Feedback Representation and Prediction Strategies

Abstract

The influence of feedback representation on prediction is examined in a single cue probability learning paradigm. Two types of feedback representation are examined: deviation representation, in which the feedback is the magnitude, or even just the sign, of the prediction error, and standard representation, in which the feedback is the outcome itself. It is found that when the predictor is represented visually (rather than numerically), and when the outcome scale is unknown, deviation representation results in higher prediction extremity than standard representation. In addition, deviation representation results in higher prediction consistency than standard representation. These findings are explained as resulting from more reliance on the representativeness heuristic in the deviation representation conditions.