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We investigated processes of truth validation during reading. Participants responded to 'true' and 'false' probes after reading simple true or false sentences. Compatible sentence/probe combinations (true/'true', false/'false') facilitated responding compared with incompatible combinations (true/'false', false/'true'), indicating truth validation. Evidence for truth validation was obtained after inducing an evaluative mindset but not after inducing a non-evaluative mindset, using additional intermixed tasks requiring true/false decisions or sentence comparisons, respectively. Event-related potentials revealed an increased late negativity (500-1000 ms after onset of the last word of sentences) for false compared with true sentences. Paralleling behavioral results, this electroencephalographic marker only obtained in the evaluative mindset condition. Further, mere semantic mismatches between subject and object of sentences led to an elevated N400 for both mindset conditions. Taken together, our findings suggest that truth validation is a conditionally automatic process that is dependent on the current task demands and resulting mindset, whereas the processing of word meaning and semantic relations between words proceeds in an unconditionally automatic fashion.

Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.