Changes in Multidigit Synergies and Their Feed-Forward Adjustments in Multiple Sclerosis

The authors explored the changes in multidigit synergies in patients with multiple sclerosis (MS) within the framework of the uncontrolled manifold hypothesis. The specific hypotheses were that both synergy indices and anticipatory synergy adjustments prior to the initiation of a self-paced quick action would be diminished in the patients compared to age-matched controls. The MS patients and age-matched controls (n = 13 in both groups) performed one-finger and multifinger force production tasks involving both accurate steady-state force production and quick force pulses. The patients showed significantly lower maximal finger forces and a tendency toward slower force pulses. Enslaving was increased in MS, but only in the lateral fingers (index and little). Indices of multifinger synergies during steady-state force production were lower in MS, mainly due to the lower amount of intertrial variance that did not affect total force. Anticipatory synergy adjustments were significantly delayed in MS. The results show that MS leads to significant changes in multidigit synergies and feed-forward adjustments of the synergies prior to a quick action. The authors discuss possible contributions of subcortical structures to the impaired synergic control.     

How do reinforcers affect choice? Preference pulses after responses and reinforcers

In concurrent schedules, reinforcers are often followed by a brief period of heightened preference for the just‐productive alternative. Such ‘preference pulses’ may reflect local effects of reinforcers on choice. However, similar pulses may occur after nonreinforced responses, suggesting that pulses after reinforcers are partly unrelated to reinforcer effects. McLean, Grace, Pitts, and Hughes (2014) recommended subtracting preference pulses after responses from preference pulses after reinforcers, to construct residual pulses that represent only reinforcer effects. Thus, a reanalysis of existing choice data is necessary to determine whether changes in choice after reinforcers in previous experiments were actually related to reinforcers. In the present paper, we reanalyzed data from choice experiments in which reinforcers served different functions. We compared local choice, mean visit length, and visit‐length distributions after reinforcers and after nonreinforced responses. Our reanalysis demonstrated the utility of McLean et al.'s preference‐pulse correction for determining the effects of reinforcers on choice. However, visit analyses revealed that residual pulses may not accurately represent reinforcer effects, and reinforcer effects were clearer in visit analyses than in local‐choice analyses. The best way to determine the effects of reinforcers on choice may be to conduct visit analyses in addition to local‐choice analyses.     

A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

Introductory remarks by the issue editors: The multiple dimensions of the parent–baby story

Abstract

In their introductory remarks, the guest editors, themselves presenters at the conference “Parent–Infant Disturbance: Theory and Therapy,” introduce the philosophy and methodology underlying this interdisciplinary psychoanalytic conference and outline the multiple dimensions of the issue at hand, as they are addressed by the individual contributions to this volume. The paper concludes with a remembrance of and dedication to the victims of the Paris terrorist attack of November 13, 2015.     

Navigation in multiobjective optimization methods

Building on previous work of the authors, this paper formally defines and reviews the first approach, referred to as navigation , towards a common understanding of search and decision‐making strategies to identify the most preferred solution among the Pareto set for a multiobjective optimization problem. In navigation methods, the decision maker interactively learns about the problem, whereas the decision support system learns about the preferences of the decision maker. This work introduces a detailed view on navigation leading to the identification of integral components and features. A number of different existing navigation methods are reviewed and characterized. Finally, an overview of applications involving navigation is given, and promising future research direction are discussed.     

心理理论的ERP研究:基于多重成分框架的视角

心理理论是社会认知的核心能力之一。现有的心理理论ERP研究以心理理论较为公认的4个成分信念、愿望、意图和情绪为框架,表现出了一些一致性的规律:心理理论相关的脑电成分根据不同的心理理论成分而有所区分;不同心理理论成分诱发的相关脑电出现的位置表现出跨任务的一致性;心理理论的ERP发展研究在儿童身上表现出与成人类似的结果,但也具有时间窗口更长,位置分布更宽广的特性。未来的研究需要设计出更符合ERP研究技术要求的任务范式,并注意心理理论概念结构的完整性,还应探寻心理理论各成分加工的共同电生理基础。     

The regression trunk approach to discover treatment covariate interaction

The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable withmultiple covariates. The performance of RTA is compared to the classical method of forward stepwise regression. The results of two simulation studies, in which the true interactions are modeled as threshold interactions, show that RTA detects the interactions in a higher number of cases (82.3% in the first simulation study, and 52.3% in the second) than stepwise regression (56.5% and 20.5%). In a real data example the final RTA model has a higher cross-validated variance-accounted-for (29.8%) than the stepwise regression model (12.5%). All of these results indicate that RTA is a promising alternative method for demonstrating differential effectiveness of treatments. Supported by The Netherlands Organization for Scientific Research (NWO), grant 030-56403 to Jacqueline Meulman for the Pioneer project “Subject-Oriented Multivariate Analysis,” and grant 451-02-058 to Elise Dusseldorp for the Veni project “Modeling interaction effects as small trees in regression and classification.” We thank Bram Bakker for making the data of his doctoral dissertation available, and David Hand, Jerome Friedman, Bart Jan van Os, Philip Spinhoven, and the anonymous reviewers for their helpful suggestions. Special thanks are due to Lawrence Hubert.     

Experimental analysis and treatment of multiply controlled problem behavior: a systematic replication and extension

We evaluated interventions designed to reduce multiply controlled problem behavior exhibited by a young boy with developmental disabilities, using a multiple baseline design. Each intervention was designed to address a specific social function of problem behavior. Results showed that the separate interventions were useful in reducing problem behavior, and terminal schedules were reached by way of schedule thinning (attention condition) and delays to reinforcement (tangible and escape conditions).     

The effects of delayed reinforcement on variability and repetition of response sequences

Four experiments examined the effects of delays to reinforcement on key peck sequences of pigeons maintained under multiple schedules of contingencies that produced variable or repetitive behavior. In Experiments 1, 2, and 4, in the repeat component only the sequence right-right-left-left earned food, and in the vary component four-response sequences different from the previous 10 earned food. Experiments 1 and 2 examined the effects of nonresetting and resetting delays to reinforcement, respectively. In Experiment 3, in the repeat component sequences had to be the same as one of the previous three, whereas in the vary component sequences had to be different from each of the previous three for food. Experiment 4 compared postreinforcer delays to prereinforcement delays. With immediate reinforcement sequences occurred at a similar rate in the two components, but were less variable in the repeat component. Delays to reinforcement decreased the rate of sequences similarly in both components, but affected variability differently. Variability increased in the repeat component, but was unaffected in the vary component. These effects occurred regardless of the manner in which the delay to reinforcement was programmed or the contingency used to generate repetitive behavior. Furthermore, the effects were unique to prereinforcement delays.