What’s in a looking time preference?

Looking time preference methods are an ubiquitous tool for tapping into infants' early skills and knowledge. However, predicting what preference infants will show in these paradigms can be difficult, and studies investigating the same ability oftentimes report opposing patterns of preference. For example, most studies investigating infant pattern learning report preferences for novel stimuli, but some report preference for familiar stimuli. How should such differences in preference direction be interpreted? One possibility is that any statistically significant preference is evidence for discrimination, such that all preferences provide similar evidential value. But another possibility is that the less-frequent preferences are so-called “sign errors”, in which a result is statistically significant, but the estimated effect size has the incorrect sign, e.g., showing a familiarity rather than novelty preference. In this paper, we use meta-analytic methods and statistical modeling to examine whether, when literatures show a heterogeneous pattern of looking time preferences, those preferences provide consistent evidential value, or whether one direction of preference may be a sign error. [Summary of included meta-analyses, results, implications]

Handling Multiplicity in Neuroimaging through Bayesian Lenses with Multilevel Modeling

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in thep-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

Anomalous phases in TE-mode magnetotellurics

This paper has been retracted by the author on 10 March 2015. It was brought to the author’s attention that equation 3 of this paper has the incorrect sign. This error has the serious consequence that the anomalous phases for the system discussed in the paper are much smaller in amplitude than reported, and hence the main conclusion, that any phase may be found in TE mode induction, has not been demonstrated and is likely incorrect.

Note on the preceding paper

We are grateful to Professor Hoyle for drawing attention to the incorrect sign that we arrived at for the factor λ in our paper (1959), but the changes consequent upon setting this right appear to be quite minor, and if anything lead to more acceptable though not very different, values from those we obtained for the various quantities involved. If the modified Maxwell equations are written now F v μ:v = 4 πJ μ - λK μ , when equations (52) on p. 324 of our paper (1959) should be replaced by E 00 = λ /8 π K 2 0 , E 11 = E 22 = E 33 = λ /8 π e 2 t/T K 2 0 . In mixed form, these are equivalent to E 0 0 = λ /8 π K 0 2 , E 1 1 = E 2 2 = E 3 3 = λ /8 π K 0 2 .