People are generally poor reporters of time spent using digital technology. Advancing smartphone features, such as the iOS Screen Time application, allow researchers to obtain more objective measurements of digital technology use. Truth and Bias models were used to test how self-reported social networking site use aligns with device-reported use as recorded by the iOS Screen Time app (N=1585). This study explored use across four major platforms (Facebook, Instagram, Twitter, Snapchat) and examined how individual differences moderate biases in reports. Participants overestimated their use for all platforms at comparable levels. Moderation by individual differences was not consistent. These findings add to the growing call from researchers to utilize assessments other than self-reports in measuring digital technology use.
ABSTRACT
The increasing adoption of brain imaging methods has greatly augmented our understanding of the neural underpinnings of communication processes. Enabled by recent advancements in mathematics and computational infrastructure, researchers have begun to move beyond traditional univariate analytic techniques in favor of methods that consider the brain in terms of evolving networks of interactions between brain regions. This network neuroscience approach is a potential boon to communication and media psychology research but also requires a careful look at the complications inherent in adopting a novel (and complex) methodological tool. In this manuscript, we provide an overview of network neuroscience in view of the needs of communication neuroscientists, discussing considerations that must be taken into account when constructing networks from neuroimaging data and conducting statistical tests on these networks. Throughout the manuscript, we highlight research domains in which network neuroscience is likely to be particularly useful for increasing theoretical clarity in communication and media psychology research.
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