Modulation of brain oscillations during fundamental visuo-spatial processing: A comparison between female collegiate badminton players and sedentary controls

ObjectivesThe present study aimed to investigate the difference in fundamental cognitive processing and neural oscillations between badminton players and sedentary controls.DesignA cross-sectional design was adopted to address this issue.MethodsWe compared time-frequency electroencephalographic (EEG) activity from collegiate female badminton players (n = 12, aged 20.58 ± 2.75 years) and age-and gender-matched sedentary non-athletic controls (n = 13, aged 19.08 ± 2.10 years) when they performed a task that involves visuo-spatial attention and working memory.ResultsWe observed that players responded faster than controls on the task without suffering any increase in error responses. Correspondingly, the players, relative to controls, exhibited higher task-related modulations in beta power in the attention condition as well as in theta and beta power in the working memory condition. Notably, the behavior-EEG correlations revealed that better attention performance is associated with lower beta power, while greater working memory is related to higher theta power.ConclusionsOur results shed light on the mechanisms of athletic superiority in fundamental cognitive functioning: the higher theta synchronization points to a greater engagement of attention, whereas the higher beta desynchronization supports the contribution of processing speed (or motor-related processing) to better performance in athletes. This study extends current understanding by suggesting that enhanced neurocognitive function seen in athletes may transfer to fundamental tasks, giving insight into the generalizability of sport experience to neurocognitive functioning.     

Neural modeling and imaging of the cortical interactions underlying syllable production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production.     

Simulating single word processing in the classic aphasia syndromes based on the Wernicke-Lichtheim-Geschwind theory

The Wernicke-Lichtheim-Geschwind (WLG) theory of the neurobiological basis of language is of great historical importance, and it continues to exert a substantial influence on most contemporary theories of language in spite of its widely recognized limitations. Here, we suggest that neurobiologically grounded computational models based on the WLG theory can provide a deeper understanding of which of its features are plausible and where the theory fails. As a first step in this direction, we created a model of the interconnected left and right neocortical areas that are most relevant to the WLG theory, and used it to study visual-confrontation naming, auditory repetition, and auditory comprehension performance. No specific functionality is assigned a priori to model cortical regions, other than that implicitly present due to their locations in the cortical network and a higher learning rate in left hemisphere regions. Following learning, the model successfully simulates confrontation naming and word repetition, and acquires a unique internal representation in parietal regions for each named object. Simulated lesions to the language-dominant cortical regions produce patterns of single word processing impairment reminiscent of those postulated historically in the classic aphasia syndromes. These results indicate that WLG theory, instantiated as a simple interconnected network of model neocortical regions familiar to any neuropsychologist/neurologist, captures several fundamental "low-level" aspects of neurobiological word processing and their impairment in aphasia.     

Psychoacoustic cues to emotion in speech prosody and music

There is strong evidence of shared acoustic profiles common to the expression of emotions in music and speech, yet relatively limited understanding of the specific psychoacoustic features involved. This study combined a controlled experiment and computational modelling to investigate the perceptual codes associated with the expression of emotion in the acoustic domain. The empirical stage of the study provided continuous human ratings of emotions perceived in excerpts of film music and natural speech samples. The computational stage created a computer model that retrieves the relevant information from the acoustic stimuli and makes predictions about the emotional expressiveness of speech and music close to the responses of human subjects. We show that a significant part of the listeners’ second-by-second reported emotions to music and speech prosody can be predicted from a set of seven psychoacoustic features: loudness, tempo/speech rate, melody/prosody contour, spectral centroid, spectral flux, sharpness, and roughness. The implications of these results are discussed in the context of cross-modal similarities in the communication of emotion in the acoustic domain.     

基于双加工理论解释下信念偏差效应的神经机制

信念偏差效应是指人们已有的知识信念对逻辑推理的影响现象.对此,双加工理论认为这是由于信念偏向系统和逻辑分析系统之间的冲突所导致的.近年来,该理论得到来自认知神经科学证据的有力支持:一方面,基于功能磁共振成像(fMRI)、近红外光谱(NIRS)以及重复性经颅磁刺激(rTMS)等技术的研究从不同角度证实右侧前额皮层与信念偏差抑制有关;另一方面,基于脑电(ERP)的研究表明,晚期正成分和晚期负成分可能参与了不同推理类型下信念偏差效应的认知加工.未来的研究可以从工作记忆、不同加工阶段、推理前提本身可信度、实验分析、实验材料生态学效度以及思维训练等方面对信念偏差效应作进一步探索.     

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.     

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.