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)

The question of when and how bottom‐up input is integrated with top‐down knowledge has been debated extensively within cognition and perception, and particularly within language processing. A long running debate about the architecture of the spoken‐word recognition system has centered on the locus of lexical effects on phonemic processing: does lexical knowledge influence phoneme perception through feedback, or post‐perceptually in a purely feedforward system? Elman and McClelland (1988) reported that lexically restored ambiguous phonemes influenced the perception of the following phoneme, (...) supporting models with feedback from lexical to phonemic representations. Subsequently, several authors have argued that these results can be fully accounted for by diphone transitional probabilities in a feedforward system (Cairns et al., 1995; Pitt & McQueen, 1998). We report results strongly favoring the original lexical feedback explanation: lexical effects were present even when transitional probability biases were opposite to those of lexical biases. (shrink)

Magnuson, McMurray, Tanenhaus, and Aslin [Cogn. Sci. 27 (2003) 285] suggest that they have evidence of lexical feedback in speech perception, and that this evidence thus challenges the purely feedforward Merge model [Behav. Brain Sci. 23 (2000) 299]. This evidence is open to an alternative explanation, however, one which preserves the assumption in Merge that there is no lexical–prelexical feedback during on‐line speech processing. This explanation invokes the distinction between perceptual processing that occurs in the short term, as an utterance (...) is heard, and processing that occurs over the longer term, for perceptual learning. (shrink)