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1.
Front Hum Neurosci ; 8: 712, 2014.
Article in English | MEDLINE | ID: mdl-25309388

ABSTRACT

Depression is a debilitating condition that adversely affects many aspects of a person's life and general health. Earlier work has supported the idea that there may be a relationship between the use of certain media and depression. In this study, we tested if self-report of depression (SRD), which is not a clinically based diagnosis, was associated with increased internet, television, and social media usage by using data collected in the Media Behavior and Influence Study (MBIS) database (N = 19,776 subjects). We further assessed the relationship of demographic variables to this association. These analyses found that SRD rates were in the range of published rates of clinically diagnosed major depression. It found that those who tended to use more media also tended to be more depressed, and that segmentation of SRD subjects was weighted toward internet and television usage, which was not the case with non-SRD subjects, who were segmented along social media use. This study found that those who have suffered either economic or physical life setbacks are orders of magnitude more likely to be depressed, even without disproportionately high levels of media use. However, among those that have suffered major life setbacks, high media users-particularly television watchers-were even more likely to report experiencing depression, which suggests that these effects were not just due to individuals having more time for media consumption. These findings provide an example of how Big Data can be used for medical and mental health research, helping to elucidate issues not traditionally tested in the fields of psychiatry or experimental psychology.

2.
Front Hum Neurosci ; 8: 1073, 2014.
Article in English | MEDLINE | ID: mdl-25709573

ABSTRACT

Multiple transformative forces target marketing, many of which derive from new technologies that allow us to sample thinking in real time (i.e., brain imaging), or to look at large aggregations of decisions (i.e., big data). There has been an inclination to refer to the intersection of these technologies with the general topic of marketing as "neuromarketing". There has not been a serious effort to frame neuromarketing, which is the goal of this paper. Neuromarketing can be compared to neuroeconomics, wherein neuroeconomics is generally focused on how individuals make "choices", and represent distributions of choices. Neuromarketing, in contrast, focuses on how a distribution of choices can be shifted or "influenced", which can occur at multiple "scales" of behavior (e.g., individual, group, or market/society). Given influence can affect choice through many cognitive modalities, and not just that of valuation of choice options, a science of influence also implies a need to develop a model of cognitive function integrating attention, memory, and reward/aversion function. The paper concludes with a brief description of three domains of neuromarketing application for studying influence, and their caveats.

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