Running head: RULES IN JUDGMENT 1 Testing learning mechanisms of rule-based judgment

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully modeled with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed least mean squares or delta learning rule can perfectly learn to solve linear problems, we found in a first study that it does not adequately describe human learning. To provide a more accurate description of learning processes we amended the delta learning rule with three learning mechanisms —a decay, an attentional learning mechanism, and a capacity limitation —and tested in a further study how well those learning mechanisms can describe and predict learning in linear judgment tasks. In the study, participants first learned to predict a continuous criterion based on four cues. To test the three learning mechanisms rigorously against each other, we changed the importance of the cues after 200 trials so that the mechanisms make different predictions with regard to how fast people adapt to the new environment. On average, judgment accuracy improved from trial 1 to 200, dropped when the task structure changed, but improved again until the end of the task. The capacity-restricted learning model best described and predicted the learning curve of the majority of participants. Taken together, these results suggest that human learning when making inferences is governed by cognitive capacity limitations.