting SDFR indicated that race alone was a significant predictor of SDFR performance, accounting for 13% of variance when it was the only predictor. However, adding semantic clustering to the model made it significantly stronger, with 29% of all variance accounted for by the two predictors. While semantic clustering did not fully mediate the contribution of race, it was found to be a significant partial mediator; almost half of race’s contribution to SDFR was shared with semantic clustering. Similar results emerged with LDFR performance; while race alone accounted for 9% of variance in recollection after a long delay, semantic clustering made a significant contribution, with the final model accounting for 28% of all variance. In this case, over half of the variance contributed by race was shared with semantic clustering, and semantic clustering was again found to be a significant partial mediator by the Sobel test. Furthermore, when mediation models were run with bootstrapping, both the SDFR and LDFR outcome models were found to be significant. At reviewers’ recommendations, multiple regression analyses controlling for participants’ FSIQ scores were also conducted. Notably, findings regarding the role of semantic clustering as a predictor of recall abilities remained robust; semantic clustering continued to significantly contribute to the variance in short- and long-delay free recall performance beyond IQ and race. However, IQ remained a significant contributor in both final models. Race was also unable to account for variance beyond that accounted for by IQ in long delay free recall, and collinearity remains a concern here. Furthermore, while IQ is an important consideration in the context of the broad neuropsychological literature, these specific findings should be interpreted with caution. IQ testing has a longstanding history of racial bias (Reynolds & Suzuki, 2013), and such an imbalance was noted between the white and AA participants in the present study despite similarities across aforementioned demographic domains. Overall, the present findings indicate that differences in recollection of a verbally presented list between African American and white adults persist between groups with similar basic demographic information. However, much of this difference can be attributed to differences in use of a specific strategy, i.e., mental organization of items by category. Although the use of these more advanced strategies is expected to develop over time in healthy populations, formal education may facilitate elements of cognitive growth in executive functions such as organization and planning despite these abilities being frequently attributed to normal development (Carr, Kurtz, Schneider, Turner, & Borkowski, 1989; Diamond, 2013). The standard measure of education within neuropsychological testing is years of education, and studies have documented its influence on performance across neuropsychological measures (Lezak, Howieson, & Loring, 2004; Norman et al., 2000). Quantitatively, this measure did not differ between our AA and white participants. However, this does not take into account the wide range of quality of schooling across the United States, and research indicates that these straightforward numbers may not be a reflection of the education participants received. Across numerous measures of quality, majority–minority public schools lag behind majority-white schools. These measures include class size, the proportion of novice teachers, and the rate of faculty turnover (Gagnon & Mattingly, 2015; Logan, Minca, & Adar, 2012). In turn, racial disparities are observed on measures administered to same-grade students, such as national exams (Logan et al., 2012). Empirical studies have indicated that African Americans perform below their reported education level on measures of reading abilities (Manly et al., 2002; Bryant et al. 2005), suggesting measurable gaps in the quality of education received between races. As expected based on the literature (Lezak et al., 2004; Manly et al., 1998a, 1998b; Sisco et al., 2013), there were significant discrepancies between AA and white participants’ IQ and reading achievement measures within our study. Particularly remarkable about these scores, however, were the correlations or lack thereof with CVLT-II performance. Both full scale and verbal IQ scores predicted a higher recollection of words and greater use of semantic clustering in white participants, indicating that intelligence predicts both recall ability and strategy use in this group. However, Letter-Word Identification scores, our proxy for academic achievement in reading, showed no correlations with CVLT-II performance in white participants. In contrast, intelligence measures showed no correlation with CVLT-II scores in AA participants. This suggests that even though the group earned lower scores across these measures than their white peers, intelligence as measured by the WASI-2 is not a predictor of their memory abilities or strategy use. However, that Letter-Word Identification performance predicted use of serial but not semantic clustering indicates that those AA individuals who demonstrate better word reading performance use serial recall more often. Reading measures such as our Letter-Word Identification task are often used as a proxy for quality of education. While these measures often correlate with increased scores on cognitive measures, in this case, it appears that the students with higher educational quality as measured by word reading performance are using a less efficient and more cognitively demanding approach to recalling word lists. Development of cognitive strategies may be rooted in one’s education. Reading ability has been found to mediate the relationship between years of education and executive functioning skills such as sequencing, word generation, and pattern identification in older adults (Johnson, Flicker, & Lichtenberg, 2006). Byrd and colleagues found that problem solving abilities on visual tasks could be predicted by reading ability in an older African American sample (Byrd et al., 2005). Many learned elements of task-taking may be attributed