Driven to distraction II: Mining existing data on relations between inhibitory abilities and academic performance

Project Number
RS 4/16 KL

Project Duration
May 2016 - November 2017


Many of us would have the experience of trying to read an academic journal, only to find ourselves reading and re-reading the same paragraphs due to an inability to remain focused. Sources of distraction could come from the external environment (e.g., constant announcements of new incoming email) or internal thoughts (e.g., worries about an upcoming meeting). It is thus surprising that in a recent review (Bull & Lee, 2014), we found inhibitory abilities did not uniquely explain performance in mathematics tasks. We suspect that the null findings are likely due to previous measures of inhibitory abilities not imposing sufficient inhibitory demands on participants. Not being forced into contexts in which success depends critically on ability to resist interference, these tasks fail to establish the boundary conditions for participantsÂ’ inhibitory abilities. It is also possible that different types of mathematical tasks impose different inhibitory demands. Standardised mathematics achievement, commonly used in previous studies, may not have the task characteristics or impose the kind of time pressure needed to impose a heavy load on inhibitory abilities. To investigate the relations between inhibitory abilities and academic performance, I am embarking on a new programme of research. An important part of this endeavor is to develop new measures of inhibitory abilities. Seeding funds for this part of the endeavor is supported by a small grant from the National Research Foundation (NRF 2015_SOL001_007: Driven to Distraction). The current application seeks to obtain additional funding to mine data from my previous studies that bear on the relation of interest. There are two studies in which various measures of inhibitory and mathematics abilities were used. Although some of these studies had been reported, findings from the inhibitory measures had not been reported in depth. Furthermore, there are recent advances in analytical techniques that may reveal hidden patterns of relations in the data.

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