How does a dyad combine information from different members in order to arrive at a consensus judgement? One suggestion is that groups combine information in a Bayes optimal fashion: the group calculates a weighted average of individuals' estimates, with the weightings being proportional to the quality of the information each individual possesses. Alternatively, the dyad may seek to identify which member's estimate is the best, and return that as a joint judgement. These models were tested by asking members of a dyad to make private estimates of a continuous quantity (the direction of movement of a coherent motion stimulus), and to then make a joint judgement. Joint judgements were more accurate than individual judgements, but were only partly based on optimal integration. Rather, the joint judgements were often in the neighbourhood of one of the individual judgements, or an uninformed average of the two judgements. Regression analyses suggest that dyads sampled from the alternative responses according to their initial disagreement, and their relative accuracy, on a trial-by-trial basis. Rather than learning about each others' ability, dyad members appear to rely on communication of estimated precision when forming judgements, and often resolve discrepancies by taking the best guess.
Delay of the reinforcing opportunity to speak in reply under invariable initial disagreement