Taking the sweetness out of the 'Share a Coke' marketing campaign: the influence of personalized labelling on elementary school children's bottled drink choices.

BACKGROUND: Drink personalization (featuring names on bottle labels) has been used by soft drink companies to make their drinks attractive to children, potentially increasing consumption. To date, no publically available research has evaluated the influence of personalization on children's drink choices. OBJECTIVES: To determine (i) whether personalizing bottled drinks influences children's drink choices; (ii) whether it is comparably effective in promoting healthy and unhealthy drinks and (iii) whether drink choices are affected by self-esteem, body mass index and parental factors. METHODS: Children aged 8-13 years (N = 404) were randomly assigned to one of three drink labeling conditions: Prime Healthy, Prime Unhealthy and Control. All participants selected one beverage from 12 options, comprising six healthy and unhealthy drinks. RESULTS: Personalizing healthy drinks increased choice of healthy drinks (OR, 2.21; 95% CI, 1.24-4.00), and personalizing unhealthy drinks reduced choice of healthy drinks (OR, 0.35; 95% CI, 0.15-.0.75). Higher self-esteem predicted choosing own-named drinks (OR = 1.08, 95% CI, 1.00-1.18; p = .049). CONCLUSIONS: Children's drink choices are influenced by personalizing drink bottles. Tighter regulation of this marketing strategy for soft drinks may reduce children choice of these drinks. Personalization may also be used to encourage children to choose healthy drinks.

Anatomical integration and rich-club connectivity in euthymic bipolar disorder.

BACKGROUND: Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the 'rich-club', represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses. METHOD: We obtained structural and diffusion MRI scans from 42 euthymic patients with BD type I and 43 age- and gender-matched healthy volunteers. Weighted fractional anisotropy connections mapped between cortical and subcortical structures defined the neuroanatomical networks. Next, we examined between-group differences in features of graph properties and sub-networks. RESULTS: Patients exhibited significantly reduced clustering coefficient and global efficiency, compared with controls globally and regionally in frontal and occipital regions. Additionally, patients displayed weaker sub-network connectivity in distributed regions. Rich-club analysis revealed subtly reduced density in patients, which did not withstand multiple comparison correction. However, hub identification in most participants indicated differentially affected rich-club membership in the BD group, with two hubs absent when compared with controls, namely the superior frontal gyrus and thalamus. CONCLUSIONS: This graph theory analysis presents a thorough investigation of topological features of connectivity in euthymic BD. Abnormalities of global and local measures and network components provide further neuroanatomically specific evidence for distributed dysconnectivity as a trait feature of BD.

Decision landscapes: visualizing mouse-tracking data.

Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g. by tracking mouse cursor trajectories. These trajectories can reveal novel information about ongoing decision processes. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. A decision landscape is an analogue of an energy potential field mathematically derived from the velocity of mouse movement during a decision. Visualized as a three-dimensional surface, it provides a comprehensive overview of decision dynamics. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. This approach not only generates three-dimensional illustration of decision landscapes, but also describes mouse trajectories by a number of interpretable parameters. These parameters characterize dynamics of decisions in more detail compared with conventional measures, and can be compared across experimental conditions, and even across individuals. The decision landscape visualization approach is a novel tool for analysing mouse trajectories during decision execution, which can provide new insights into individual differences in the dynamics of decision making.