Nonlinear Bayesian analysis for single case designs

Several authors have suggested the use of multilevel models for the analysis of data from single case designs. Multilevel models are a logical approach to analyzing such data, and deal well with the possible different time points and treatment phases for different subjects. However, they are limited in several ways that are addressed by Bayesian methods. For small samples Bayesian methods fully take into account uncertainty in random effects when estimating fixed effects; the computational methods now in use can fit complex models that represent accurately the behavior being modeled; groups of parameters can be more accurately estimated with shrinkage methods; prior information can be included; and interpretation is more straightforward. The computer programs for Bayesian analysis allow many (nonstandard) nonlinear models to be fit; an example using floor and ceiling effects is discussed here.     

论格式塔心理理论中的系统科学思想

在系统科学出现之前,格式塔心理在事物的整体性、同型论、非线性动力系统、顿悟说和现象学的透视方法等方面非常地接近系统科学了。格式塔的整体论思想非常的丰富,甚至可以看做是贝塔朗菲系统论的初级版本;格式塔的同型论思想初步给出了心物关系难题的回答,可以看做是协同论的一个雏形;格式塔的动力场论首先提出解决非线性动力结构的问题,为非线性思维的成长带来了动力;最后,格式塔心理学研究的现象学透视法、拓扑学与向量学方法的引入使我们看到,格式塔理论已经是一种接近成熟的系统科学思想了。     

Do meditators and non-meditators have different HRV dynamics?

PurposeThe heart is a complex system and many researchers have been recently studying cardiac behavior using the theory of nonlinear dynamical systems. One of the most appealing tools for analyzing heart function is the heart rate variability (HRV) signal. This study aimed to elucidate the HRV dynamics of six distinct states: spontaneous normal breathing (SNB) and metronomic breathing (MB), as non-meditator groups, before Chinese Chi meditation (CCM), during CCM, before Kundalini yoga meditation (KYM), and during KYM, as meditator groups.MethodsThe HRV data were obtained from the Physionet database. Lagged Poincare indices, Lyapunov exponent (LE), Lempel-Ziv complexity (LZ), and 4 types of entropy were calculated.ResultsThe results showed the greatest discrepancies in the lagged Poincare indices for KYM and CCM. In contrast, the least variations were achieved for MB. Compared to SNB, an enhancement in the log energy entropy and a reduction in the LZ and other entropies were concluded during KYM and CCM practices. In contrast, a reverse pattern was observed for MB. Using support vector machine, HRV dynamics were classified with average accuracies of 99.14 and 98.2% and average sensitivities of 99.87 and 99.57% for pre-KYM and during KYM, respectively.ConclusionIt was shown that the HRV dynamics were significantly different in meditators and non-meditators.     

Dynamics of postural control during bilateral stance – Effect of support area,visual input and age

The present study investigated the effect of support area, visual input and aging of the dynamics of postural control during bilateral stance. Fifteen young (22.1 ± 1.7 years) and fifteen older (68.3 ± 2.7 years) individuals completed four different 90 s bilateral stance trials: 1) shoulder wide feet distance with eyes open, 2) shoulder wide feet distance with eyes closed, 3) narrow feet distance with eyes open, and 4) narrow feet distance with eyes closed on a force plate form. The anterior (AP) and mediolateral (ML) center of pressure (COP) trajectories were calculated from the middle 60 s of the ground reaction forces and moments. Sample entropy (SaEn), correlation dimension (CoD), the largest Lyapunov exponent (LyE) and entropic half-life (ENT½) were calculated for the COP in both directions. In young individuals, a narrower support area resulted in a restricted movement solution space with lower SaEn, lower LyE and longer ENT½ in the executed motor control strategy, whereas it increased the CoD in the older individuals. During the eyes closed trials, SaEn, CoD and LyE increased and decreased ENT½ for both groups in the AP direction and increased SaEn and LyE in the ML direction for the older individuals alone. This indicates that aging is associated with direction- and task-dependent changes in the dynamics of the executed COP movements during postural stance tasks.     

Multilayer cognitive architecture for UAV control

Extensive use of unmanned aerial vehicles (UAVs) in recent years has induced the rapid growth of research areas related to UAV production. Among these, the design of control systems capable of automating a wide range of UAV activities is one of the most actively explored and evolving. Currently, researchers and developers are interested in designing control systems that can be referred to as intelligent, e.g. the systems which are suited to solve such tasks as planning, goal prioritization, coalition formation, etc. and thus guarantee high levels of UAV autonomy. One of the principal problems in intelligent control system design is tying together various methods and models traditionally used in robotics and aimed at solving such tasks as dynamics modeling, control signal generation, location and mapping, path planning, etc. with the methods of behavior modeling and planning which are thoroughly studied in cognitive science. Our work is aimed at solving this problem. We propose layered architecture—STRL (strategic, tactical, reactive, layered)—of the control system that automates the behavior generation using a cognitive approach while taking into account complex dynamics and kinematics of the control object (UAV). We use a special type of knowledge representation—sign world model—that is based on the psychological activity theory to describe individual behavior planning and coalition formation processes. We also propose path planning methodology which serves as the mediator between the high-level cognitive activities and the reactive control signals generation. To generate these signals we use a state-dependent Riccati equation and specific method for solving it. We believe that utilization of the proposed architecture will broaden the spectrum of tasks which can be solved by the UAV’s coalition automatically, as well as raise the autonomy level of each individual member of that coalition.     

