Hardt G-I transfer denotes an increase in individual performance due to group interaction, for example, because of acquiring certain skills or knowledge from the other group members. Whereas such G-I transfer has been successfully shown for problem-solving tasks, evidence for G-I transfer on quantitative estimation tasks is scarce. We address this research gap with a focus on how often a group has to interact in order to fully exploit the benefit of this learning effect. Results from two experiments support the idea that a single group interaction is sufficient to induce a stable G-I transfer, which reduces group members' metric error. Smaller metric errors indicate that people improved their representation of the correct upper and lower boundaries, or what range of values is plausible. In contrast to nominal groups, both members of continuously interacting groups and members of groups with only one initial interaction exhibited stable G-I transfer, and the size of this transfer did not significantly differ between the latter two conditions. Furthermore, we found evidence for differential weighting of group members' individual contributions that goes beyond sheer individual capability gains under certain circumstances, namely in tasks with a population bias.

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