Three notes on ALSCAL

It is reported that (1) a new coordinate estimation routine is superior to that originally proposed for ALSCAL; (2) an oversight in the interval measurement level case has been found and corrected; and (3) a new initial configuration routine is superior to the original.     

Disturbances of rhythm formation in patients with Parkinson's disease: part II. a forced oscillation model

The origin of the characteristic disturbances of rhythm formation in patients with Parkinson's disease (the hastening phenomenon) was discussed, using a second-order system of the periodic response. The input signal was regarded as a pulse series of a Dirac function. The output process of the system had maximal errors of response at input frequencies of f = omega0/n (n = 1, 2, . . .), where omega0 was the intrinsic frequency of the system. Damping coefficient epsilon represented a function of an inhibitor against these maximal errors and the errors diverged to infinity when epsilon = 0. The solution of this forced oscillation system indicated that the intrinsic oscillation of the system has a possibility to be excited at these critical frequencies f = omega/n. Inferred from data on the tapping test, the frequency of an intrinsic oscillation was 5 Hz in the central nervous system, then the critical frequencies were predicted 5/n = 5, 2.5, . . . Hz. On the tapping test the errors of response become maximum around 2.5 and 5 Hz (taps per second), and their peak heights increased from the minimum in well trained normal subjects to the maximum in patients. An inhibitory mechanism against the maximal error would function well, i.e. epsilon greater than 0, in normal subjects but so insufficiently (epsilon leads to 0) in patients that the excited intrinsic oscillation would control their response directly. Thus some patients could no longer maintain a synchronous tapping response at 2.5 Hz or 5 Hz and showed a hastened tapping of 5 6 Hz independent of the signal frequency.     

Cross-cultural Differences in Self-reported Decision-making Style and Confidence

The Melbourne Decision Making Questionnaire (Mann, Burnett, Radford, & Ford, 1997) measures self-reported decision-making coping patterns. The questionnaire was administered to samples of University students in the US (N = 475), Australia (N = 262), New Zealand (N = 260), Japan (N = 359), Hong Kong (N = 281), and Taiwan (N = 414). As predicted, students from the three Western, individualistic cultures (US, Australia, and New Zealand) were more confident of their decision-making ability than students from the three East Asian, group-oriented cultures (Japan, Hong Kong, Taiwan). No cross-cultural differences were found in scores on decision vigilance (a careful decision-making style). However, compared with Western students, the Asian students tended to score higher on buck-passing and procrastination (avoidant styles of decision making) as well as hypervigilance (a panicky style of decision making). Japanese students scored lowest on decision self-esteem and highest on procrastination and hypervigilance. It was argued that the conflict model and its attendant coping patterns is relevant for describing and comparing decision making in both Western and Asian cultures.     

Choice model analysis of the “pick any/n” type of binary data

In the “pick any/n” method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple-choice data or successive categories data where the number of response categories is limited to two. An item response model was proposed for the latter case, which is a combination of an unfolding model and a choice model. The marginal maximum-likelihood estimation method was developed for parameter estimation to avoid incidental parameters, and an expectation-maximization algorithm used for numerical optimization. Two examples of analysis are given to illustrate the proposed method, which we call MAXSC.     

CPCA: A program for principal component analysis with external information on subjects and variables

A program is described for principal component analysis with external information on subjects and variables. This method is calledconstrained principal component analysis (CPCA), in which regression analysis and principal component analysis are combined into a unified framework that allows a full exploration of data structures both within and outside known information on subjects and variables. Many existing methods are special cases of CPCA, and the program can be used for multivariate multiple regression, redundancy analysis, double redundancy analysis, dual scaling with external criteria, vector preference models, and GMANOVA (growth curve models).     

A generalization of Takane's algorithm for dedicom

An algorithm is described for fitting the DEDICOM model for the analysis of asymmetric data matrices. This algorithm generalizes an algorithm suggested by Takane in that it uses a damping parameter in the iterative process. Takane's algorithm does not always converge monotonically. Based on the generalized algorithm, a modification of Takane's algorithm is suggested such that this modified algorithm converges monotonically. It is suggested to choose as starting configurations for the algorithm those configurations that yield closed-form solutions in some special cases. Finally, a sufficient condition is described for monotonic convergence of Takane's original algorithm.Financial Support by the Netherlands organization for scientific research (NWO) is gratefully acknowledged. The authors are obliged to Richard Harshman.     

Functional Multiple-Set Canonical Correlation Analysis

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the method solves a matrix eigen-analysis problem through the adoption of a basis expansion approach to approximating data and weight functions. We apply the proposed method to functional magnetic resonance imaging (fMRI) data to identify networks of neural activity that are commonly activated across subjects while carrying out a working memory task.     

Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also incorporates direct and modulating effects of input variables on specific latent variables and on connections between latent variables, respectively. An alternating least square (ALS) algorithm is developed for parameter estimation. An improved bootstrap method called a modified moving block bootstrap method is used to assess reliability of parameter estimates, which deals with time dependence between consecutive observations effectively. We analyze synthetic and real data to illustrate the feasibility of the proposed method.     

Overdistribution in source memory

Semantic false memories are confounded with a second type of error, overdistribution, in which items are attributed to contradictory episodic states. Overdistribution errors have proved to be more common than false memories when the 2 are disentangled. We investigated whether overdistribution is prevalent in another classic false memory paradigm: source monitoring. It is. Conventional false memory responses (source misattributions) were predominantly overdistribution errors, but unlike semantic false memory, overdistribution also accounted for more than half of true memory responses (correct source attributions). Experimental control of overdistribution was achieved via a series of manipulations that affected either recollection of contextual details or item memory (concreteness, frequency, list order, number of presentation contexts, and individual differences in verbatim memory). A theoretical model was used to analyze the data (conjoint process dissociation) that predicts that (a) overdistribution is directly proportional to item memory but inversely proportional to recollection and (b) item memory is not a necessary precondition for recollection of contextual details. The results were consistent with both predictions.