Functional neuroimaging has been used to study a wide array of psychological traits, including aspects of personality and intelligence. Progress in identifying the neural correlates of individual differences in such traits, for the sake of basic science, has moved us closer to the applied science goal of measuring them and thereby raised ethical concerns about privacy. How realistic are such concerns given the current state of the art? In this article, we describe the statistical basis of the measurement of psychological traits using functional neuroimaging and examine the degree to which current functional neuroimaging protocols could be used for this purpose. By analyzing the published data from 16 studies, we demonstrate that the use of imaging to gather information about an individual's psychological traits is already possible, but to an extremely limited extent. Abstract & Functional neuroimaging has been used to study a wide array of psychological traits, including aspects of personality and intelligence. Progress in identifying the neural correlates of individual differences in such traits, for the sake of basic science, has moved us closer to the applied science goal of measuring them and thereby raised ethical concerns about privacy. How realistic are such concerns given the current state of the art? In this article, we describe the statistical basis of the measurement of psychological traits using functional neuroimaging and examine the degree to which current functional neuroimaging protocols could be used for this purpose. By analyzing the published data from 16 studies, we demonstrate that the use of imaging to gather information about an individual's psychological traits is already possible, but to an extremely limited extent. &
Disciplines
Medicine and Health Sciences
Previous studies have combined random-site hierarchical sampling designs with analysis of variance techniques, and grid sampling with spatial autocorrelation analysis. We illustrate that analysis techniques and sampling designs are interchangeable using densities of an infaunal bivalve from a study in Poverty Bay, New Zealand. Hierarchical designs allow the estimation of variances associated with each level, but high-level factors are imprecisely estimated, and they are inefficient for describing spatial pattern. Grid designs are efficient for describing spatial pattern, and are amenable to conventional analysis. Our example deals with a continuous spatial habitat, but our conclusions also apply in disjunct or patchy habitats. The influence of errors in positioning is also assessed. The advantages of systematic sampling are reviewed, and more efficient hierarchical approaches are identified. The distinction between biological and statistical significance in all analyses is emphasised.
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