The personality attributes and leisure activities of the internet users: a Taiwanese case study

This study aims to examine the relationship between the personality attributes of Internet users and their leisure activities. A questionnaire survey was undertaken which revealed that most Internet users are single males aged between 21-30 belonging to the lower income groups, employed in information technology or related fields. The personality attributes of the sample showed a tendency towards a mixed locus control category. The survey indicated that the preferred leisure activities of this population group are reading, collecting and computer-based activities. However, ‘movement’ and collecting were the only leisure activities to show a significant correlation with the users’ personality attributes.

Facilitating student engagement and collaboration in a large postgraduate course using wiki-based activities

This paper investigates the impact of wiki-based activities on student participation and collaborative learning in a large postgraduate international management course. The wiki was used in this study as a facilitator for engagement and collaboration rather than a means of online discussions. Based on both qualitative and quantitative data, we find strong evidence that the use of the wiki facilitated student engagement and collaboration, both inside and outside the classroom. Moreover, student learning had significantly improved as a result of the enhanced learning environment.

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.