• behavior prediction;
  • biased information processing;
  • decision making;
  • implicit measures;
  • voting behavior

Implicit measures have become very popular in virtually all areas of basic and applied psychology. However, there are empirical and theoretical arguments that might raise doubts about their usefulness in research on political attitudes. Based on a review of relevant evidence, we argue that implicit measures can be useful to identify distal sources of political preferences in domains where self-presentation may bias self-reports (e.g., influence of racial attitudes on voting decisions). In addition, implicit measures of proximal political attitudes can contribute to the prediction of future political decisions by virtue of their capability to predict biases in the processing of decision-relevant information (e.g., prediction of voting behavior of undecided voters). These conclusions are supported by research showing that implicit measures predict real-world political behavior over and above explicit measures. The reviewed findings suggest that implicit measures may serve as a useful supplement to improve the prediction of election outcomes. Open questions and potential directions for future research are discussed.