ABSTRACTPeople hiss and swear when they make errors, frown and swear again when they encounter conflicting information. Such error- and conflict-related signs of negative affect are found even when there is no time pressure or external reward and the task itself is very simple. Previous studies, however, provide inconsistent evidence regarding the affective consequences of resolved conflicts, that is, conflicts that resulted in correct responses. We tested whether response accuracy in the Eriksen flanker task will moderate the effect of trial (...) incongruence using affective priming to measure positive and negative affect. We found that responses to incongruent trials elicit positive affect irrespective of their accuracy. Errors, in turn, result in negative affect irrespective of trial congruence. The effects of conflicts and errors do not interact and affect different dimensions of affective priming. Conflicts change the speed of evaluative categorisation while errors are reflected in categorisation accuracy... (shrink)

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors (...) allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors. (shrink)