Fewer but poorer: Benevolent partiality in prosocial preferences

A prosocial action typically provides a more sizable benefit when directed at those who have less as opposed to those who have more. However, not all prosocial acts have a direct bearing on socioeconomic disadvantage, nor does disadvantage necessarily imply a greater need for the prosocial outcome. Of interest here, welfare impact may depend on the number of beneficiaries but not on their socioeconomic status. Across four preregistered studies of life-saving decisions, we demonstrate that when allocating resources, many people are benevolently partial. That is, they choose to help the disadvantaged even when this transparently implies sacrificing lives. We suggest that people construct prosocial aid as an opportunity to correct morally aversive inequalities, thusmaking relativelymore disadvantaged recipients amore justifiable target of help. Benevolent partiality is reduced when people reflect beforehand on what aspects they will prioritize in their donation decision

Crowdsourced consumer data: how do we make sure it's good?

Crowdsourcing data through online marketplaces such as Amazon Mechanical Turk poses new challenges about how consumer research should be designed, conducted and analysed. Additionally, it raises questions about the validity of the participants and the information they provide. As protocols for crowdsourcing data are still being worked out, we have developed a few guidelines that will benefit those using such platforms for research purposes

Running experiments on Amazon Mechanical Turk

Although Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool

Running experiments on Amazon Mechanical Turk

Although Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool

Participant Nonnaiveté and the reproducibility of cognitive psychology

Many argue that there is a reproducibility crisis in psychology. We investigated nine well-known effects from the cognitive psychology literature—three each from the domains of perception/action, memory, and language, respectively—and found that they are highly reproducible. Not only can they be reproduced in online environments, but they also can be reproduced with nonnaïve participants with no reduction of effect size. Apparently, some cognitive tasks are so constraining that they encapsulate behavior from external influences, such as testing situation and prior recent experience with the experiment to yield highly robust effects

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.Comment: CHI '2