Variable rather than extreme slow reaction times distinguish brain states during sustained attention

A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that ‘suboptimal’ is different from ‘slow’ at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.

C-22 Examining the Factor Structure of the Clock-In-the-Box in Older Adults

Objective To investigate the factor structure of the Clock-In-the-Box (CIB), a cognitive screening measure, and compare it to the original CIB subscores (Working Memory and Planning/Organization) that were created based on clinical observations. The new factor structure was used to determine the predictive validity of the CIB subscores, in predicting cognitive diagnosis in an older veteran population. Methods Neuropsychological evaluations conducted at VA Boston Healthcare System were reviewed. Exploratory factor analysis (EFA) and logistic regression were employed to determine the predictive validity of the new CIB subscores compared to the original subscores. Results The cohort had a mean age of 69.77 years (SD = 10.12), 97% male and mainly white (84.9%). EFA revealed a best fit two-factor model, explaining 60% of the variance (Factor 1 - 46% and, Factor 2 - 14% of the variance). Factor 1 reflected conceptual items (i.e., numbers, resembles clock) while Factor 2 reflected planning/organizational items (i.e., hand length, number spacing). Factors were moderately correlated (r = .456). Logistic regression revealed the original and new subscores were equivalent in predicting cognitive impairment when controlling for age and education; correctly classified 82% of the cases. When controlling for age and education, only Factor 2 remained predictive of impairment. Conclusions Analysis of specific task items resulted in subscores that differ from those initially generated based on clinical experience, with both providing clinically useful information. The CIB is a brief instrument with good predictive validity of cognitive impairment and clinically useful as a first line screening to inform the need for further assessment.

Clash of the Titans: Factor Portfolios versus Alternative Weighting Schemes

In this article, the authors (re) introduce mean–variance portfolio construction for factor portfolios. These models, first popular with quants in the 1990s, are being resurrected today in a different context for transparent factor portfolios. The authors then evaluate the merits of these mean–variance factor portfolios against alternative weighting schemes. They point out that alternative weighting schemes have arguably weak theoretical foundations, and their supporters rationalize them with a range of (very different) reasons, most of them dissatisfying in the view of the authors. They then show that alternative weighting schemes derive a large part of their outperformance from a handful of well-known factors. The authors argue that sensibly built factor portfolios deliver a similar or higher information ratio by explicitly harnessing the factors and doing so in an efficient risk- and transaction cost-aware way.

PENGARUH FAKTOR PERSONAL, CULTURE AND PSHYCOLOGICALTERHADAP PEMILIHAN JURUSAN AKUNTANSI FAKULTAS EKONOMI UNIVERSITAS WIRARAJA

Pemilihan Jurusan Akuntansi Fakultas Ekonomi Universitas Wiraraja Sumenep dipengaruhi beberapa faktor, antara lainPersonal, Culture and Pshycological.Populasi dalam penelitian ini adalah seluruh mahasiswa Jurusan Akuntansi Fakultas Ekonomi Universitas Wiraraja Sumenep. Sampel dalam penelitian ini sebanyak 109 dengan menggunakan purposive sampling. Penelitian ini merupakan penelitian kuantitatif. Data dalam penelitian ini adalah data primer yang diperoleh melalui survey dengan menyebarkan kuesioner.Uji kualitas data menggunakan uji validitas dan reliabilitas. Uji asumsi klasik menggunakan Autokorelasi Durbin-Watson test, Uji Multicolinieritas dengan VIF (Variance Factor Infalaction) < 10, uji Heteroskedastisitas dengan grafik scatterplot, uji normalitas dengan P-P Plot of Regression Standardized, uji model regresidengan uji F dan uji t, dan interpretasi juga menggunakan SPSS 16. Hasil Uji F bahwa Personal (X1), Culture (X2) dan Pshycological (X3) secara simultan berpengaruh signifikan terhadap Pemilihan Jurusan Akuntansi Fakultas Ekonomi (Y), sedangkan dari Uji t Personal (X1), Culture (X2) dan Pshycological (X3) secara parsial berpengaruh signifikan terhadap Y (Pemilihan Jurusan Akuntansi Fakultas Ekonomi). Kata Kunci: Personal, Culture, Pshycological, Pemilihan Jurusan

Outlier Detection by means of Monte Carlo Estimation including resistant Scale Estimation

The identification of outliers in measurement data is hindered if they are present in leverage points as well as in rest of the data. A promising method for their identification is the Monte Carlo estimation (MCE), which is subject of the present investigation. In MCE the data are searched for data subsamples without leverage outliers and with few (or no) non-leverage outliers by a random generation of subsamples. The required number of subsamples by which several of such subsamples are generated with a given probability is derived. Each generated subsample is rated based on the residuals resulting from an adjustment. By means of a simulation it is shown that a least squares adjustment is suitable. For the rating of the subsamples, the sum of squared residuals is used as a measure of the fit. It is argued that this (unweighted) sum is also appropriate if data have unequal weights. An investigation of the robustness of a final Bayes estimation with the result of the Monte Carlo search as prior information reveals its inappropriateness. Furthermore, the case of an unknown variance factor is considered. A simulation for different scale estimators for the variance factor shows their impracticalness. A new resistant scale estimator is introduced which is based on a generalisation of the median absolut deviation. Taking into account the results of the investigations, a new procedure for MCE considering a scale estimation is proposed. Finally, this method is tested by simulation. MCE turns out to be more reliable in the identification of outliers than a conventional resistant estimation method.