Personality and performance on digit span tests in online and offline settings.

This paper describes two studies examining links between personality and performance on a cognitive test in online and laboratory settings. Study 1 was completed online. 345 participants passively recruited through a personality assessment website completed a Five Factor Model personality inventory derived from the International Personality Item Pool. They then completed an online text-based digit span test. This required participants to repeat increasingly longer strings of digits, either in the same order (forward) or in the opposite of the presentation order (reverse). Conventional digit span tasks ask participants to respond verbally; in this instance they responded by typing the digits. Agreeableness and Openness to Experience each had small but significant associations with forward and reverse digit span. In a second, laboratory based, study, 103 participants completed paper versions of the IPIP Five Factor inventory, the NEO-FFI, and a battery of cognitive tests including the WAIS 4 digit span test. In this instance, Agreeableness and Openness to Experience were not significantly correlated with digit span measures. Taken together, these studies suggest that personality characteristics may influence performance on an online cognitive test. This effect was not seen in an offline version of the study. The paper will consider potential implications for online testing, for equivalence of online and offline methods, and for links between personality and performance on this cognitive test.

Brief response to Ashton and colleagues regarding Fractionating Human Intelligence

Ashton and colleagues concede in their response (Ashton, Lee, & Visser, in this issue), that neuroimaging methods provide a relatively unambiguous measure of the levels to which cognitive tasks co-recruit dif- ferent functional brain networks (task mixing). It is also evident from their response that they now accept that task mixing differs from the blended models of the classic literature. However, they still have not grasped how the neuroimaging data can help to constrain models of the neural basis of higher order ‘g’. Specifically, they claim that our analyses are invalid as we assume that functional networks have uncorrelated capacities. They use the simple analogy of a set of exercises that recruit multiple muscle groups to varying extents and highlight the fact that individual differences in strength may correlate across muscle groups. Contrary to their claim, we did not assume in the original article (Hampshire, High- field, Parkin, & Owen, 2012) that functional networks had uncorrelated capacities; instead, the analyses were specifically designed to estimate the scale of those correlations, which we referred to as spatially ‘diffuse’ factors

Fractionating Human Intelligence

What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or ‘‘factors’’ reflect the functional organiza- tion of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demon- strate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor ‘‘g’’ is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these com- ponents of intelligence by dissociating them using questionnaire variables. We propose that intelli- gence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.