Are there Reasons to Challenge a Symbolic Computationalist Approach in Explaining Deductive Reasoning?

The majority of the existing theories explaining deductive reasoning could be included in a classic computationalist approach of the cognitive processes. In fact, deductive reasoning could be seen to be the pinnacle of the symbolic computationalism, its last fortress to be defended in the face of new, dynamic, and ecological perspectives over cognition. But are there weak points in that position regarding deductive reasoning? What would be the reasons for which new perspectives could gain in credibility? What could be their most important tenets? The answers given to those questions in the paper include two main points. The first one is that the present empirical data could not sustain unambiguously one view over the other, that they are obtained in artificial experimental conditions, and that there are data that are not easily explainable using the traditional computationalist paradigm. The second one is that approaching the deductive reasoning from dynamic and ecological perspectives could have significant advantages. The most obvious one is the possibility to integrate more easily the research regarding the deductive reasoning with the results obtained in other domains of the psychology (especially in what respects the lower cognitive processes), in artificial intelligence or in neurophysiology. The reasons for that would be that such perspectives, as they are sketched in the paper, would imply, essentially, processes of second-order pattern formation and recognition (as it is the case for perception), embodied cognition, and dynamic processes as the brain ones are.     

Hierarchical organization of a reference system in newborn spontaneous movements

In this paper, we studied spontaneous newborn movements regarding the coordination of the four limbs, arms and legs, from a dynamic perspective. We used the method of recurrence plots to analyse the kinematic data from audiovisual recordings of neonates. We identified temporal and spatial synchronization of the four limbs that resulted in high recurrence patterns of biomechanical reference configurations. Furthermore, we identified transitions between linear and nonlinear epochs in the movement behavior of newborns on different time scales by means of recurrence quantification analysis. Results are discussed in the context of the concept of a structural hierarchy, in which different time scales correspond to hierarchical levels of organization.     

Safe and optimal navigation for autonomous multi-rotor aerial vehicle in a dynamic known environment by a decomposition-coordination method

In this paper, we present a new solution for the Autonomous navigation problem, using a Decomposition-Coordination Method (DCM) 1. The main purpose of this work is to compute an optimal and safe path for the multi-rotor Unmanned Aerial Vehicle (UAV) in a dynamic environment, moving from an initial location to the desired state. We assume that the flight environment is totally known to a supervisory unit, and the positions and trajectories of dynamic obstacles could be known in real-time, thus to perform in such environment a high reactivity is required as well as good connectivity with the supervisory unit that provides the safe path, each time one obstacle or more are detected on the road, so that the UAV could autonomously diverts from the unsafe path to the new safe one, and avoid the potential collisions. First and foremost, we choose a generalized nonlinear model for the multi-rotors in view of the rotational and translational dynamics of the UAV. We then associate that model with the objective functions. After that, we proceed to the resolution of the multi-objective optimization problem using our approach of decomposition-coordination. The principle of this method consists in decomposing the system into several smaller subsystems to simplify the treatment. Then we achieve the coordination afterward using Lagrange multipliers. To prove the convergence and stability of our method we make use of a Lyapunov function chosen particularly for this system. In the last section we present the simulation results, to confirm the reliability of our method.     

Is the relationship between self-esteem and physical aggression necessarily U-shaped?

Recent research by Perez, Vohs, and Joiner [Perez, M., Vohs, K. D., & Joiner, T. E., Jr. (2005). Discrepancies between self- and other-esteem as correlates of aggression. Journal of Social and Clinical Psychology, 24, 607–620] has supported a U-shaped curvilinear relationship between self-esteem and physical aggression in a sample of 140 undergraduates. The present study attempted to replicate this effect with a sample size more than 12 times larger. Thus, 1781 undergraduates completed items from Rosenberg’s [Rosenberg, M. (1965). Society and adolescent self-image. Princeton, NJ: Princeton University] Self-Esteem Scale and from the Physical Aggression subscale of Buss and Perry’s [Buss, A. H., & Perry, M. (1992). The Aggression Questionnaire. Journal of Personality and Social Psychology, 63, 452–459] Aggression Questionnaire. The results did not support a U-shaped relationship between self-esteem and physical aggression; if anything, they supported an inverted U-shaped one, such that the simple relationship between self-esteem and physical aggression became more negative and as self-esteem increased. Controlling for gender strengthened these effects, consistent with a pattern of mutual suppression between gender and self-esteem.     

Signal detection models for the same–different task

Signal detection models for the same–different task are presented. In contrast to the standard approach that only considers the proportion correct, the models apply to the full four by two same–different table. The approach allows one to consider models that recognize bias and other effects. Two basic signal detection models, associated with different decision rules, are presented. A version of the covert decisions rule is introduced that directly allows for same–different bias, in contrast to earlier versions. It is shown how to fit the models with standard software for nonlinear mixed models. The models are applied to data from a recent same–different study.