Neuromechanical synergies in single-leg landing reveal changes in movement control

Our purpose was to examine changes in single-leg landing biomechanics and movement control following alterations in mechanical task demands via external load and landing height. We examined lower-extremity kinematic, kinetic, and electromyographic (EMG) adjustments, as well as changes in movement control from neuromechanical synergies using separate principal component analyses (PCA). Nineteen healthy volunteers (15M, 4F, age: 24.3 ± 4.9 y, mass: 78.5 ± 14.7 kg, height: 1.73 ± 0.08 m) were analyzed among 9 single-leg drop landing trials in each of 6 experimental conditions (3 load and 2 landing height) computed as percentages of subject bodyweight (BW, BW + 12.5%, BW + 25%) and height (H12.5% & H25%). Condition order was counterbalanced, including: 1.) BW·H12.5, 2.) BW + 12.5·H12.5, 3.) BW + 25·H12.5, 4.) BW·H25, 5.) BW + 12.5·H25, 6.) BW + 25·H25. Lower-extremity sagittal joint angles and moments (hip, knee, & ankle), vertical ground reaction force (GRFz), and electrical muscle activity (gluteus maximus, biceps femoris, vastus medialis, medial gastrocnemius, & tibialis anterior muscles), were analyzed in each trial. Biomechanical adjustments and neuromechanical synergies were assessed using PCA. Subjects reduced effective landing height through segmental configuration adjustments at ground contact, extending at the hip and ankle joints with greater load and landing height (p ⩽ 0.028 and p ⩽ 0.013, respectively), while using greater medial gastrocnemius pre-activation with greater load (p ⩽ 0.006). Dimension reduction was observed under greater mechanical task demands, compressing and restructuring synergies among patterns of muscle activation, applied loads, and segmental configurations. These results provide insight into movement control and potential injury mechanisms in landing activities.     

Ownership unity,neural substrates,and philosophical relevance: A response to Rex Welshon’s “Searching for the neural realizers of ownership unity”

In this commentary, I critically assess Rex Welshon’s position on the neural substrates of ownership unity. First, I comment on Welshon’s definition of ownership unity and underline some of the problems stemming from his phenomenological analysis. Second, I analyze Welshon’s proposal to establish a mechanistic relation between neural substrates and ownership unity. I show that it is insufficient and defend my own position on how neural mechanisms may give rise to whole subjects of experience, which I call the neuro-integrative account of consciousness. Lastly, I comment on Welshon’s (2013) philosophical contribution and claim that it leaves the reader theoretically stranded.     

情绪智力的基础：情绪觉察的研究现状与展望

情绪觉察作为情绪智力的重要基础,指的是识别与描述自己和他人情绪的能力,对个体的心理健康,人际交往具有重要意义。为系统考察情绪觉察的研究现状,采用定性和定量相结合的方式回顾以往研究,即先采用文献计量学的方法定量分析该领域研究的概况,然后在此基础上,对研究的具体内容从概念界定、测量工具、神经基础、相关因素和干预研究五个方面作进一步系统梳理。最后,对以往研究进行了总结和展望,以期为今后研究提供借鉴。     

A Time-Varying Measure of Dyadic Synchrony for Three-Dimensional Motion

We propose a novel approach to the analysis of synchronized three-dimensional motion in dyads. Motion recorded at high time resolution, as with a gaming device, is preprocessed in each of the three spatial dimensions by spline smoothing. Synchrony is then defined, at each time point, as the cosine between the two individuals’ estimated velocity vectors. The approach is extended to allow a time lag, allowing for the analysis of leader-follower dynamics. Mean square cosine over the time range is proposed as a scalar summary of dyadic synchrony, and this measure is found to be positively associated with cognitive empathy.     

多维项目反应理论补偿性模型参数估计:基于广义回归神经网络集合

运用广义回归神经网络(GRNN)方法对小样本多维项目反应理论(MIRT)补偿性模型的项目参数进行估计,尝试解决传统参数估计方法样本数量要求较大的问题。MIRT双参数Logistic补偿模型被设置为二级计分的二维模型。首先,模拟二维能力参数、项目参数值与考生作答矩阵。其次,把通过主成分分析得到的前两个因子在每个题目上的载荷作为区分度的初始值以及题目通过率作为难度的初始值,这两个指标的初始值作为神经网络的输入。集成100个神经网络,其输出值的均值作为MIRT的项目参数估计值。最后,设置2×2种(能力相关水平:0.3和0.7; 两种估计方法:GRNN和MCMC方法)实验处理,对GRNN和MCMC估计方法的返真性进行比较。结果表明,小样本的情况下,基于GRNN集成方法的参数估计结果优于MCMC方法。     

Categories of Large Numbers in Line Estimation

How do people stretch their understanding of magnitude from the experiential range to the very large quantities and ranges important in science, geopolitics, and mathematics? This paper empirically evaluates how and whether people make use of numerical categories when estimating relative magnitudes of numbers across many orders of magnitude. We hypothesize that people use scale words—thousand, million, billion—to carve the large number line into categories, stretching linear responses across items within each category. If so, discontinuities in position and response time are expected near the boundaries between categories. In contrast to previous work (Landy, Silbert, & Goldin, 2013) that suggested only that a minority of college undergraduates employed categorical boundaries, we find that discontinuities near category boundaries occur in most or all participants, but that accurate and inaccurate participants respond in opposite ways to category boundaries. Accurate participants highlight contrasts within a category, whereas inaccurate participants adjust their responses toward category centers.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

The AHA! experience: creativity through emergent binding in neural networks

Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can support cognitive and emotional processes underlying human creativity.     

Perceiving the Present: Systematization of Illusions or Illusion of Systematization?

Mark Changizi et al. (2008) claim that it is possible systematically to organize more than 50 kinds of illusions in a 7 × 4 matrix of 28 classes. This systematization, they further maintain, can be explained by the operation of a single visual processing latency correction mechanism that they call “perceiving the present” (PTP). This brief report raises some concerns about the way a number of illusions are classified by the proposed systematization. It also poses two general problems—one empirical and one conceptual—for the PTP approach.     

Effects of Attention on the Strength of Lexical Influences on Speech Perception: Behavioral Experiments and Computational Mechanisms

The effects of lexical context on phonological processing are pervasive and there have been indications that such effects may be modulated by attention. However, attentional modulation in speech processing is neither well documented nor well understood. Experiment 1 demonstrated attentional modulation of lexical facilitation of speech sound recognition when task and critical stimuli were identical across attention conditions. We propose modulation of lexical activation as a neurophysiologically plausible computational mechanism that can account for this type of modulation. Contrary to the claims of critics, this mechanism can account for attentional modulation without violating the principle of interactive processing. Simulations of the interactive TRACE model extended to include two different ways of modulating lexical activation showed that each can account for attentional modulation of lexical feedback effects. Experiment 2 tested conflicting predictions from the two implementations and provided evidence that is consistent with bias input as the mechanism of attentional control of lexical activation.