The National Institutes of Health-Toolbox cognition battery (NIH-TCB) is widely used in cognitive aging studies and includes measures in cognitive domains evaluated for dimensional structure and psychometric properties in prior research. The present study addresses a current literature gap by demonstrating how NIH-TCB integrates into a battery of traditional clinical neuropsychological measures. The dimensional structure of NIH-TCB measures along with conventional neuropsychological tests is assessed in healthy older adults.

Baseline cognitive data were obtained from 327 older adults. The following measures were collected: NIH-Toolbox cognitive battery, Controlled Oral Word Association (COWA) letter and animals tests, Wechsler Test of Adult Reading (WTAR), Stroop Color-Word Interference Test, Paced Auditory Serial Addition Test (PASAT), Brief Visuospatial Memory Test (BVMT), Letter-Number Sequencing (LNS), Hopkins Verbal Learning Test (HVLT), Trail Making Test A&B, Digit Span. Hmisc, psych, and GPARotation packages for R were used to conduct exploratory factor analyses (EFA). A 5-factor solution was conducted followed by a 6-factor solution. Promax rotation was used for both EFA models.

The 6-factor EFA solution is reported here. Results indicated the following 6 factors: working memory (Digit Span forward, backward, and sequencing, PASAT trials 1 and 2, NIH-Toolbox List Sorting, LNS), speed/executive function (Stroop color naming, word reading, and color-word interference, NIH-Toolbox Flanker, Dimensional Change, and Pattern Comparison, Trail Making Test A&B), verbal fluency (COWA letters F-A-S), crystallized intelligence (WTAR, NIH-Toolbox Oral Recognition and Picture Vocabulary), visual memory (BVMT immediate and delayed), and verbal memory (HVLT immediate and delayed. COWA animals and NIH-Toolbox Picture Sequencing did not adequately load onto any EFA factor and were excluded from the subsequent CFA.

Findings indicate that in a sample of healthy older adults, these collected measures and those obtained through the NIH-Toolbox battery represent 6 domains of cognitive function. Results suggest that in this sample, picture sequencing and COWA animals did not load adequately onto the factors created from the rest of the measures collected. These findings should assist in interpreting future research using combined NIH-TCB and neuropsychological batteries to assess cognition in healthy older adults.

Performance validity (PVT) and symptom validity tests (SVT) have become standard practice in assessing credibility of neuropsychological profiles and symptom report. While PVTs assess cognitive task engagement, SVTs assess credibility of patient symptom report. Although prior research aimed to conceptualize the relationship between the two validity measure types, it generally focused on SVTs from the Minnesota Multiphasic Personality Inventory (MMPI-2 &RF) and the Structured Inventory of Malingered Symptoms (SIMS; Ord et al., 2021, MMPI-2; Van Dyke et al., 2013). Further studies have demonstrated mixed results, with many studies concluding that symptom and performance validity are separate but related constructs. The current study aimed to assess the relationship between PVTs and SVTs utilizing symptom validity measures from the Personality Assessment Inventory (PAI) across three samples, including neurodevelopmental, psychiatric, and traumatic brain injury groups.

Participants included 634 individuals consecutively referred for neuropsychological assessment who completed the Test of Memory Malingering (TOMM) and the PAI (mean Age = 41.7, SD = 15.7; mean Education = 13.7, SD = 2.7; 53% female; 89% Caucasian). Participants were divided into three groups based on referral, including neurodevelopmental (mean Age = SD = 10.7; mean Education = 13.4, SD = 2.5; 39% female; 79% Caucasian), psychiatric (mean Age = 44.7, SD = 15.0; mean Education = 13.8, SD = 2.8; 58% female; 90% Caucasian), and traumatic brain injury samples (mean Age = SD = 15.5; mean Education = 13.3, SD = 2.3; 50% female; 91% Caucasian). Four structural equation models (latent variable models) were constructed. The first model was fit across the entire sample while the remaining three were fit for the aforementioned subsamples. TOMM trials modeled the performance validity latent variable while SVTs from the PAI modeled the symptom validity latent variable (Positive Impression Management and Defensiveness Index modeled underreporting; Negative Impression Management, Malingering Index, and Cognitive Bias Scale modeled overreporting).

In the full sample model overreporting significantly predicted performance validity (p < 0.001, r = -0.31), indicating higher symptom overreporting related to poorer performance validity while symptom underreporting did not significantly predict performance validity (p = 0.09, r = 0.08). In the neurodevelopmental model overreporting did not significantly predict performance validity (p = 0.44, r = 0.10). Further, symptom underreporting did not significantly predict performance validity (p = 0.40, r = 0.10). Similarly, for the TBI model, overreporting did not significantly predict performance validity (p = 0.82, r = -0.02) and symptom underreporting did not significantly predict performance validity (p = 0.50, r = -0.08). For the psychiatric sample symptom underreporting did not significantly predict performance validity (p = 0.06, r = 0.11); however, symptom overreporting significantly predicted performance validity (p < 0.001, r = - 0.39).

The current study expands on prior research comparing the relationship between SVTs and PVTs in neuropsychological evaluation utilizing SVTs from the PAI. Results of the present study suggest the relationship between the SVTs and PVTs varies by referral type and further supports using both PVTs and SVTs in neuropsychological assessment.

Cognitively healthy individuals who complete a neuropsychological test battery can obtain very low scores. These very low scores are not likely indicative of cognitive impairment but are rather considered spuriously low scores. The expected number of low scores varies based on number and type of neuropsychological tests. Typically, base rates have been determined from normative samples, which could differ from samples seen in clinical settings. The current study reports on base rates of spuriously low cognitive scores in older adults presenting to a memory clinic who were diagnosed with subjective cognitive impairment after interprofessional assessment and information from collateral informants ruled out objective cognitive impairment.

Base rates of spuriously low scores for a neuropsychological battery of 12 scores were based on 92 cognitively healthy older adults presenting to a specialist memory clinic (M(age) = 61.00, SD = 12.00; M(edu) = 12.00, SD = 2.74). Crawford’s Monte Carlo simulation algorithm was used to estimate multivariate base rates by calculating the percentage of cognitively healthy memory clinic patients who produced age and education normed scores at or below the 5th percentile. The following tests were used to produce the 12 scores: block design, digit span backwards, and coding from the WAIS-IV; logical memory I and II from the WMS-IV; immediate and delayed memory scores from the California Verbal Learning Test Second Edition short form; immediate and delayed memory scores from the Brief Visuospatial Memory Test Revised; category switching, letter number sequencing, and inhibition switching from the Delis Kaplin Executive Functioning System.

