Are you sure? Modelling Local Confidence of a Driver
When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a (local) confidence judgment. Confidence judgments have among other things an effect on justifications of future decisions and behaviour. A better understanding of the metacognitive processes responsible for these confidence judgements could improve behaviour models. However, to date there is little to no applied research done into confidence in dynamic environments as for example driving. Confidence judgments are mostly studied in a fundamental manner, focusing on confidence in simplistic perceptual or preferential tasks. At the same time, cognitive models of decision making of drivers have not accounted for confidence judgments yet. In this study, we made a first attempt of connecting these two fields of research by investigating the confidence of human drivers in left-turn gap acceptance decisions in a driver simulator experiment (N=17). The study aimed to, firstly, investigate if confidence can be properly measured in a dynamic task. Secondly, it sought to establish the relationship between confidence and the characteristics of a traffic situation, in this study constituting the gap size described by the time to arrival of and distance to oncoming traffic. Thirdly, we aimed to model the dynamics of the underlying cognitive process using the evidence accumulation approach. We found that self-reported confidence judgements displayed a similar pattern as expected based on the earlier fundamental studies into confidence. Specifically, confidence increased with the gap size when participants decided to accept the gap, and decreased with gap size when the gap was rejected. Moreover, we found that confidence judgments can be captured through an extended dynamic drift diffusion decision model. In our model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the dynamic perceptual information perceived by the decision-maker. The model assumes that confidence ratings are based on the state of the accumulator after post-decision evidence accumulation. Overall, the study confirms that principles known from the fundamental research into confidence also hold for dynamic applied tasks.