The heterogeneous processes of cheating: Attention evidence from two eye tracking experiments

Cognitive and affective processes of prosociality

One piece of the puzzle to prosocial behavior is understanding its underlying cognitive and affective processes. We discuss how modeling behavior in social dilemmas can be expanded by integrating cognitive theories and attention-based models of decision processes, and models of affective influences on prosocial decision-making. We review theories speaking to the interconnections of cognition and affect, identifying the need for further theory development regarding modeling moment-by-moment decision-making processes. We discuss how these theoretical perspectives are mirrored in empirical evidence, drawn from classical outcome-oriented as well as contemporary process-tracing research. Finally, we develop perspectives for future research trajectories aiming to further elucidate the processes by which prosocial decisions are formed, by linking process measures to usually unobservable cognitive and affective reactions.

Psychological science for a responsible sharing economy

The sharing economy is fueled by trust, which allows strangers to cooperate. To share responsibly, one needs to be aware of the various consequences sharing has on interacting and third parties. When transparency about such consequences is lacking, mutual trust among interacting parties may encourage people to cooperate and share, in turn, creating unintended negative impacts. Psychologists have long studied trust and cooperation, yet few insights from psychological science have been used to understand the sharing economy. Here, we propose that evoking trust may paradoxically increase motivated information processing leading people to share irresponsibly by ignoring the negative consequences sharing has on others. We review three conditions under which evoking trust may lead to irresponsible sharing: ethical blind spots, willful ignorance, and misinformation. We propose that transparent information is key to enable and encourage responsible sharing. More psychological research is needed to better understand how this flourishing, trust-based industry can be shaped to encourage safe, cooperative, and responsible sharing.

Does eye-tracking have an effect on economic behavior?

Eye-tracking is becoming an increasingly popular tool for understanding the underlying behavior driving human decisions. However, an important unanswered methodological question is whether the use of an eye-tracking device itself induces changes in participants’ behavior. We study this question using eight popular games in experimental economics chosen for their varying levels of theorized susceptibility to social desirability bias. We implement a simple between-subject design where participants are randomly assigned to either a control or an eye-tracking treatment. In seven of the eight games, eye-tracking did not produce different outcomes. In the Holt and Laury risk assessment (HL), subjects with multiple calibration attempts demonstrated more risk averse behavior in eye-tracking conditions. However, this effect only appeared during the first five (of ten) rounds. Because calibration difficulty is correlated with eye-tracking data quality, the standard practice of removing participants with low eye-tracking data quality resulted in no difference between the treatment and control groups in HL. Our results suggest that experiments may incorporate eye-tracking equipment without inducing changes in the economic behavior of participants, particularly after observations with low quality eye-tracking data are removed.

Enhancing models of social and strategic decision making with process tracing and neural data

Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the "black box" of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi-faceted decisions that are taken in varying contexts and are based on many different types of information. This article is categorized under: Economics > Interactive Decision-Making Neuroscience > Cognition Psychology > Reasoning and Decision Making.