An estimated 33.58% of the cognitively healthy memory clinic population would have one or more low scores (5th percentile cutoff),14.7% would have two or more low scores, 6.55% would have three or more, 2.94% would have four or more, and 1.31% percent would have 5 or more very low scores due to chance.

Determining base rates of spuriously low scores on a neuropsychological battery in a clinical sample of referred older adults with subjective memory complaints could assist in the diagnostic process. By understanding base rates of clinical samples, clinicians can use empirical data to adjust for expected low scores rather than using conventional corrections (such as 1/20 test scores expected to be low). In a memory clinic sample, three or more low test scores out of 12 is expected to be relatively rare in those who were later determined to have no objective evidence of cognitive impairment based on interprofessional assessment. Understanding normal frequency of low scores will prevent undue conclusions of cognitive impairment which will minimize false positives in diagnosis.

The MMPI-2-RF contains scales that assess different types of invalid response styles, especially potential symptom over-reporting (e.g., F-r, Fs, Fp-r, FBS-r, RBS). However, these scales are not designed to specifically capture noncredible symptoms reports associated with Attention-Deficit/Hyperactivity Disorder (ADHD). Robinson & Rogers (2018) proposed the experimental Dissimulation ADHD validity scale (Ds-ADHD) on the MMPI-2-RF that was effective in distinguishing credible and non-credible ADHD diagnoses via a simulator-based study. Within the current study, the Ds-ADHD scale was compared to the established MMPI-2-RF validity scales within a mixed sample of U.S. Military Veterans.

173 Veterans (Mage = 36.18, SDage = 11.10, Medu = 14.01, SDedu = 2.11, 88% male, 81% White, 17% Black) completed a neuropsychological evaluation which included an internally consistent MMPI-2-RF profile and up to 10 performance validity tests (PVTs) as well as a question about a possible ADHD diagnosis. The credible group was determined if participants passed all PVTs (n=146) and completed at least 2 PVTs. The non-credible group was determined by failing two or more PVTs (n=27). Group assignment was clinically confirmed. The Ds-ADHD scale was calculated according to Robinson & Rogers’ (2018); responses of “true” (i.e., erroneous stereotypes) were coded as 1 and “false” answers were coded 2, creating a 10- to 20-point scale. Thus, lower scores would be associated with a higher likelihood of a feigned ADHD presentation. Other MMPI-2-RF validity scales of interest included F-r, Fs, Fp-r, FBS-r, and RBS.

The established MMPI-2-RF validity scales were significantly correlated with PVT group membership, but correlations were weak to moderately strong (rS ranged from -.43 to -.18; ps < .05). A series of stepwise regression models were completed with the Ds-ADHD scale and one of the MMPI-2-RF validity scales as independent variables, with group membership as the dependent variable. Ds-ADHD) contributed uniquely to each model (CÜ ranged from .03 to .04, ps < .05). The established MMPI-2-RF validity scales effectively classified group membership (AUC values ranged from .57 to .68), and the Ds-ADHD scale had a marginally higher AUC (.69); however, it was not statistically significantly stronger than any of the established scales (ps > .05).

Clinicians interested in identifying potentially simulated ADHD presentations with the MMPI-2-RF may desire to calculate the Ds-ADHD scale, which previously only had support from a simulator-based study. The Ds-ADHD scale significantly contributed to each model, suggesting that it helped explain groups over and above each of the traditional MMPI-2-RF validity scales. However, it only had a marginally stronger ability to classify participants, indicating that there may be diminishing returns for clinicians. Among the traditional validity scales, RBS and F-r best classified groups, and FBS-r was the least effective. This study employed a cross-sectional design in a mixed sample of Veterans undergoing a neuropsychological evaluation. Future research should focus on replicating the findings using a credible sample that was limited to an independently verified diagnosis of ADHD.

The global prevalence of persons living with dementia will soon exceed 50 million. Most of these individuals reside in low- and middle-income countries (LMICs). In South Africa, one such LMIC, the physician-to-patient ratio of 9:10 000 severely limits the capacity of clinicians to screen, assess, diagnose, and treat dementias. One way to address this limitation is by using mobile health (mHealth) platforms to scale-up neurocognitive testing. In this paper, we describe one such platform, a brief tablet-based cognitive assessment tool (NeuroScreen) that can be administered by lay health-providers. It may help identify patients with cognitive impairment (related, for instance, to dementia) and thereby improve clinical care and outcomes. However, there is a lack of data regarding (a) the acceptability of this novel technology for delivery of neurocognitive assessments in LMIC-resident older adults, and (b) the influence of technology-use experience on NeuroScreen performance of LMIC-resident older adults. This study aimed to fill that knowledge gap, using a sample of cognitively impaired South African older adults.

Participants were 60 older adults (63.33% female; 91.67% right-handed; age M = 68.90 years, SD = 9.42, range = 50-83), all recruited from geriatric and memory clinics in Cape Town, South Africa. In a single 1-hour session, they completed the entire NeuroScreen battery (Trail Making, Number Speed, Finger Tapping, Visual Discrimination, Number Span Forward, Number Span Backward, List Learning, List Recall) as well as a study-specific questionnaire assessing acceptability of NeuroScreen use and overall experience and comfort with computer-based technology. We summed across 11 questionnaire items to derive a single variable capturing technology-use experience, with higher scores indicating more experience.

Almost all participants (93.33%) indicated that NeuroScreen was easy to use. A similar number (90.00%) indicated they would be comfortable completing NeuroScreen at routine doctor's visits. Only 6.67% reported feeling uncomfortable using a tablet, despite about three-quarters (76.67%) reporting never having used a tablet with a touchscreen before. Almost one in five participants (18.33%) reported owning a computer, 10.00% a tablet, and 70.00% a smartphone. Correlations between test performance and technology-use experience were statistically significant (or strongly tended toward significance) for most NeuroScreen subtests that assessed higherorder cognitive functioning and that required the participant to manipulate the tablet themselves: Trail Making 2 (a measure of cognitive switching ability), r = .24, p = .05; Visual Discrimination A (complex processing speed [number-symbol matching]), r = .38, p = .002; Visual Discrimination B (pattern recognition), r = .37, p = .004; Number Speed (simple information processing speed), r = .36, p = .004. For the most part, there were no such significant associations when the NeuroScreen subtest required only verbal input from the participant (i.e., on the list learning and number span tasks).

