Monitoring a meat-free pledge with smartphones: An experimental study.

Pledges are a popular strategy to encourage meat reduction, though experimental studies of their efficacy are lacking. Three-hundred and twenty-five participants from three different countries (UK, Germany, Australia) were randomly assigned to pledge 28 days meat-free or not, and their behavior was tracked via smartphones. Participants answered daily surveys regarding their eating behavior, meat cravings, and shared photos of their meals. Baseline data was collected prior to the pledge, after the 28 days, and one-month post-intervention. Participants assigned to the pledge condition ate less meat across the 28 days, compared to control participants. Meat reductions, observed at outtake, did not endure one-month post-intervention. Overall, German participants ate the least amount of meat, and showed the sharpest decrease in consumption when pledging. Meat cravings tended to increase among pledgers, relative to control participants. Pledgers who reported high starting intentions and conflict about meat tended to eat less meat and reported fewer cravings. All participants reported reduced meat-eating justifications one-month post-intervention. These findings provide experimental evidence that pledges can encourage meat consumers to reduce their intake, though additional mechanisms are needed to sustain commitments.

Visual digital data, ethical challenges, and psychological science.

Digital visual data afford psychologists with exciting research possibilities. It becomes possible to see real-life interactions in real time and to be able to analyze this behavior in a fine-grained and systematic manner. However, the fact that faces (and other personally identifying physical characteristics) are captured as part of these data sets means that this kind of data is at the highest level of sensitivity by default. When this is combined with the possibility of automatic collection and processing, then the sensitivity risks are compounded. Here we explore the ethical challenges that face psychologists wishing to take advantage of digital visual data. Specifically, we discuss ethical considerations around data acquisition, data analysis, data storage, and data sharing. We begin by considering the challenges of securing visual data from both public space security systems and social media sources. We then explore the dangers of bias and discrimination in automatic data processing, as well as the dangers to human analysts. We set out the ethical requirements for secure data storage, the dangers of "function creep," and the challenges of the right of the individual to withdraw from databases. Finally, we consider the tensions that exist between sensitive visual data that require extra protections and the recent open science movement, which advocates data transparency and sharing. We conclude by offering a practical route map for tackling these complex ethical issues in the form of a Privacy and Data Protection Impact Assessment template for researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

"Are we in this together?": embedding social identity detection in drones improves emergency coordination.

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders' decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors' talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system.