to success in school, whether they are expressly learned once the basics are mastered or they are a component of simultaneous development. Fortunately, literature suggests that semantic clustering may be a skill that can be taught in the classroom and subsequently utilized in a way that improves recall as assessed by neuropsychological measures (Carr et al., 1989; Frank, Keene, & Taylor, 1993). As such, our findings suggest that this may be a target area of intervention within the education system; if students of any race are struggling to memorize information, being provided with this strategy may bolster their performance. In a group similar to our healthy sample without memory impairments, learning this strategy may facilitate performance on other tests similarly. That semantic clustering was a significant partial mediator between race and both short and long delay free recall suggests that instruction on semantic clustering approach to recall with African American students may help to improve learning and memory efficiency and performance. It will be necessary for future studies to empirically examine the relationship between quality of schooling and individuals’ strategy use. The present studied included the WJ-III Letter-Word Identification subtest as a measure of academic achievement, but it is not necessarily a reflection of broader education or academic success. Instead, future studies may choose to use measures such as the Wide Range Achievement Test (WRAT-4; Wilkinson & Robertson, 2006) that include elements of multiple academic domains, as has been used by Manly and colleagues (Manly et al., 2002, 2003). Researchers may also consider assessing explicit school-related variables such as student-to-teacher ratio, average number of school days attended, or length of the school term as possible predictors of neuropsychological outcomes similar to Sisco and colleagues (2013). Notably, some variability in free recall remained accounted for by race alone and not by semantic clustering tendencies. Research has suggested that differences in neuropsychological performance between African American and white participants may be rooted in other disparities; aside from differences in school quality, performance may be affected by physical health challenges (Schwartz et al., 2004) and psychosocial factors (Macintyre, Ellaway, & Cummins, 2002). Morgan and colleagues (2008) found that education quality also did not fully mediate racial differences in performance on some cognitive measures, further indicating that it will be necessary to evaluate the effects of these different domains. Future studies may also consider assessing socioeconomic status in a different manner than was presently used. The Hollingshead Four-Factor Index of Social Status was developed in 1975 and may not accurately map onto some of today’s occupations and their level of status. Its use of education level or parent education level in its calculations holds the same limitations as the present study in that it does not consider quality of education. Furthermore, dichotomizing the group into high-mid and low SES segments was done in order to ameliorate these potential small differences in categorization and create larger groups, but it narrowed variability across individuals and limited analysis options. Although data collection in a university is often considered a limiting factor with respect to generalizability, the present study benefited from the diversity of its setting. The majority of participants were enrolled in undergraduate coursework at a 4-year university, but our sample is notably more reflective of the general population than a traditional university sample. The large urban university out of which the study was based consists of 30% first-generation college students and 26% adult learners (U.S. News & World Report, 2013). Over 60% of the undergraduate population at the university are considered underrepresented minority students. Of our participants who provided the data, 74 came from mid to very high socioeconomic status households, and 21 came from low to very low socioeconomic households. Paternal education information was not collected, but participants’ mothers’ education level ranged from a high school degree or less (N = 29) to some college (N = 27) to a college degree or more (N = 40). Therefore, the findings of this study should be considered relatively generalizable to the broader population. The present study is further served by its use of a widely validated neuropsychological measure, the CVLT-II, and its well-defined assessment of strategy use, specifically, serial and semantic clustering (Delis et al., 2010). This quantification of a technique allows researchers to begin to parse apart the root of broader discrepancies in performance between groups, as we have aimed to do in this study. The findings of the current study suggest that semantic clustering, a strategy that was found to lead to increased recall of a list after both a short and long delay, is utilized more by white adults than African American adults. It is also a significant partial mediator of the relationship between race and recall ability, indicating that it may be an area of intervention to mitigate racial differences in cognitive abilities that often emerges in neuropsychological testing. As research suggests that test-taking strategies may be developed alongside educational development, education disparities should be considered as a possible driver of these abilities, although additional health and psychosocial effects should be evaluated as well. Funding This work was supported by a Research Scholar Grant from the American Cancer Society awarded to T.Z.K. (#RSGPB-CPPB-114044), a Georgia State University Brains & Behavior Initiative graduate student fellowship to M.E.F., and a Georgia State University Honors Undergraduate University Assistantship to T.F.P. Conflict of Interest No conflicts of interest exist. 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Archives of Clinical NeuropsychologyOxford University Press

Published: Feb 21, 2018

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