NeuroScreen, a tablet-based neurocognitive screening tool, appears feasible for use among older South Africans, even if they are cognitively impaired and have limited technological familiarity. However, test performance might be influenced by amount of technology-use experience; clinicians using the battery must consider this in their interpretations.

Executive functions (EFs) refer to a set of top-down cognitive processes that are fundamental for the control of goal directed behaviours (Lezak et al., 2004). Inhibition (the capacity to ignore irrelevant information) and selective attention (the capacity to selectively focus on relevant information) are considered as the core components of EFs (Barkley, 2001; Veer et al., 2017). EFs can be impaired following brain damage (Chung et al., 2013) and they are traditionally assessed individually, using paper-and-pencil tests that have long been criticized for their ecological and sensitivity limitations (Dugbartey et al., 1999; Miyake et al., 2000). Here we developed a serious game in immersive virtual reality to measure inhibition and selective attention based on the go/no-go paradigm and the D2 Test.

Sixty healthy participants were asked to perform a series of tasks, where in each task, the target was a mole wearing a coloured helmet. In task A, either the target or a distractor bomb was presented. The participants had to respond to the target and inhibit a response to the bomb. In task B, the target was presented with distractor moles wearing different coloured helmets. The two tasks could also be combined, task AB, where the target was presented with distractors (as in task B) versus the bomb was presented with distractor moles. All the stimuli appeared from four molehills aligned to sagittal axis (near to far from the participant). Responses were made with the dominant hand in task A and with both tasks in tasks B and AB. The participants were instructed to hit the target with a virtual hammer.

Response time analysis showed that in tasks A, B and AB, participants were slower to respond to the far compared to near targets. In task B and AB, participants were additionally slower to respond to the left compared right targets. Significant interactions between laterality and proximity for tasks B and AB showed that the participants were significantly slower to response to left vs right target in both far and near conditions. All participants were able to inhibit responses to the bomb and distractor stimuli.

In conclusion, we have developed a novel serious game in immersive virtual reality for the assessment of inhibition and selective attention, both as individual tests and as a combined test. Future studies will test patients with executive dysfunction to test the validity of this new serious game.

Risk factors that contribute to brain pathology and cognitive decline among older adults include demographic factors (e.g., age, educational attainment), genetic factors, health factors, and depression (Plassman et al., 2010). Variability within an individual’s performance across cognitive tasks is referred to as dispersion (Hultsch et al., 2002), which appears sensitive to subtle cognitive impairments associated with neurodegenerative pathology in older adults (Bangen et al., 2019; Kälin et al., 2014). Thaler and colleagues (2015) found that dispersion across domains of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was a useful indicator of cognitive changes associated with cardiovascular disease and mortality. Also, research by Manning and colleagues (2021) found that elevated ratings of depression and anxiety in older adults was associated with greater dispersion across neuropsychological testing. The present study aimed to replicate findings that greater dispersion in neuropsychological performance is associated with impaired neurocognitive performance and greater self-reported depression among older adults who present for neuropsychological evaluation with cognitive concerns.

Neuropsychological testing data was obtained from a university hospital. Chart reviews were conducted on 369 participants who met initial criteria (60 years or older with testing data from the RBANS Form A, Wechsler Test of Adult Reading, and Geriatric Depression Scale [GDS]). Retrospective analyses were conducted on a final sample of 293 participants from 60 to 94 years old (Mage = 74.41, SDage = 7.43; 179 females, 114 males). Diagnoses were used for group comparisons between cognitively intact individuals with subjective cognitive complaints (SCC, n = 49), persons with Mild Neurocognitive Disorder (mND, n =137), and persons with Major Neurocognitive Disorder (MND, n = 107).

As expected, results indicated that higher dispersion was related to lower Total RBANS Scores (r = -0.54, p < .001) and significant differences across diagnostic groupings (F(2, 289) = 29.19, p < 0.001; SCC, mND, MND) indicated that variability in performance was an indicator of greater neurocognitive impairment. Contrary to expectations, greater dispersion was very weakly associated with lower reported depressive symptomatology (r = -0.13, p = 0.03). A three-stage hierarchical linear regression was conducted with the RBANS Coefficient of Variation (CoV) as the dependent variable and three predictor variables (Age, Total RBANS, Total GDS). The regression analysis results indicated that age was not a significant predictor, but both Total RBANS and GDS Scores were. The most important predictor was Total RBANS Scores which uniquely explained 21% of the variation in dispersion.

This study adds to the current literature regarding the clinical utility of dispersion in neuropsychological performance as an indicator of early and subtle neurocognitive impairment. Depressive symptom reporting was expected to help predict the degree of variability, but this factor was only weakly associated with the RBANS CoV.

Limitations of this study include its retrospective use of archival data and the restricted range on some variables of interest. Further research is needed to examine the relative utility of different measures of dispersion and why increased cognitive performance variability is related to neurocognitive impairment and decline.

Determine the classification concordance between a standalone performance validity test (PVT) and embedded PVTs from multiple cognitive domains.

Participants were 106 patients (49.1% female; 69% white) that underwent neuropsychological evaluation at an outpatient university doctoral clinical psychology training and research clinic (M/SD: age = 32.38/11.95; education = 13.7/2.75). A comprehensive neuropsychological battery included the Medical Symptom Validity Test (MSVT) and embedded PVTs from different cognitive domains: attention - Wechsler Adult Intelligence Scale - Fourth Edition Reliable Digit Span and Digit Span age-corrected scaled score (DS ACSS); memory - California Verbal Learning Test, 3rd Edition (CVLT-3) Forced-Choice Recognition (FCR), executive functions -Wisconsin Card Sorting Test (WCST) Failure to Maintain Set (FMS); visual-spatial/construction -Rey Complex Figure Test (RCFT) Copy raw score; language - Boston Diagnostic Aphasia Examination Complex Ideation Material (CIM); and motor functions - Finger Tapping Test (FTT). All participants were administered the MSVT but not all participants were administered all seven embedded PVTs. Credible/noncredible classification concordance rates and kappa correlations (i.e., percentage of agreement) were computed for each pairwise PVT combination.

