Measuring processing biases in balanced argument experiments

How do people revise their opinions when exposed to a balanced and diverse information diet? By combining a balanced-argument experiment with a computational theory of argument communication we shed new light on this question. Empirical studies repeatedly examined whether or not biased processing of balanced arguments may lead to more extreme attitudes and contribute to polarization tendencies, but empirical evidence remains mixed. Two forces counteract one another in such a balanced-argument setting: first, there is a moderating effect of being exposed to arguments from both sides. Second, there is a polarizing effect of filtering the information mix in favor of existing beliefs (biased processing). Our theoretical model takes into account that biased processing may come in degrees. Drawing on the theory we develop an artificial experiment — a computational miniature of the real one — and analytically derive a response function for the expected attitude changes. This function contains the strength of biased processing (β) as a free parameter. Theoretical analysis reveals a sharp transition from attitude moderation to polarization indicating that small, domain-specific variations in the strength of biased processing may result in qualitatively different patterns of attitude change, both consistent with our theory. In the empirical experiment (N = 1078) individuals are exposed to an equal share of 7 pro and 7 counter arguments regarding 6 different technologies for energy production (for each N > 170). Attitudes are measured before and after exposure. Using this data we estimate the strength of biased processing for the six empirical topics. While the processing bias is in the regime of attitude moderation for gas and biomass, it is significantly higher and in the regime of polarization for coal, wind (onshore and offshore) as well as solar power. If time permits, we will discuss the implications of these results for group deliberation processes.