Null hypotheses are simple, precise, and theoretically important. Conventional statistical analysis cannot support them; Bayesian analysis can. The challenge in a Bayesian analysis is to formulate a suitably vague alternative, because the vaguer the alternative is (the more it spreads out the unit mass of prior probability), the more the null is…

A dominant theme in modeling human perceptual judgments is that sensory neural activity is summed or integrated until a critical bound is reached. Such models predict that, in general, the shape of response time distributions change across conditions, although in practice, this shape change may be subtle. An alternative view is that response time…

Reports an error in "Finding Useful Questions: On Bayesian Diagnosticity, Probability, Impact, and Information Gain" by Jonathan D. Nelson (Psychological Review, 2005[Oct], Vol 112[4], 979-999). In Table 13, the data should indicate that 7% of females had short hair and 93% of females had long hair. The calculations and discussion in the article…

Several norms for how people should assess a question's usefulness have been proposed, notably Bayesian diagnosticity, information gain (mutual information), Kullback-Liebler distance, probability gain (error minimization), and impact (absolute change). Several probabilistic models of previous experiments on categorization, covariation assessment,…

It is shown that Bayesian algorithms are computationally simpler in frequency formats than in the probability formats used in previous research. Analysis of several thousand solutions to Bayesian problems showed that when information was presented in frequency formats, statistically naive participants derived up to 50% of inferences by Bayesian…