Twenty-two percent (n = 23) of the sample failed at least one PVT, with 17.0% (n = 18) failing at least two. DS ACSS was the embedded PVT with the highest MSVT concordance rate at 92.4% and a fair kappa coefficient of .39; WCST FMS had the lowest concordance with MSVT at 82.9% and a slight kappa coefficient of .19. The highest concordance among embedded PVTs from different cognitive domains was CVLT-3 FCR and RCFT Copy raw score at 89.7% with a fair kappa coefficient of .35; the lowest agreement among embedded PVTs was WCST FMS and FTT at 74.0% with a kappa coefficient of -.02. More conservative kappa coefficients among all pair-wise embedded PVT combinations from different cognitive domains ranged from -.02 to .36. For all standalone and embedded PVT pairwise concordance rates, only two fell below the recommended minimum agreement of 80%: FCR vs. FMS = 79.3% and FMS vs. FTT = 74.0%.

Embedded PVTs across various cognitive domains have high agreement with a standalone PVT to aid in classifying noncredible performance, in the 83-92% range. Embedded PVTs from different cognitive domains also have mostly high agreement classification rates amongst themselves in aiding to determine noncredible performance, in the 74-90% range, with the lowest agreement rate between executive function and motor tests at 74%. More conservative kappa-based agreements between PVT pair-wise combinations were fairly consistent with other studies, with most being in the fair range. Finally, these findings indicate about a 17% base rate of noncredible cognitive performance in an outpatient university-based clinic.

There are two forms of intra-individual variability (IIV) in the literature: inconsistency, or variability on one task across many time points, and dispersion, or variability across many tasks at one time point. These forms of IIV are often lumped together into one construct, but there is limited evidence supporting this practice, as few studies have examined the relationship between these measures. Additionally, it is not clear how stable these constructs are over time. Therefore, the goal of the present study was (1) to explore the relationship between (a) inconsistency and dispersion and (b) IIV and mean performance, and (2) to determine whether these relationships are stable over a one- to two-year follow-up interval.

A total of 123 community-dwelling older adults (Mage=69.5, Meducation=15.6 years) from an archival database completed the Push-Turn-Taptap task to assess inconsistency and the Delis-Kaplan Executive Functioning System (D-KEFS) to assess dispersion. These measures were selected because both are highly executive, thereby allowing us to hold the cognitive domain constant across forms of IIV. Dispersion was calculated by taking the standard deviation of the executive conditions of four D-KEFS subtests (Verbal Fluency, Design Fluency, Trail Making, and Color Word Interference). Follow-up data were collected one to two and a half years after baseline. Bivariate and partial correlations controlling for time to follow-up were examined.

Inconsistency and dispersion were not correlated at baseline but were weakly correlated at follow-up (r=.281, p=.012). Additionally, both forms of IIV were moderately correlated with themselves across the follow-up interval (inconsistency: r=.450, p<.001; dispersion: r=.448, p<.001). The partial correlations were nearly identical to bivariate correlations.

Additionally, inconsistency was correlated with poorer mean executive functioning (EF) performance on both the PTT (baseline: r=.281, p<.001, follow-up: r=.435, p<.001) and D-KEFS (baseline: r= -.270, p=.003, follow-up: r=-.573, p<.001). In contrast, dispersion was correlated with mean EF performance only on the D-KEFS at baseline (r= -.292, p<.001) but with both measures at follow-up (PTT: r=.232, p=.039; D-KEFS: r= -.378, p<.001). When controlling for follow-up interval, inconsistency was no longer correlated with baseline mean PTT performance, but all other relationships remained the same (i.e., dispersion and inconsistency displayed the same pattern of correlations with mean EF).

Although inconsistency and dispersion are both forms of IIV, they are weakly related. In other words, although they may have shared mechanisms, these two methods of measuring IIV likely represent different constructs. In the future, authors should take care to specify the form of IIV being investigated in their publications rather than referring to either form as IIV generally. Additionally, both forms of IIV are weakly to moderately correlated with mean EF performance, indicating that IIV is related to but separate from mean-level performance. Interestingly, IIV and mean performance were more strongly correlated at follow-up, which may be suggestive of incipient cognitive decline. Lastly, it seems that both inconsistency and dispersion are somewhat stable across a one- to two-year follow-up interval, suggesting that IIV may be a trait-level construct to some extent. However, IIV may also be influenced by state-level contextual factors, and more research examining the stability of and contributors to IIV is necessary.

Assessing one’s functional capacity

– in addition to neuropsychological performance

– is essential for determining neurocognitive status, and functional assessment is often provided via informant report. While informant characteristics have been shown to influence reports of participant functioning, the degree to which they moderate relationships between reported functioning and participant performance on neuropsychological testing is unclear. Moreover, associations among informant characteristics, reported functioning, and neuropsychological performance have not been directly examined with non-Hispanic Black (NHB) samples, despite this population’s disproportionately high risk for dementia.

In this cross-sectional observational study, we examined the influence of informant characteristics on (1) informant reports of participant functioning (assessed via the Functional Activity Questionnaire [FAQ]), and (2) associations between reported functioning and participant performance on neuropsychological testing, among NHB adult participants in the National Alzheimer’s Coordinating Center cohort (n=1024).

Younger age, female sex/gender, higher education, longer relationships with participants, and cohabitation were informant characteristics associated with poorer reported functioning (ps<.01). Moreover, poorer reported functioning was associated with poorer performance on (1) memory and language tests, particularly for participants with male (versus female) informants, and (2) the Multilingual Naming Test, particularly for participants with cohabitating (versus non-cohabitating) informants (ps<.01).

Within the context of neurocognitive evaluation of NHB adults, informant age, sex/gender, education, relationship length, and cohabitation status influence informant reports of participant functioning, and informant sex/gender and cohabitation status in turn moderate associations between reported functioning and participant performance on comprehensive neuropsychological testing.

Craft Story 21 is a practical, comprehensive, and freely available tool to assess logical memory in patients with memory impairment. Currently, the test does not have normative values in Spanish that adjust to our specific population. Furthermore, the original test does not have a recognition phase to increase the specificity of the memory profile by allowing a distinction between different amnesic profiles. Therefore, this study has two main aims: 1) the generation of normative data for the Craft Story 21 memory test, adjusting to the characteristics of our Spanish-speaking country according to sex, age, and educational level; and 2) the design and validation of the recognition phase of the test and the assessment of its psychometric properties.

The baremization sample comprised 81 healthy participants aged 41 to 91, assessed through the Uniform Data Set III (UDS III) battery of the National Alzheimer’s Coordinating Center (NACC). The design of the recognition phase included three steps: (1) construction of the scale and review by experts, (2) pilot study, and (3) analysis of its psychometric properties. In the latter, 190 participants were recruited and classified into two groups matched by age, sex, and educational level: Mild Cognitive Impairment (MCI n=96) according to Petersen’s (1999) criteria and healthy controls (HC n=94). In addition, the diagnostic accuracy of the test was studied by the ROC curve method, its concurrent validity by correlation with other memory tests (RAVLT), and its internal consistency with Cronbach’s alpha test.

