An eyewitness paradox

Two methods of analysing data from an identification parade give rise to seemingly contradictory outcomes, both of which reach statistical significance. The traditional method is based on the first choices of eyewitnesses, whereas the paired-comparison method takes account of each eyewitness's full rank ordering of the lineup participants. The person that most eyewitnesses pick out as the perpetrator of an offence (according to the first-choice method) may in fact be the person that those same eyewitnesses as a group regard as least likely to have committed the act (according to the paired-comparison method). Implications of the paradox for the analysis of lineup data are considered.     

Expertise reversal effect in a pen-tablet-based learning environment: The role of learningcentered emotions in the interplay between learner expertise and task complexity

The study investigated interactions between learner expertise and task complexity evaluated from both cognitive and affective perspectives. One hundred and seventy-three students, both novices and advanced learners, were asked to learn Japanese writing in a pen-tablet-based digital learning environment with varying task complexity levels. Cognitive load and learning-centred emotions were measured at intervals during learning, while writing performance was monitored by runtime tracking. Results indicated that while advanced learners performed better than novices across the range of task complexity, the moderate task complexity was shown to be superior in enhancing performance for both levels of expertise. Results for learning-centred emotions showed that advanced learners reported lower enjoyment and higher frustration when completing the low complexity task, whereas the moderately complex task was reported to be the most enjoyable and less frustrating for these learners. No significant difference in emotions was found across levels of task complexity for novices. Finally, a constructed composite indicator of cognitive-affective efficiency of instructional conditions showed a significant interaction between levels of learner expertise and task complexity primarily caused by affective factors.     

Category discrimination by pigeons using five polymorphous features

Eight pigeons were trained to discriminate between sets of color photographs of natural scenes. The scenes differed along five two-valued dimensions (site, weather, camera distance, camera orientation, and camera height), and all combinations of the feature values were used. One value of each dimension was designated as positive, and slides containing three or more positive feature values were members of the positive stimulus set. Thus, each feature had an equal, low, correlation with reinforcement, and all features had zero correlations with each other. Seven of the 8 pigeons learned this discrimination, and their responding came under the control of all five features. Within the positive and negative stimulus sets, response rates were higher to stimuli that contained more positive feature values. Once discrimination had been achieved, reversal training was given using a subset of the slides. In this subset, only a single feature was correlated with reinforcement. All pigeons learned this reversal successfully and generalized it to additional photographs with the same feature content. After reversal, the original reinforcement contingencies were reinstated, and training was continued using all the slides except those that had been used in reversal. Reversal generalized to these slides to some extent. Analysis of the response rates to individual slides showed that, compared with prereversal training, only the feature that had been subjected to reversal contingencies showed a reversed correlation with response rate. The remaining features showed the same correlation with response rate as they had before reversal training. Thus, reversal on some members of a category following category discrimination training led to generalization to stimuli within the category that were not involved in the reversal, but not to features that were not reversed. It is therefore inappropriate to refer to the pigeons as learning a concept.     

Interpreting canonical correlation analysis through biplots of structure correlations and weights

This paper extends the biplot technique to canonical correlation analysis and redundancy analysis. The plot of structure correlations is shown to the optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate regression and canonical correlation analysis/redundancy analysis is exploited for producing an optimal biplot that displays a matrix of regression coefficients. This plot can be made from the canonical weights of the predictors and the structure correlations of the criterion variables. An example is used to show how the proposed biplots may be interpreted.     

A simplification of a result by zellini on the maximal rank of symmetric three-way arrays

Zellini (1979, Theorem 3.1) has shown how to decompose an arbitrary symmetric matrix of ordern ×n as a linear combination of 1/2n(n+1) fixed rank one matrices, thus constructing an explicit tensor basis for the set of symmetricn ×n matrices. Zellini's decomposition is based on properties of persymmetric matrices. In the present paper, a simplified tensor basis is given, by showing that a symmetric matrix can also be decomposed in terms of 1/2n(n+1) fixed binary matrices of rank one. The decomposition implies that ann ×n ×p array consisting ofp symmetricn ×n slabs has maximal rank 1/2n(n+1). Likewise, an unconstrained INDSCAL (symmetric CANDECOMP/PARAFAC) decomposition of such an array will yield a perfect fit in 1/2n(n+1) dimensions. When the fitting only pertains to the off-diagonal elements of the symmetric matrices, as is the case in a version of PARAFAC where communalities are involved, the maximal number of dimensions can be further reduced to 1/2n(n–1). However, when the saliences in INDSCAL are constrained to be nonnegative, the tensor basis result does not apply. In fact, it is shown that in this case the number of dimensions needed can be as large asp, the number of matrices analyzed.     

SEQUENTIAL APPLICATION OF MAJOR HABIT-REVERSAL COMPONENTS TO TREAT MOTOR TICS IN CHILDREN

In this study, we sequentially administered up to four components of the habit-reversal treatment to 4 children with motor tics within a multiple baseline design. The habit-reversal components included (a) awareness training; (b) awareness training and self-monitoring; (c) awareness training, self-monitoring, and social support; and (d) awareness training, social support, and the use of a competing response. Results demonstrated that the combined use of awareness training, social support, and competing response training was effective in eliminating motor tics in 2 of 4 children, that awareness training alone was effective for 1 child, and that a combination of awareness training and self-monitoring was effective for the 4th child. The treatment and ensuing improvement were found to be socially valid. We discuss possible explanations for these results and recommend directions for future research.     

AUGMENTING SIMPLIFIED HABIT REVERSAL WITH AN AWARENESS ENHANCEMENT DEVICE: PRELIMINARY FINDINGS

The chronic hair pulling of a 36-year-old woman with moderate mental retardation was initially treated with a simplified habit-reversal (SHR) procedure that consisted of awareness training, competing response training, and social support. When SHR did not produce large and sustained reductions in hair pulling, an awareness enhancement device was added, and it reduced hair pulling to near-zero levels in two settings. The results are discussed, and directions for future research with this device are provided.     

Minimum rank and minimum trace of covariance matrices

This paper considers some mathematical aspects of minimum trace factor analysis (MTFA). The uniqueness of an optimal point of MTFA is proved and necessary and sufficient conditions for a point x to be optimal are established. Finally, some results about the connection between MTFA and the classical minimum rank factor analysis will be presented.     

Multilevel metric rank match for person re-identification

Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level, the extracted features are ranked to find the hard sample pairs and the backward dissemination triplet loss. Each rank level has different penalize value to adjust the network, in which the value is bigger with the deeper level of the whole network. Experiment results on CUHK03, Market1501 and DukeMTMC datasets indicate that The MMRM algorithm can outperform the previous state-of-the-arts.