Excited and aroused: The predictive importance of simple choice process metrics.

We conduct a lottery experiment to assess the predictive importance of simple choice process metrics (SCPMs) in forecasting risky 50/50 gambling decisions using different types machine learning algorithms as well traditional modeling approaches. The SCPMs are recorded during fixed pre-decision phase and derived from tracking subjects’ eye movements, pupil sizes, skin conductance, cardiovascular respiratory signals. Our study demonstrates that provide relevant information for predicting decisions, but we do not find accuracy be substantially affected by adding standard data. Instead, our results show highly depends on differences subject-specific risk preferences is largely driven including design variables. As key result, evidence dynamic changes psychophysiological responses appear linked habituation resource-depletion effects. Subjects’ willingness gamble choice-revealing arousal signals both decrease progresses. Moreover, findings highlight accounting previous payoff characteristics when investigating role emotions cognitive bias repeated decision-making scenarios.