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 favored. A general solution is a sensitivity analysis: Compute the odds for or against the null as a function of the limit(s) on the vagueness of the alternative. If the odds on the null approach 1 from above as the hypothesized maximum size of the possible effect approaches 0, then the data favor the null over any vaguer alternative to it. The simple computations and the intuitive graphic representation of the analysis are illustrated by the analysis of diverse examples from the current literature. They pose three common experimental questions: 1) Are two means the same? 2) Is performance at chance? 3) Are factors additive?

Experimental results often support a null hypothesis, as shown by three illustrative examples from the recent literature. Conventional statistical analysis cannot support a null hypothesis, whereas Bayesian analysis can. The challenge in a Bayesian analysis is to formulate a suitably vague alternative to the null. The null is a precise hypothesis, while the alternatives to it are usually vague. Bayesian analysis penalizes vagueness: the vaguer the alternative, the more the null is favored when the observed effect is small. The question is: how vague should the alternative be? A general solution is to compute the odds for or against the null as a function of the upper limit on the vagueness of the alternative. If the odds favoring the null approach 1 from above as the hypothesized size of the effect approaches 0, then the data favor the null over any alternative to it. The simple computation and the highly intuitive graphic representation of the analysis are illustrated by the analysis of the three examples, for each of which the null is the theoretically consequential hypothesis.

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 favored. A general solution is a sensitivity analysis: Compute the odds for or against the null as a function of the limit(s) on the vagueness of the alternative. If the odds on the null approach 1 from above as the hypothesized maximum size of the possible effect approaches 0, then the data favor the null over any vaguer alternative to it. The simple computations and the intuitive graphic representation of the analysis are illustrated by the analysis of diverse examples from the current literature. They pose 3 common experimental questions: (a) Are 2 means the same? (b) Is performance at chance? (c) Are factors additive?

Latent print examinations are complex perceptual and cognitive tasks. Examiners rely on their visual systems to find similarities in pairs of prints. They then must compare the degree of perceived similarity against that found in previous examinations, and ultimately must decide whether the commonalities found between prints (as well as regions of unexplainable disagreement) merit the conclusion that the prints either did or did not come from the same source (or are inconclusive). This process involves perception, similarity judgments, memory, and decision-making. These abilities vary among people and can be improved with training and experience. They are also subject to potential biases and external influences. This chapter will illustrate, based on knowledge from the visual and cognitive sciences, how an understanding of the human mind is relevant and critical

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Investigating specificity of experimentally induced prior expectations in motion perception

■ Several studies report a right hemisphere advantage for visuospatial integration and a left hemisphere advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual feature combinations. A split-brain patient and normal control participants viewed multishape scenes presented in either the right or the left visual fields. Unbeknownst to the participants, the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and the test displays were presented in the left visual field (right hemisphere). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning. ■

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

We easily recognize objects and faces across a myriad of retinal images produced by each object. One hypothesis is that this tolerance (a.k.a. ‘‘invariance’’) is learned by relying on the fact that object identities are temporally stable. While we previously found neuronal evidence supporting this idea at the top of the nonhuman primate ventral visual stream (inferior temporal cortex, or IT), we here test if this is a general tolerance learning mechanism. First, we found that the same type of unsupervised experience that reshaped IT position tolerance also predictably reshaped IT size tolerance, and the magnitude of reshaping was quantitatively similar. Second, this tolerance reshaping can be induced under naturally occurring dynamic visual experience, even without eye movements. Third, unsupervised temporal contiguous experience can build new neuronal tolerance. These results suggest that the ventral visual stream uses a general unsupervised tolerance learning algorithm to build its invariant object representation.