The Baremization sample was divided into 16 groups: 4 age groups (41-51, 51-61, 6171 and >72 years), two educational levels (6-12 years and >12 years), and sex (male and female). Performance was significantly different between age groups (p < 0.003**). No significant differences were found in Craft Story 21 performance between education (p > 0.09) or sex (p > 0.56) groups within the same age group. Normative values in terms of means and standard deviations are presented for each group. Regarding the design of the recognition phase, the groups did not show significant differences in age (p= 0.13), sex (p= 0.88), or schooling (p= 0.33). The overall score of Craft Story 21 test showed the ability to discriminate between healthy controls from patients with MCI (sensitivity = 81.6% and specificity = 72.4%). Its diagnostic accuracy by phase (immediate AUC= 0.86; delayed AUC= 0.86 and recognition AUC= 0.75) was superior than Rey Auditory and Verbal Learning Test (RAVLT): immediate (AUC= 0.79), delayed (AUC= 0.82) and recognition (AUC= 0.74). It presented evidence of concurrent validity with RAVLT in its immediate (r=0.56, p<0.001), delayed (r= 0.66, p<0.001) and recognition (r= 0.37, p<0.001) trails. The instrument also presented evidence of reliability (a= 0.82).

The Craft Story 21 test is a practical, brief and multicultural scale. Thus having appropriate scales for the specific population to be assessed to a more accurate and precise description of the memory profile. Additionally, the new Recognition phase of the test showed evidence of validity and reliability for assessing memory processes.

Global neurocognitive impairment (NCI) has been reported in white people living with HIV/AIDS (PLWHA) in 40%. In Latino populations there have been variable rates described from 30 to 77%. This variation has to do with the lack of normative data for Latino population and the application of norms for English-speakers, increasing the probability of misidentification of NCI. Thus, recognizing which are the best norms available for the Mexican population is important for the accurate identification of NCI. The aim of the present study was to investigate the rate and pattern of HIV associated neurocognitive impairment (NCI) and to compare rates of NCI between rates calculated using norms for the Latin-American population (NLAP) and norms for the US-Mexico border region (NP-NUMBRS).

CIOMS international ethical guidelines for the participation of human subjects in health research were followed. 82 PLWHA living in Tijuana (Mexico) participated in the study (Age: Mean=39.6, SD=10.9; 28.3% Female; Years of education: Mean=8.5, SD=3.6). PLWHA were recruited from the board-and-care home “Las Memorias” (73.4% on antiretroviral therapy; Years since HIV diagnosis: Mean=9.9, SD=7.1). Participants completed a neuropsychological test battery sensitive to detect HIV associated NCI that assessed four cognitive domains (verbal fluency, speed of information processing, executive function and learning/memory). Raw scores in these tests were transformed to percentiles using LAPN and transformed to T-scores using NP-NUMBRS. T-scores were averaged across tests to compute domain specific and global impairment scores. NCI was defined as percentile scores <16 and T-scores < 40. McNemar’s tests were used to compare the rate of NCI utilizing NLAP vs NP-NUMBRS.

According to NLAP, rates of global NCI were about 13.4%. Utilizing NP-NUMBRS rates of global NCI were about 34.1%. However, there is a positive and significant correlation between Global Neurocognitive Function score in PLWHA according to NLAP and NP-NUMBRS (r=0.66, p<.05). Rates of global NCI in PLWHA were significantly lower when using LAP norms (McNemar Chi-Square=29.89; p<.001). Regarding the pattern of NCI according both norms learning and memory was the most affected cognitive domain with 34% of impairment according to NLAP vs 51% of impairment according to NP-NUMBRs.

Utilizing NP-NUMBRS, rates of NCI are consistent with findings of prior studies. Employing norms for LAP the rates of NCI are lower that the ones reported in the literature. This is an important finding since PLWHA included in the sample have several vulnerable factors such as deportation, prostitution, drug abuse and discrimination for sexual preference, factor that could impact cognition. The pattern of neurocognitive function was also similar to those of prior studies in HIV. To accurately make NCI diagnosis it is important to use norms that consider specific characteristics of the population. The diagnosis of NCI is important since these deficits present a strong risk of concurrent problems in a wide range of health behaviors like medication non-adherence in PLWHA.

Education is known to impact neuropsychological test performance, and self-reported years of education is often used in stratifying normative data. However, this variable does not always reflect education quality, particularly among underrepresented populations, and may overestimate cognitive impairment in individuals with low education quality. This cross-sectional study evaluated relative contributions of years of education and reading level to several verbally mediated assessments to improve interpretation of neuropsychological performance.

Data was obtained from the Vanderbilt Memory and Aging Project. Cognitively-unimpaired participants (n=175, 72±7 years, 59% male, 87% Non-Hispanic White, 16±2 years of education) completed a comprehensive neuropsychological protocol. Stepwise linear regressions were calculated using education and Wide Range Achievement Test (WRAT)-3 Reading subtest scores as predictors and letter fluency (FAS, CFL), category fluency (Vegetable and Animal Naming), the Boston Naming Test (BNT), and California Verbal Learning Test (CVLT)-II as outcomes to assess increase in variance explained by educational quality. Models covaried for age and sex. The False Discovery Rate (FDR) based on the Benjamini-Hochberg procedure (Benjamini & Hochberg, 1995) was used to correct for multiple comparisons.

