Understanding patterns of accumulation: improving forecast-based decisions via nudging

Abstract

In this study we investigated challenges associated with comprehension of graphical patterns of accumulation (Experiment 1) and how to improve accumulation-based reasoning via nudging (Experiment 2). On each trial participants were presented with two separate graphs, each depicting a linear, saturating, or exponential data trajectory. They were then asked to make a binary decision based on their forecasts of how these trends would evolve. Correct responses were associated with a focus on the rate of increase in graphs; incorrect responses were driven by prior knowledge and beliefs regarding the context and/or selective attention towards the early phases of the line trajectories. To encourage participants to think more critically and accurately about the presented data, in Experiment 2, participants completed a nudge phase: they either made a forecast about a near horizon or read particular values on the studied trajectories prior to making their decisions. Forecasting about how the studied trajectories would progress led to improvements in determining expected accumulation growth. Merely reading values on the existing trajectory did not lead to improvements in decision accuracy. We demonstrate that actively asking participants to make specific forecasts prior to making decisions based on the accumulation trajectories improves decision accuracy.