A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: standardization and initial construct validation

Abstract

There is increased need for efficient computerized methods to collect reliable data on a range of cognitive domains that can be linked to specific brain systems. Such need arises in functional neuroimaging studies, where individual differences in cognitive performance are variables of interest or serve as confounds. In genetic studies of complex behavior, which require particularly large samples, such trait measures can serve as endophenotypes. Traditional neuropsychological tests, based on clinical pathological correlations, are protracted, require extensive training in administration and scoring, and leave lengthy paper trails (double-entry for analysis). We present a computerized battery that takes an average of 1h and provides measures of accuracy and speed on 9 neurocognitive domains. They are cognitive neuroscience-based in that they have been linked experimentally to specific brain systems with functional neuroimaging studies. We describe the process of translating tasks used in functional neuroimaging to tests for assessing individual differences. Data are presented on each test with samples ranging from 139 (81 female) to 536 (311 female) of carefully screened healthy individuals ranging in age from 18 to 84. Item consistency was established with acceptable to high Cronbach alpha coefficients. Inter-item correlations were moderate to high within domain and low to nil across domains, indicating construct validity. Initial criterion validity was demonstrated by sensitivity to sex differences and the effects of age, education and parental education. These results encourage the use of this battery in studies needing an efficient assessment of major neurocognitive domains such as multi-site genetic studies and clinical trials.

Figure 1

A flow diagram for adapting cognitive neuroscience tasks as psychometric tests in neuroimaging and clinical studies.

Figure 2

Examples of test stimuli and procedures.

Figure 3

Mean (±SEM) of men (blue bars) and women (red bars) on the tests included in the battery. Domain names as in Table 1. Note that no accuracy measures are available for the sensorimotor test because no errors were made and for the motor speed test because no errors were possible. * p < .01; ** p < .001, red stars indicate women had better performance while blue stars indicate better performance in men. Note that accuracy and speed provide complementary information, and while performance is overall similar for men and women, some well known sex differences are replicated (better performance for women in memory and affect recognition and for men in spatial and motor domains), and some novel ones revealed (better performance for men on CPT and working memory).

Figure 4

Correlations of age with accuracy (black bars) and response time (RT; gray bars) indices of performance on the tests. Error bars indicate 95% confidence intervals based on 1000 bootstraps. As seen, the effects of age are stronger for speed than for accuracy, and more pronounced for abstraction/flexibility and episodic memory than for attention and working memory.

Figure 5

Correlations of education (left panel) and parental education (right panel) with accuracy (black bars) and response time (RT; gray bars) indices of performance on the tests. Error bars were indicating 95% confidence intervals based on 1000 bootstraps. As seen, both education and parental education are associated with better performance, and the effects are stronger for accuracy than for speed. It also appears that the correlations are higher for parental education than for education, affecting both accuracy and speed.