The mean WRAT-3 score was 51±4 (range:37-57), indicating post-high school reading level. Education and WRAT-3 scores were moderately correlated (r=0.36, p<0.01). Both WRAT-3 and years of education independently predicted letter fluency (WRAT-3 p<0.001; education p<0.02), category fluency (WRAT-3 p<0.001; education p<0.05), and CVLT-II performance (WRAT-3 p-values<0.005; education p-values<0.02) in single predictor models. On BNT, WRAT-3 (p<0.001), but not education (p=0.06), predicted performance in single predictor models. In combined models with both WRAT-3 and education, WRAT-3 scores remained a significant predictor of FAS (WRAT-3 b=1.21, p<0.001; education b=0.006, p=0.99) and CFL performance (WRAT-3 b=1.02, p<0.001; education b=0.51, p=0.14). Both WRAT-3 (b=0.21, p=0.01) and years of education (b=0.35, p=0.03) predicted Animal Naming, while WRAT-3 (b=0.16,p=0.008), but not years of education (p=0.37), predicted Vegetable Naming. WRAT-3 was a significant predictor of BNT performance (b=0.21, p<0.001) but not years of education (p=0.65). WRAT-3 predicted CVLT-II learning (b=0.32, p=0.04), immediate recall (b=0.16, p=0.005), and delayed recall performances (b=0.15, p=0.005), while education did not (p-values>0.14). All significant results persisted after FDR correction. WRAT-3 scores explained an additional level of variance beyond the covariates and education alone for FAS (AR=18%), CFL (AR=13%), Animal Naming and Vegetable Naming (AR= 3%), BNT (AR=18%), and CVLT-II learning (AR=2%), immediate recall (AR=4%), and delayed recall (AR=3%).

Reading level more strongly associated with performance on several verbally mediated neuropsychological measures than years of education. For all measures, the addition of reading level significantly increased the amount of variance explained by the model compared to covariates and education alone, which aligns with existing research. However, most of this past work looks at individuals with lower levels of educational quality. Because our cohort was highly educated and at the upper end of the reading spectrum, our results suggest that reading level is important to consider even for more highly educated individuals. Therefore, reading level is a critical variable to consider when interpreting verbally mediated neuropsychological measures for individuals across the educational spectrum.

The psychometric properties of the Salthouse Listening Span (SLS; Salthouse & Babcock, 1991) task are relatively unknown.

Previous research has demonstrated that SLS performance is positively associated with processing speed and vocabulary (Salthouse, 2005). Further research has documented that SLS performance is useful in differentiating attention deficit/hyperactive disorder from other clinical conditions (Nikolas, Marshall, & Hoelzle, 2019). While the SLS task is purported to measure working memory, relatively little is known about how the task is related to frequently administered neuropsychological measures. Furthermore, it remains unclear whether emotional functioning may affect task performance. The current study investigates associations between frequently administered tasks and the SLS as well as the impact of anxiety and depression on SLS performance.

A battery of neuropsychological tasks and self-report measures was administered to undergraduates [N=161, 75.2% female, Mage=19 (1.06), MGPA=3.5(.35)]. Participant exclusion based on failed performance validity task, non-native English speaking, and/or task incompletion resulted in a final sample of N=131. Participants completed the SLS, a task where one answers questions about sentences read aloud to them, while simultaneously attempting to remember the final word from sentences. SLS performance was quantified two ways: (1) longest span score (SLS-LSS) and (2) total words recalled correctly (SLS-WRC). Anxiety and depression were measured via the Beck Anxiety Index (BAI) and the Beck Depression Index (BDI). Two groups were derived based on participant BAI and BDI responses: low to mild emotional distress (N=99, scores of 0-15 on BAI, BDI, or both) and moderate to severe emotional distress (N=33, scores of 16-63 on the BDI, BAI, or both). Correlations were conducted between the SLS and WAIS-IV: digit span, arithmetic, coding, and symbol search, DKEFS: verbal fluency, and WTAR. A one-way ANOVA was run to examine potential differences in performance on the SLS based on levels of emotional distress.

The SLS-LSS had negligible correlations with verbal fluency, coding, or symbol search performances (r<0.1). SLS-LSS demonstrated a small to medium positive correlation with arithmetic [r(130)=0.17, p=.06], digit span [r(130)=0.27, p=.002], and WTAR [r(130)=.27, p=.002]. SLS-WRC did not demonstrate meaningful correlations with any cognitive domain. Overall, the presence of moderate anxiety and/or moderate depression did not significantly affect performance on SLS- LSS [F(1, 130)= 1.5, p=0.22] or SLS-WRC [F(1, 132)=0.55, p=0.46].

The SLS is a promising cognitive task with little research investigating its psychometric properties. Overall, minimal correlations were observed with tasks quantifying executive functioning, verbal abilities, and processing speed. Lack of strong correlations indicate that more research should be conducted to fully understand what this task is measuring. Moreover, the SLS-WRC score did not appear to have significant correlations across domains, indicating that the SLS-LSS may be more strongly related to working memory and general intelligence. Encouragingly, emotional functioning did not appear to impact performance on this task. While the SLS appears to have some relation to IQ, more research should be conducted to determine what this task measures and what variables may affect task performance.

Neuropsychiatric/behavioral-psychological symptoms of dementia (BPSD) frequently contribute to worse prognosis of patients with neurodegenerative conditions. BPSD are commonly measured via a brief, informant-rated version of the Neuropsychiatric Inventory (NPI), the NPI-Q. Previously (see our other submission to this conference), we established optimal latent structures by comparing different factor models in the literature using confirmatory factor analyses (CFAs). However, questions remain as to why so many different models were found in the literature. One possibility is sampling differences, including different proportions of individuals across cognitive stages (e.g., mild cognitive impairment, moderate dementia) or syndromes (e.g., Alzheimer’s amnestic syndrome, Dementia with Lewy Bodies). We tested this hypothesis by subjecting candidate models to measurement invariance (MI) analyses stratified by cognitive stage and syndrome.

Individuals were included if they had completed an NPI-Q during their first visit at an Alzheimer Disease Research Center reporting to the National Alzheimer Coordinating Center (NACC). This resulted in 20,500 individuals (57% female; 80% White, 13% Black, 8% Hispanic), with a mean age of 71 (SD = 10.41) and 15 average years of education (SD = 3.43). Regarding staging, 75.9% of individuals did not meet criteria for all-cause dementia, whereas 24.1% individuals had all-cause dementia. Regarding syndromes, 35.6% had an Alzheimer’s presentation (“AD-type”) and 5.6% had either a behavioral variant frontotemporal dementia or Lewy-Body dementia presentation (“behavioral-type”). A 3-factor and 4-factor model were subject to MI across these groupings. We conducted MI analyses for equal forms, equal loadings, and equal intercepts using the lavaan R package with a diagonally weighted least squares (DWLS) estimator.

