Humans can choose between fundamentally different options, such as watching a movie or going out for dinner. According to the utility concept, put forward by utilitarian philosophers and widely used in economics, this may be accomplished by mapping the value of different options onto a common scale, independent of specific option characteristics (Fehr and Rangel, 2011; Levy and Glimcher, 2012). If this is the case, value-related activity patterns in the brain should allow predictions of individual preferences across fundamentally different reward categories. We analyze fMRI data of the prefrontal cortex while subjects imagine the pleasure they would derive from items belonging to two distinct reward categories: engaging activities (like going out for drinks, daydreaming, or doing sports) and snack foods. Support vector machines trained on brain patterns related to one category reliably predict individual preferences of the other category and vice versa. Further, we predict preferences across participants. These findings demonstrate that prefrontal cortex value signals follow a common scale representation of value that is even comparable across individuals and could, in principle, be used to predict choice.
Purpose Life cycle thinking and assessment require a holistic approach to the evaluation of product supply chains. An assessment from raw material extraction to end of life of any products is based on modeling a wide number of aspects and options, e.g., at technological, geographical, and temporal levels. Since the use phase is one of the most contributing life cycle stages for some products (e.g., appliance, housing, cars), a robust modeling of this stage is fundamental. Several attempts to better modeling use-phase have been performed; however, so far no systematic study is available on how to integrate behavioral science (BS) insights into LCA. This is even more important when the impact of the product under consideration is strongly determined by the use phase relatively to other life cycle stages. The aim of this paper is to explore how behavioral science has been used to date and how BS can contribute towards more robust modeling of use phase in LCA and as basis for a behavior-driven ecodesign. Methods We identified the key areas in which LCA and ecodesign may benefit from integrating insights from behavioral science and developing a conceptual model. Both robust modeling and the design of behavior change interventions rest on a sound understanding of behavior in the specific context of interest though empirical investigation. Hence, we reviewed literature on behavioral science and introduce key drivers of human behavior that are relevant in the context of use phase modeling and ecodesign. We provide examples where these were applied to facilitate the integration of BS elements by practitioners. Results and discussion Consumer's behavior is increasingly recognized as one of the drivers of overall environmental impact of a product, and some examples of use of BS for LCA are available in literature. We suggest that behavioral science can be useful in the context of life cycle assessment in two ways: measuring behavior and assessing potential and means for changing behavior. Specifically, insights and methods from behavioral sciences could be applied for assessing variability of consumer behavior, understanding leverages for behavioral changes, and possible rebound effects. Conclusions This insight may help to model the use phase more accurately, to identify realistic scenarios, and to support behavior-driven eco-innovation.
There is a need for research that provides an evidence base for the pharmacotherapy of people with mental disorders. The abundance of digital data in recent years has facilitated pharmacoepidemiology in the form of observational comparative effectiveness studies at the population level. Advantages are large patient samples, coverage of under-researched sub-populations and naturalistic conditions. Pharmacoepidemiology is also cheaper and quicker to carry out than RCTs, meaning that issues regarding generic medication, stopping medication (deprescribing) and long-term outcomes are more likely to be addressed. Methods can also be extended to pharmacovigilance and drug repurposing. Drawbacks of observational studies come from the non-randomised nature of treatment selection, and the inherent risk of confounding by indication. Potential methods for managing this may include active comparison groups, inter-individual designs, propensity scoring and instrumental variables. Many of the more rigorous pharmacoepidemiology studies have been strengthened through multiple triangulated analytic approaches to improve confidence in inferred causal relationships. With these developments in data resources and analytic techniques, it is encouraging that guidelines are beginning to include evidence from robust pharmacoepidemiogical studies alongside RCTs. Collaboration between guideline-writers and researchers involved in pharmacoepidemiology may help researchers ask the questions that are important to policy-makers and ensure that results get integrated into the evidence-base. Further development of statistical and data science techniques, alongside capacity building in terms of data resources, a wider researcher base and public engagement, will be necessary to take full advantage of future opportunities.
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Accessibility of powerful computers and availability of so-called "big data" from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this article we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.
ObjectiveThis paper examines whether there are possible wear-out effects associated with repeated exposure to pictorial health warnings on tobacco products. Wear-out effects can be general, that is, people get used to the presence of pictorial warnings in general, or specific to the content of the warnings (ie, the images used). Distinguishing between these two types of wear-out is important for understanding how to maintain the effectiveness of health warnings over time.MethodsThis study used data from two surveys carried out in 10 European countries. Participants (n=12 600) were exposed in a random order to a series of health warnings and assessed the salience of the warnings as well as their effects on smoking intentions. Using these data and country variations in health warning legislation, we tested whether warning pictures are subject to general and/or specific wear-out effects.ResultsResponses were stronger to combined text+picture warnings than to text-only warnings. This effect was lower for smokers living in countries where combined warnings were already in place at the time of the data collection, compared with smokers residing in countries where text-only warnings were in use. This result, observed for combined warnings with new pictures, is in line with the presence of general wear-out effects. Combined warnings with an unknown pictorial content were more effective than those including pictorial warnings already in use, suggesting that specific wear-out effects are also at play.ConclusionsThese findings strengthen the evidence that pictorial health warnings are an effective tool for tobacco control policies and suggest that, even in the presence of a general wear-out effect among smokers, periodically introducing new pictures helps to maintain warning effectiveness over time.
One fundamental question in decision making research is how humans compute the values that guide their decisions. Recent studies showed that people assign higher value to goods that are closer to them, even when physical proximity should be irrelevant for the decision from a normative perspective. This phenomenon, however, seems reasonable from an evolutionary perspective. Most foraging decisions of animals involve the trade-off between the value that can be obtained and the associated effort of obtaining. Anticipated effort for physically obtaining a good could therefore affect the subjective value of this good. In this experiment, we test this hypothesis by letting participants state their subjective value for snack food while the effort that would be incurred when reaching for it was manipulated. Even though reaching was not required in the experiment, we find that willingness to pay was significantly lower when subjects wore heavy wristbands on their arms. Thus, when reaching was more difficult, items were perceived as less valuable. Importantly, this was only the case when items were physically in front of the participants but not when items were presented as text on a computer screen. Our results suggest automatic interactions of motor and valuation processes which are unexplored to this date and may account for irrational decisions that occur when reward is particularly easy to reach.
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