Suchergebnisse

We find strong support for an on-line model of the candidate evaluation process that in contrast to memory-based models shows that citizens are responsive to campaign information, adjusting their overall evaluation of the candidates in response to their immediate assessment of campaign messages and events. Over time people forget most of the campaign information they are exposed to but are nonetheless able to later recollect their summary affective evaluation of candidates which they then use to inform their preferences and vote choice. These findings have substantive, methodological, and normative implications for the study of electoral behavior. Substantively, we show how campaign information affects voting behavior. Methodologically, we demonstrate the need to measure directly what campaign information people actually attend to over the course of a campaign and show that after controling for the individual's on-line assessment of campaign messages, National Election Study-type recall measures prove to be spurious as explanatory variables. Finally, we draw normative implications for democratic theory of on-line processing, concluding that citizens appear to be far more responsive to campaign messages than conventional recall models suggest.

We describe and test two process models of candidate evaluation. The memory-based model holds that evaluations are dependent on the mix of pro and con information retrieved from memory. The impression-driven model holds that evaluations are formed and updated "on-line" as information is encountered. The results provide evidence for the existence of stereotyping and projection biases that render the mix of evidence available in memory a nonveridical representation of the information to which subjects were exposed. People do not rely on the specific candidate information available in memory. Rather, consistent with the logic of the impression-driven processing model, an "on-line" judgment formed when the information was encountered best predicts candidate evaluation. The results raise both methodological and substantive challenges to how political scientists measure and model the candidate evaluation process.

In this commentary, we embed the volume's contributions on public beliefs about science in a broader theoretical discussion of motivated political reasoning. The studies presented in the preceding section of the volume consistently find evidence for hyperskepticism toward scientific evidence among ideologues, no matter the domain or context—and this skepticism seems to be stronger among conservatives than liberals. Here, we show that these patterns can be understood as part of a general tendency among individuals to defend their prior attitudes and actively challenge attitudinally incongruent arguments, a tendency that appears to be evident among liberals and conservatives alike. We integrate the empirical results reported in this volume into a broader theoretical discussion of the John Q. Public model of information processing and motivated reasoning, which posits that both affective and cognitive reactions to events are triggered unconsciously. We find that the work in this volume is largely consistent with our theories of affect-driven motivated reasoning and biased attitude formation.

A computational model of political attitudes and beliefs is developed that incorporates contemporary psychological theory with well-documented findings from electoral behavior. We compare this model, John Q. Public (JQP), to a Bayesian learning model via computer simulations of observed changes in candidate evaluations over the 2000 presidential campaign. In these simulations, JQP reproduces responsiveness, persistence, and polarization of political attitudes, while the Bayesian learning model has difficulty accounting for persistence and polarization. We conclude that "motivated reasoning"-the discounting of information that challenges priors along with the uncritical acceptance of attitude-consistent information-is the reason our model can better account for persistence and polarization in candidate evaluations. Adapted from the source document.