The 3-factor model demonstrated good fit among individuals experiencing (CFI = 0.965, TLI = 0.955) and not experiencing (CFI = 0.984, TLI = 0.979) dementia, as well as among AD-type (CFI = 0.983, TLI = 0.978) presentations, but had borderline poor fit for behavioral-type (CFI = 0.932, TLI = 0.912) presentations. The 4-factor model had better fit among those experiencing (CFI = 0.985, TLI = 0.977) and not experiencing (CFI = 0.995, TLI = 0.992) dementia. Additionally, the 4-factor model demonstrated good of fit for AD-type (CFI = 0.993, TLI = 0.989) and poorer fit for behavioral-type (CFI = 0.949, TLI = 0.922) syndromes. Chi-square differences suggested that equal loading and equal intercept hypotheses should be rejected for both 3- and 4-factor models, for both staging and syndromal groupings. However, relative fit indices suggested that the equal form, equal loading, and equal intercept hypotheses could be adequate for only the 4-factor model.

The variability of factor structures in the BPSD literature appears, at least partially, explained by sampling variability among cognitive stages and dementia syndromes. The best models in the literature appear to have good fit in non-demented individuals and, among those who have dementia, in those with an AD syndrome. Only Sayegh & Knight’s 4-factor model had adequate (albeit, not optimal) fit among those with all-cause dementia and, more specifically, among those with a behavioral-type dementia syndrome. These findings inform BPSD theory and practical implementation of NPI-Q subscales.

Existing research has demonstrated that neuropsychiatric/behavioral-psychological symptoms of dementia (BPSD) frequently contribute to worse prognosis in patients with neurodegenerative conditions (e.g., increased functional dependence, worse quality of life, greater caregiver burden, faster disease progression). BPSD are most commonly measured via the Neuropsychiatric Inventory (NPI), or its briefer, informant-rated questionnaire (NPI-Q). Despite the NPI-Q’s common use in research and practice, there is disarray in the literature concerning the NPI-Q’s latent structure and reliability, possibly related to differences in methods between studies. Also, hierarchical factor models have not been considered, even though such models are gaining favor in the psychopathology literature. Therefore, we aimed to compare different factor structures from the current literature using confirmatory factor analyses (CFAs) to help determine the best latent model of the NPI-Q.

This sample included 20,500 individuals (57% female; 80% White, 12% Black, 8% Hispanic), with a mean age of 71 (SD = 10.41) and 15 average years of education (SD = 3.43). Individuals were included if they had completed an NPI-Q during their first visit at one of 33 Alzheimer Disease Research Centers reporting to the National Alzheimer Coordinating Center (NACC). All CFA and reliability analyses were performed with lavaan and semTools R packages, using a diagonally weighted least squares (DWLS) estimator. Eight single-level models using full or modified versions of the NPI-Q were compared, and the top three were later tested in bifactor form.

CFAs revealed all factor models of the full NPI-Q demonstrated goodness of fit across multiple indices (SRMR = 0.039-0.052, RMSEA = 0.025-0.029, CFI = 0.973-0.983, TLI = 0.9670.977). Modified forms of the NPI-Q also demonstrated goodness of fit across multiple indices (SRMR = 0.025-0.052, RMSEA = 0.0180.031, CFI = 0.976-0.993, TLI = 0.968-0.989). Top factor models later tested in bifactor form all demonstrated consistently stronger goodness of fit regardless of whether they were a full form (SRMR = 0.023-0.035, RMSEA = 0.015-0.02, CFI = 0.992-0.995, TLI = 0.985-0.991) or a modified form (SRMR = 0.023-0.042, RMSEA = 0.015-0.024, CFI = 0.985-0.995, TLI = 0.9770.992). Siafarikas and colleagues’ (2018) 3-factor model demonstrated the best fit among the full-form models, whereas Sayegh and Knight’s (2014) 4-factor model had the best fit among all single-level models, as well as among the bifactor models.

Although all factor models had adequate goodness of fit, the Sayegh & Knight 4-factor model had the strongest fit among both single-level and bifactor models. Furthermore, all bifactor models had consistently stronger fit than single-level models, suggesting that BPSD are best theoretically explained by a hierarchical, non-nested framework of general and specific contributors to symptoms. These findings also inform consistent use of NPI-Q subscales.

Precariously housed individuals are exposed to multiple adverse factors negatively impacting neurocognitive functioning. Additionally, this population is subjected to poor life outcomes, such as impaired psychosocial functioning. Neurocognitive functioning plays an important role in psychosocial functioning and may be especially critical for precariously housed individuals who face numerous barriers in their daily lives. However, few studies have explicitly examined the cognitive determinants of functional outcomes in this population. Cognitive intraindividual variability (IIV) involves the study of within-person differences in neurocognitive functioning and has been used as marker of frontal system pathology. Increased IIV has been associated with worse cognitive performance, cognitive decline, and poorer everyday functioning. Hence, IIV may add to the predictive utility of commonly used neuropsychological measures and may serve as an emergent predictor of poor outcomes in at-risk populations. The objective of the current study was to examine IIV as a unique index of the neurocognitive contributions to functional outcomes within a large sample of precariously housed individuals. It was hypothesized that greater IIV would be associated with poorer current (i.e., baseline) and long-term (i.e., up to 12 years) psychosocial functioning.

Four hundred and thirty-seven adults were recruited from single-room occupancy hotels located in the Downtown Eastside of Vancouver, Canada (Mage = 44 years, 78% male) between November 2008 and November 2021. Baseline neurocognitive functioning was assessed at study enrolment. Scores from the Social and Occupational Functioning Assessment Scale (SOFAS), the Role Functioning Scale (RFS), the physical component score (PCS) and the mental component score (MCS) of the 36-Item Short Form Survey Instrument were obtained at participants’ baseline assessments and at their last available follow-up assessment to represent baseline and long-term psychosocial functioning, respectively. Using an established formula, an index of IIV was derived using a battery of standardized tests that broadly assessed verbal learning and memory, sustained attention, mental flexibility, and cognitive control. A series of multiple linear regressions were conducted to predict baseline and long-term social and role functioning (average across SOFAS and RFS scores), and PCS and MCS scores from IIV. In each of the models, we also included common predictors of functioning, including a global cognitive composite score, age, and years of education.

The IIV index and the global composite score did not explain a significant proportion of the variance in baseline and long-term social and role functioning (p > .05). However, IIV was a significant predictor of baseline (B = -3.84, p = .021) and long-term (B = -3.58, p = .037) PCS scores, but not MCS scores (p > .05). The global composite score did not predict baseline or long-term PCS scores.

IIV significantly predicted baseline and long-term physical functioning, but not mental functioning or social and role functioning, suggesting that IIV may be a sensitive marker for limitations in everyday functioning due to physical health problems in precariously housed individuals. Critically, the present study is the first to show that IIV may be a useful index for predicting poor long-term health-related outcomes in this population compared to traditional neuropsychological measures.

