Applying valence framing to enhance the effect of information on transport-related carbon dioxide emissions

The provision of information about transport-related carbon dioxide (CO2) emissions to the traveler can be seen as an instrument to increase the likelihood of more sustainable choices being made by individuals. However, as transport-related CO2 emissions are largely seen as a 'social' cost rather than a 'private' cost to the individual, the behavioral engagement with and response to information on environmental effects of travel choices may be limited. It is argued that framing, studied in a range of contexts, can be used to enhance the evaluation of choice attributes and promote more sustainable choices. An experiment is reported that examines the effect of valence framing of amounts of CO2 emissions on the perceived differences between alternative amounts. Through the use of positive and negative terms, the information is framed to focus attention either on the potential of a travel mode to provide environmental benefit (positive frame) or on its potential to reduce environmental loss (negative frame). Survey participants' estimates of CO2 amounts were compared for positive and negative framing of the same information using an ordered logit model. The findings imply that negative framing is more effective than positive framing in highlighting differences between CO2 amounts of alternative travel modes and therefore is likely to influence travel-related choices. © 2012 Elsevier Ltd

The interaction of gambling outcome and gambling harm-minimisation strategies for electronic gambling: the efficacy of computer generated self-appraisal messaging

It has been argued that generating pop-up messages during electronic gambling sessions, which cause a player to engage in self-appraisal of their gambling behaviour, instil greater control and awareness of behaviour (Monaghan, Computers in Human Behaviour, 25, 202–207, 2009). Consideration for the potential interaction between the messaging’s efficacy and gambling outcome (winning or losing) is lacking however. Thirty participants took part in a repeated-measures experiment where they gambled on the outcome of a computer-simulated gambling task. Outcome was manipulated by the experimenter to induce winning and losing streaks. Participants gambled at a significantly faster speed and a higher average stake size, which resulted in a greater betting intensity in the Loss condition compared to the Win condition. Computer generated self-appraisal messaging was then applied during the gambling session, which was able to significantly reduce the average speed of betting in the Loss condition only, demonstrating an interaction effect between computer generated messaging and gambling outcome

Investigating cooperation with robotic peers

We explored how people establish cooperation with robotic peers, by giving participants the chance to choose whether to cooperate or not with a more/less selfish robot, as well as a more or less interactive, in a more or less critical environment. We measured the participants' tendency to cooperate with the robot as well as their perception of anthropomorphism, trust and credibility through questionnaires. We found that cooperation in Human-Robot Interaction (HRI) follows the same rule of Human-Human Interaction (HHI), participants rewarded cooperation with cooperation, and punished selfishness with selfishness. We also discovered two specific robotic profiles capable of increasing cooperation, related to the payoff. A mute and non-interactive robot is preferred with a high payoff, while participants preferred a more human-behaving robot in conditions of low payoff. Taken together, these results suggest that proper cooperation in HRI is possible but is related to the complexity of the task

Can Plan Recommendations Improve the Coverage Decisions of Vulnerable Populations in Health Insurance Marketplaces?

OBJECTIVE: The Affordable Care Act's marketplaces present an important opportunity for expanding coverage but consumers face enormous challenges in navigating through enrollment and re-enrollment. We tested the effectiveness of a behaviorally informed policy tool--plan recommendations--in improving marketplace decisions. STUDY SETTING: Data were gathered from a community sample of 656 lower-income, minority, rural residents of Virginia. STUDY DESIGN: We conducted an incentive-compatible, computer-based experiment using a hypothetical marketplace like the one consumers face in the federally-facilitated marketplaces, and examined their decision quality. Participants were randomly assigned to a control condition or three types of plan recommendations: social normative, physician, and government. For participants randomized to a plan recommendation condition, the plan that maximized expected earnings, and minimized total expected annual health care costs, was recommended. DATA COLLECTION: Primary data were gathered using an online choice experiment and questionnaire. PRINCIPAL FINDINGS: Plan recommendations resulted in a 21 percentage point increase in the probability of choosing the earnings maximizing plan, after controlling for participant characteristics. Two conditions, government or providers recommending the lowest cost plan, resulted in plan choices that lowered annual costs compared to marketplaces where no recommendations were made. CONCLUSIONS: As millions of adults grapple with choosing plans in marketplaces and whether to switch plans during open enrollment, it is time to consider marketplace redesigns and leverage insights from the behavioral sciences to facilitate consumers' decisions

Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value

BACKGROUND: There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. OBJECTIVE: We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. DESIGN/METHODS: Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. RESULTS: Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a 'risk premium' of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. CONCLUSIONS: This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia

Altered Risk-Based Decision Making following Adolescent Alcohol Use Results from an Imbalance in Reinforcement Learning in Rats

Alcohol use during adolescence has profound and enduring consequences on decision-making under risk. However, the fundamental psychological processes underlying these changes are unknown. Here, we show that alcohol use produces over-fast learning for better-than-expected, but not worse-than-expected, outcomes without altering subjective reward valuation. We constructed a simple reinforcement learning model to simulate altered decision making using behavioral parameters extracted from rats with a history of adolescent alcohol use. Remarkably, the learning imbalance alone was sufficient to simulate the divergence in choice behavior observed between these groups of animals. These findings identify a selective alteration in reinforcement learning following adolescent alcohol use that can account for a robust change in risk-based decision making persisting into later life

Brain Imaging Studies in Pathological Gambling

This article reviews the neuroimaging research on pathological gambling (PG). Because of the similarities between substance dependence and PG, PG research has used paradigms similar to those used in substance use disorder research, focusing on reward and punishment sensitivity, cue reactivity, impulsivity, and decision making. This review shows that PG is consistently associated with blunted mesolimbic-prefrontal cortex activation to nonspecific rewards, whereas these areas show increased activation when exposed to gambling-related stimuli in cue exposure paradigms. Very little is known, and hence more research is needed regarding the neural underpinnings of impulsivity and decision making in PG. This review concludes with a discussion regarding the challenges and new developments in the field of neurobiological gambling research and comments on their implications for the treatment of PG

The Neural Basis of Following Advice

Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, computational modeling of behavioral data to describe cognitive mechanisms involved in social learning, and model-based functional magnetic resonance imaging (fMRI) to identify the neurobiological basis of following advice. One-time advice received before learning had a sustained influence on people's learning processes. This was best explained by social learning mechanisms implementing a more positive evaluation of the outcomes from recommended options. Computer simulations showed that this “outcome-bonus” accumulates more rewards than an alternative mechanism implementing higher initial reward expectation for recommended options. fMRI results revealed a neural outcome-bonus signal in the septal area and the left caudate. This neural signal coded rewards in the absence of advice, and crucially, it signaled greater positive rewards for positive and negative feedback after recommended rather than after non-recommended choices. Hence, our results indicate that following advice is intrinsically rewarding. A positive correlation between the model's outcome-bonus parameter and amygdala activity after positive feedback directly relates the computational model to brain activity. These results advance the understanding of social learning by providing a neurobiological account for adaptive learning from advice