A series of five experiments investigated the extent of subliminal processing of negation. Participants were presented with a subliminal instruction to either pick or not pick an accompanying noun, followed by a choice of two nouns. By employing subjective measures to determine individual thresholds of subliminal priming, the results of these studies indicated that participants were able to identify the correct noun of the pair – even when the correct noun was specified by negation. Furthermore, using a grey-scale contrast method (...) of masking, Experiment 5 confirmed that these priming effects were evidenced in the absence of partial awareness, and without the effect being attributed to the retrieval of stimulus–response links established during conscious rehearsal. (shrink)

The review first discusses componential explanations of automaticity, which specify non/automaticity features (e.g., un/controlled, un/conscious, non/efficient, fast/slow) and their interrelations. Reframing these features as factors that influence processes (e.g., goals, attention, and time) broadens the range of factors that can be considered (e.g., adding stimulus intensity and representational quality). The evidence reviewed challenges the view of a perfect coherence among goals, attention, and consciousness, and supports the alternative view that (a) these and other factors influence the quality of representations in (...) an additive way (e.g., little time can be compensated by extra attention or extra stimulus intensity) and that (b) a first threshold of this quality is required for unconscious processing and a second threshold for conscious processing. The review closes with a discussion of causal explanations of automaticity, which specify factors involved in automatization such as repetition and complexity, and a discussion of mechanistic explanations, which specify the low-level processes underlying automatization. (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)