Digit Span has been a core Working Memory task, with extensive research conducted on the Forward and Backward components. The latest revision of the WAIS-IV introduced the Sequencing component, designed to increase the working memory and mental manipulation demands. However, relatively little research has been done to understand how Sequencing can be interpreted in clinical settings, as compared to Forward and Backward. The purpose of this study was to investigate how effectively individual components of the Digit Span task predict performance on four independent neuropsychological measures with high working memory demands.

Subjects included 148 adults (Age: M= 39.22, SD= 13.61; Handedness= 130 right, 10 left and 8 mixed; Males = 88) with refractory epilepsy. Two subjects had primary generalized seizures while 146 subjects had complex partial seizures (EEG Localization: 44 right temporal; 60 left temporal; 24 independent bitemporal; 1 left extratemporal; 17 indeterminant). Dependent variables included the 2.4 second ISI trial of the Paced Auditory Serial Addition Task (PASAT); the sum of correct responses on Trial 1 and List B of the California Verbal Learning Test (CVLT); the DKEFS Tower Test raw score; and completion time on Part B of the Trail Making Test. The independent variables included the individual raw scores for the Forward, Backward and Sequencing components of the WAIS-IV. Hierarchical linear regression was conducted to determine the variance accounted for by each component of the Digit Span and if that variance was redundant or unique. The four dependent variables were analyzed separately with Digits Forward, Backward and Sequencing entered in a single block.

PASAT: The overall model was significant, R2= 0.36. When examining the individual components, Sequencing was the only significant predictor (ß = 0.422, p < 0.001). CVLT: The overall model was significant, R2 = 0.203. When examining the individual components, Sequencing was the only significant predictor (ß = 0.410, p < 0.001). Tower Test: The overall model was significant, R2 = 0.176. When examining the individual components, Sequencing was the only significant predictor (ß = 0.373, p = 0.004). Trail Making: The overall model was significant R2 = 0.315. When examining the individual components both Forward (ß = -0.287, p =0.005) and Sequencing (ß= -0.364, p < 0.001) accounted for a significant amount of the variance.

The combined model for Digit Span accounted for significant amounts of variance in performance on all dependent measures, ranging from 17.6% to 36%. Sequencing accounted for substantially more variance across all examined tasks. On the PASAT, CVLT and Tower Test, the variance accounted for by the components of Digit Span appears to be redundant. However, on Trail Making, both Forward and Sequencing accounted for significant amounts of variance that appear to be independent of one another. What specific task requirement(s) of the Trail Making Test versus the other measures analyzed are accounted for by Forward span is not clear. But this suggests that the individual components of the Digit Span test may measure different things across different tasks.

As the presentation of anxiety may differ between younger and older adults, it is important to select measures that accurately capture anxiety symptoms for the intended population. The 21-item Beck Anxiety Inventory (BAI) is widely used; however, its high reliance on somatic symptoms may result in artificial inflation of anxiety ratings among older adults, particularly those with medical conditions. The 30-item Geriatric Anxiety Scale (GAS) was specifically developed for older adults and has shown strong psychometric properties in community-dwelling and long-term care samples. The reliability and validity of the GAS in a memory clinic setting is unknown. The present study aimed to compare the psychometric properties of the GAS and the BAI in a memory disorder clinic sample.

Participants included 35 older adults (age=73.3±5.0 years; edu=15.3±2.8 years; 42% female; 89% non-Hispanic white) referred for a neuropsychological evaluation in a memory disorders clinic. In addition to the GAS and BAI, the Geriatric Depression Scale (GDS) and Montreal Cognitive Assessment (MoCA) were included. Cutoffs for clinically significant anxiety were based on published data for each measure. A dichotomous anxiety rating (yes/no) was created to examine inter-measure agreement; minimal anxiety was classified as “no” and mild, moderate and severe anxiety were classified as “yes.” Internal scale reliability was examined using Cronbach’s alpha. Convergent and discriminant validity were examined using Spearman rank correlation coefficients. Frequency distributions determined the proportion of yes/no anxiety ratings, and a McNemar test compared the proportion of anxiety classifications between the two measures.

Both measures had excellent internal consistency (BAI: a=.88; GAS: a=.94). The BAI and GAS were highly correlated with each other (r=.79, p<.001) and positively correlated with a depression measure (BAI-GDS: r=.51, p=.002; GAS-GDS: r=.53, p=.001). Discriminant validity was supported by lower correlations between the anxiety measures and cognition (BAI-MoCA: r=.38, p=.061; GAS-MoCA: r=.34, p=.098). The BAI classified 14 participants as having anxiety (40%) and 21 participants as not having anxiety (60%), whereas the GAS classified 21 participants as having anxiety (60%) and 14 participants as not having anxiety (40%). The proportion of anxiety classifications were significantly different between the two measures (p =.016). For 28 participants (80%), there was agreement between the anxiety ratings. Seven participants (20%) were classified as having anxiety by the GAS, but not by the BAI; GAS items related to worry about being judged or embarrassed may contribute to discrepancies, as they were frequently endorsed by these participants and are unique to the GAS.

Results support that both anxiety measures have adequate psychometric properties in a clinical sample of older adult patients with memory concerns. It was expected that the BAI would result in higher classification of anxiety due to reliance on somatic symptoms; however, the GAS rated more participants as having anxiety. The GAS may be more sensitive to detecting anxiety in our sample, but formal anxiety diagnoses were not available in the current dataset. Future research should examine the diagnostic accuracy of the GAS in this population. Overall, preliminary results support consideration of the GAS in memory disorder evaluations.

Previous research has found that measures of premorbid intellectual functioning may be predictive of performance on memory tasks among older adults (Duff, 2010). Intellectual functioning itself is correlated with education. The purpose of this study was to investigate the incremental validity of a measure of premorbid intellectual functioning over education levels to predict performance on the Virtual Environment Grocery Store (VEGS), which involves a simulated shopping experience assessing learning, memory, and executive functioning.

Older adults (N = 118, 60.2% female, age 60-90, M = 73.51, SD = 7.46) completed the Wechsler Test of Adult Reading and the VEGS.

WTAR and education level explained 9.4% of the variance in VEGS long delay free recall, F = 5.97, p = 0.003). WTAR was a significant predictor (ß = 0.25, p = 0.006), while level of education was not.

These results suggest that crystalized intelligence may benefit recall on a virtual reality shopping task.