The effects of recursive communication dynamics on belief updating. Issue 1931 (29th July 2020)

• Record Type: Journal Article
• Authors: Pescetelli, Niccolò; Yeung, Nick
• Is Part Of: Proceedings. Volume 287:Issue 1931(2020)
• Publication Date: 2020-07-29
• Subjects: belief change -- escalation -- social interaction -- confidence -- decision-making; Biology -- Periodicals; 570.5
• DOI: 10.1098/rspb.2020.0025
• Languages: English
• ISSNs: 0962-8452

Abstract

Abstract : Many social interactions are characterized by dynamic interplay, such that individuals exert reciprocal influence over each other's behaviours and beliefs. The present study investigated how the dynamics of reciprocal influence affect individual beliefs in a social context, over and above the information communicated in an interaction. To this end, we developed a simple social decision-making paradigm in which two people are asked to make perceptual judgments while receiving information about each other's decisions. In a Static condition, information about the partner only conveyed their initial, independent judgment. However, in a Dynamic condition, each individual saw the evolving belief of their partner as they learnt about and responded to the individual's own judgment. The results indicated that in both conditions, the majority of confidence adjustments were characterized by an abrupt change followed by smaller adjustments around an equilibrium, and that participants' confidence was used to arbitrate conflict (although deviating from Bayesian norm). Crucially, recursive interaction had systematic effects on belief change relative to the static baseline, magnifying confidence change when partners agreed and reducing confidence change when they disagreed. These findings indicate that during dynamic interactions—often a characteristic of real-life and online social contexts—information is collectively transformed rather than acted upon by individuals in isolation. Consequently, the output of social events is not only influenced by what the dyad knows but also by predictable recursive and self-reinforcing dynamics.