Tool use by chimpanzees (Pan troglodytes) of the Arnhem Zoo community

Abstract: Chimpanzees have a large repertoire of tool-use behaviors. This study reports on the variety and the extent of tool use exhibited by the chimpanzees of the Arnhem Zoo community in The Netherlands, living in an enriched captive setting since 1971. The use of tools by 29 chimpanzees aged from 0 to 37 years was observed. We identified 13 types of tool use comparable to those found in the wild. Some of these types of tool use seem to be specific to this community, and can be explained by the ecological characteristics of this captive setting. Chimpanzees started to use tools from the age of 2 years. Young chimpanzees, from 5 to 9 years old, showed a greater repertoire of tool use than infants and adults. All types of tool use in the community have appeared by the age of 10, the age of puberty for chimpanzees. Multivariate analysis was applied for the 29 individuals by 13 types of tool use in a one-zero matrix. The results show two major categories of tool use, one in a practical or substantial context and the other in a nonpractical or play context. The subjects clustered into groups reflecting developmental stages, although there are great individual differences. In conclusion, this captive community provides a unique opportunity to clarify the details of tool use by chimpanzees.     

Fitting multivariage normal finite mixtures subject to structural equation modeling

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.     

Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables

Points of view analysis (PVA), proposed by Tucker and Messick in 1963, was one of the first methods to deal explicitly with individual differences in multidimensional scaling, but at some point was apparently superceded by the weighted Euclidean model, well-known as the Carroll and Chang INDSCAL model. This paper argues that the idea behind points of view analysis deserves new attention, especially as a technique to analyze group differences. A procedure is proposed that can be viewed as a streamlined, integrated version of the Tucker and Messick Process, which consisted of a number of separate steps. At the same time, our procedure can be regarded as a particularly constrained weighted Euclidean model. While fitting the model, two types of nonlinear data transformations are feasible, either for given dissimilarities, or for variables from which the dissimilarities are derived. Various applications are discussed, where the two types of transformation can be mixed in the same analysis; a quadratic assignment framework is used to evaluate the results.The research of the first author was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW); the research of the second author by the Netherlands Organization for Scientific Research (NWO Grant 560-267-029). An earlier version of this paper was presented at the European Meeting of the Psychometric Society, Leuven, 1989. We wish to thank Willem J. Heiser for his stimulating comments to earlier versions of this paper, and we are grateful to the Editor and anonymous referees for their helpful suggestions.     

Multivariate normal integrals and contingency tables with ordered categories

Ak-dimensional multivariate normal distribution is made discrete by partitioning thek-dimensional Euclidean space with rectangular grids. The collection of probability integrals over the partitioned cubes is ak-dimensional contingency table with ordered categories. It is shown that loglinear model with main effects plus two-way interactions provides an accurate approximation for thek-dimensional table. The complete multivariate normal integral table is computed via the iterative proportional fitting algorithm from bivariate normal integral tables. This approach imposes no restriction on the correlation matrix. Comparisons with other numerical integration algorithms are reported. The approximation suggests association models for discretized multivariate normal distributions and contingency tables with ordered categories.The contingency-table approach occurred to me while I was collaborating with Paul Holland of the Educational Testing Service in 1985 on bivariate dependence functions. Holland maintains a belief that the continuous can learn from the discrete. This work is a reassertion of his claim.This research was sponsored by the National Science Council, Republic of China.     

Integrating Dimensional and Discrete Theories of Emotions: A New Set of Anger- and Fear-Eliciting Stimuli for Children

The selection of appropriate stimuli for inducing specific emotional states has become one of the most challenging topics in psychological research. In the literature there is a lack of affective picture database specifically suited to investigate emotional response in children. Here the authors present the methodology that led us to create a new database (called Anger- and Fear-Eliciting Stimuli for Children) of affective stimuli inducing experiences of 3 target emotions (neutral, anger, and fear) to use in experimental session involving children. A total of 84 children were asked to (a) indicate the perceived emotion and its intensity and (b) rate the three affective dimensions of the Self-Assessment Manikin (SAM). Based on concordance between labeled and expected target emotion, the authors decided to select 15 stimuli to be included in Multivariate modeling techniques were applied to evaluate the association between expected target emotion and SAM ratings. The authors found that the hit rate for the neutral pictures was good (greater than 81%), for fear-eliciting pictures it was greater than 64%, and for anger-eliciting pictures it was moderate (between 45% and 56%). The study results reveal also an age effect only in the arousal scale. However, the authors did not find significant gender-related differences in SAM ratings.     

Linking vitamin D status,executive functioning and self‐perceived mental health in adolescents through multivariate analysis: A randomized double‐blind placebo control trial

The aim of the present randomized double‐blind placebo control trial was to investigate if vitamin D supplementation had an effect on vitamin D status, executive functioning and self‐perceived mental health in a group of Norwegian adolescents during winter time. Fifty adolescents were randomly assigned into an intervention group (vitamin D pearls) or a control group (placebo pearls). Before (pre‐test in December/January) and after (post‐test in April/May) the intervention period the participants were exposed to a test procedure, consisting of blood draw, completion of cognitive tests (Tower of Hanoi and Tower of London), and the Youth Self‐report version of the Child Behavior Checklist. Multivariate data analysis showed that participants with low vitamin D status scored worse on the Tower of London tests and the more difficult sub‐tasks on the Tower of Hanoi tests. They also had a tendency to report higher frequency of externalizing behavior problems and attention deficit. At pre‐test, the overall mean vitamin D status measured as 25‐hydroxy vitamin D was 42 nmol/L, defining deficiency (Intervention group = 44 nmol/L, Control group = 39 nmol/L). However, vitamin D supplementation caused a significant increase in vitamin D status resulting in a sufficient level in the Intervention group at post‐test (mean 62 nmol/L). The results also revealed that the intervention group improved their performance on the most demanding sub‐tasks on the ToH. Overall, the study indicates that vitamin D status in adolescents may be important for both executive functioning and mental health.     

视觉表象可视化——视觉表象研究的新途径

功能磁共振技术在表象研究中得到广泛应用是表象研究追求客观化精确性的必然趋势。本文介绍了功能磁共振多变量模式分析方法及其演变历程,探讨了借助该方法实现“视觉表象可视化”的理论依据与亟待解决的关键问题。分析指出“视觉表象可视化”将为表象研究提供全新的研究视角与方法途径。     

Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – A Method for Quantifying Correlation between Multivariate Time-Series

In this paper, Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) is introduced. It is an extension of Multidimensional Recurrence Quantification Analysis (MdRQA), which allows to quantify the (auto-)recurrence properties of a single multidimensional time-series. MdCRQA extends MdRQA to bi-variate cases to allow for the quantification of the co-evolution of two multidimensional time-series. Moreover, it is shown how a Diagonal Cross-Recurrence Profile (DCRP) can be computed from the MdCRQA output that allows to capture time-lagged coupling between two multidimensional time-series. The core concepts of these analyses are described, as well as practical aspects of their application. In the supplementary materials to this paper, implementations of MdCRQA and the DCRP as MatLab- and R-functions are provided.     

The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data

Abstract

In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward–Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward–Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data.