Network Structure Explains the Impact of Attitudes on Voting Decisions

Attitudes can have a profound impact on socially relevant behaviours, such as voting. However, this effect is not uniform across situations or individuals, and it is at present difficult to predict whether attitudes will predict behaviour in any given circumstance. Using a network model, we demonstrate that (a) more strongly connected attitude networks have a stronger impact on behaviour, and (b) within any given attitude network, the most central attitude elements have the strongest impact. We test these hypotheses using data on voting and attitudes toward presidential candidates in the US presidential elections from 1980 to 2012. These analyses confirm that the predictive value of attitude networks depends almost entirely on their level of connectivity, with more central attitude elements having stronger impact. The impact of attitudes on voting behaviour can thus be reliably determined before elections take place by using network analyses.Comment: Final version published in Scientific Report

Cognitive-Affective Inconsistency and Ambivalence: Impact on the Overall Attitude–Behavior Relationship

This research explored whether overall attitude is a stronger predictor of behavior when underlying cognitive-affective inconsistency or ambivalence is low versus high. Across three prospective studies in different behaviors and populations (Study 1: eating a low-fat diet, N = 136 adults, eating five fruit and vegetables per day, N = 135 adults; Study 2: smoking initiation, N = 4,933 adolescents; and Study 3: physical activity, N = 909 adults) we tested cognitive-affective inconsistency and ambivalence individually and simultaneously as moderators of the overall attitude–behavior relationship. Across studies, more similar effects were observed for inconsistency compared with ambivalence (in both individual and simultaneous analyses). Meta-analysis across studies supported this conclusion with both cognitive-affective inconsistency and ambivalence being significant moderators when considered on their own, but only inconsistency being significant when tested simultaneously. The reported studies highlight the importance of cognitive-affective inconsistency as a determinant of the strength of overall attitude

Preconference "Estimating and interpreting psychological networks"

Here you find files related to the preconference at the FGSP 2019

Networks of Beliefs: An Integrative Theory of Individual- and Social-Level Belief Dynamics

We present a theory of belief dynamics that explains the interplay between internal beliefs in people’s minds and beliefs of others in their external social environments. The Networks of Belief theory goes beyond existing theories of belief dynamics in three ways. First, it provides an explicit connection between belief networks in individual minds and belief dynamics on social networks. The connection, absent from most previous theories, is established through people’s social beliefs, or perceived beliefs of others. Second, the theory recognizes that the correspondence between social beliefs and others’ actual beliefs can be imperfect, because social beliefs are affected by personal beliefs as well as by the actual beliefs of others. Past theories of belief dynamics on social networks do not distinguish between perceived and actual beliefs of others. Third, the theory explains diverse belief dynamics phenomena parsimoniously through the differences in attention and the resulting felt dissonances in personal, social, and external parts of belief networks. We implement our theoretical assumptions in a computational model within a statistical physics framework and derive model predictions. We find support for our theoretical assumptions and model predictions in two large survey studies (N1=973, N2=669). We then derive insights about diverse phenomena related to belief dynamics, including group consensus and polarization, group radicalization, minority influence, and different empirically observed belief distributions. We discuss how the theory goes beyond different existing models of belief dynamics and outline promising directions for future research