Back

Consensus Formation History Shapes Collective Updating

Sun, C.; Zhou, R.; Zhang, D.; Wang, J.; Meng, Y.; Tang, R.

2026-09-29 neuroscience
10.64898/2026.09.23.753723 bioRxiv
Show abstract

Consensus is rarely a one-off achievement. In a changing world, groups must repeatedly update judgments they have already agreed upon, making consensus updating a common feature of real-world collective decisions. However, previous research on collective decision-making has focused mainly on how groups reach a single consensus, leaving the updating of established consensus largely unexamined. This raises two unanswered questions: does consensus updating involve biases analogous to sunk cost, and are such biases related to the cost of the previous consensus? Building a Fake Consensus paradigm, we manipulated consensus-formation cost and examined how dyads updated a subjectively established consensus after new information arrived. An independent individual experiment provided information-matched updating baselines, allowing us to determine whether dyads updated less or more than individuals responding to the same evidence. Contrary to sunk-cost predictions, low-cost consensus led dyads to update less than the individual baseline, whereas medium- and high-cost consensus led them to update more. The pathway through perceived consensus sharedness was significant, and the association between sharedness and updating was stronger at higher left temporoparietal junction inter-brain synchrony. These findings indicate that the history of consensus formation shapes subsequent updating by altering the psychological meaning of shared endorsement, while interpersonal alignment determines how this shared experience translates into coordinated behavior. To our knowledge, this is the first experimentally controlled study of consensus updating in teams to isolate formation history from actual disagreement, providing a new paradigm for studying how collective judgments change after agreement.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.