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.

Transparency and Public Involvement in Animal Research

To be legitimate, research needs to be ethical, methodologically sound, of sufficient value to justify public expenditure and be transparent. Animal research has always been contested on ethical grounds, but there is now mounting evidence of poor scientific method, and growing doubts about its clinical value. So what of transparency? Here we examine the increasing focus on openness within animal research in the UK, analysing recent developments within the Home Office and within the main group representing the interests of the sector, Understanding Animal Research. We argue that, while important steps are being taken toward greater transparency, the legitimacy of animal research continues to be undermined by selective openness. We propose that openness could be increased through public involvement, and that this would bring about much needed improvements in animal research, as it has done in clinical research.