Attitude convergence in small networks
Abstract
People s decisions, preferences, and perceptions are pervasively affected by others around them, a process known as social influence. Influence has typically been studied at one of two levels: (i) individuals conformity to each other, and (ii) alignment in groups of interacting individuals. Little work, however, has probed the intersection of these two levels of influence, by examining how "local" influence between individuals results in group convergence. Under this STIR proposal, my team and I will examine this question by examining attitude convergence in "chains," or small networks of participants, using behavioral and neuroimaging techniques. Participants will rate their attitudes towards a series of paintings, and observer ratings of those paintings made by others, and then make a second, socially-influenced rating. Participants will further be binned into eight chains of six participants each. Four of these chains will be initially exposed-via the first participant in that chain-to an experimentally manipulated profile of initial "group" preferences , and four chains will be exposed to a separate profile. Subsequent participants in each chain will view the rating made by the participant before them, and will not be exposed to our experimentally manipulated ratings. This will allow us to simultaneously examine influence at two levels : (i) the extent to which experimentally manipulated group ratings cause individuals to converge in preference, and (ii) the extent to which these manipulations "cascade" through chains of participants through local interactions. We will examine convergence of attitudes through assessment of both participants self reported preferences, and through examination of correlations in brain activity across participants. This paradigm will produce novel insights about the effects of group norms and dyadic interactions on individuals attitudes, and pave the way for applied efforts to optimize decision-making in small groups.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 04, 2018
- Source ID
- W911NF1610172
Entities
People
- Jamil Zaki
Organizations
- Army Contracting Command
- Stanford University
- United States Army