Artificial intelligence for the measurement of vocal stereotypy

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session-by-session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.     

On the current status of the evolutionary theory of behavior dynamics

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior.     

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.     

Law,discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.     

A predictive coding model of gaze shifts and the underlying neurophysiology

A comprehensive model of gaze control must account for a number of empirical observations at both the behavioural and neurophysiological levels. The computational model presented in this article can simulate the coordinated movements of the eye, head, and body required to perform horizontal gaze shifts. In doing so it reproduces the predictable relationships between the movements performed by these different degrees of freedom (DOFs) in the primate. The model also accounts for the saccadic undershoot that accompanies large gaze shifts in the biological visual system. It can also account for our perception of a stable external world despite frequent gaze shifts and the ability to perform accurate memory-guided and double-step saccades. The proposed model also simulates peri-saccadic compression: the mis-localization of a briefly presented visual stimulus towards the location that is the target for a saccade. At the neurophysiological level, the proposed model is consistent with the existence of cortical neurons tuned to the retinal, head-centred, body-centred, and world-centred locations of visual stimuli and cortical neurons that have gain-modulated responses to visual stimuli. Finally, the model also successfully accounts for peri-saccadic receptive field (RF) remapping which results in reduced responses to stimuli in the current RF location and an increased sensitivity to stimuli appearing at the location that will be occupied by the RF after the saccade. The proposed model thus offers a unified explanation for this seemingly diverse range of phenomena. Furthermore, as the proposed model is an implementation of the predictive coding theory, it offers a single computational explanation for these phenomena and relates gaze shifts to a wider framework for understanding cortical function.     

Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.     

急性应激对威胁刺激注意定向和注意解除的影响:认知神经机制研究

急性应激和注意偏向是焦虑障碍和创伤后应激障碍发生和症状保持的两个重要因素。急性应激导致交感神经系统激活以及儿茶酚胺和糖皮质激素分泌增加,因而影响对威胁刺激的注意偏向。但是急性应激如何影响注意偏向中的注意定向和注意解除尚不清楚。本项目采用点探测任务、威胁线索空间提示任务,结合恐惧条件反射、眼动和事件相关电位技术,研究急性应激对注意定向和注意解除影响的认知神经机制。研究结果可为治疗焦虑和创伤后应激障碍提供支持,为公共卫生管理政策的制定提供建议。     

情节式未来思考中目标加工研究进展

情节式未来思考是个体将自我投射到未来以预先经历某事件的能力,个人目标加工在情节式未来思考中发挥着重要作用。目前对情节式未来思考中目标加工的行为学研究通常将其划分为目标过程模拟和目标结果模拟两种类型,以探究其对执行意图的影响为主;脑机制神经影像学研究则聚焦于默认网络的内侧前额叶和后扣带的重要作用,以及默认网络、额顶控制网络和奖赏区的协同作用。未来研究应进一步探讨情节式未来思考目标加工的形式、年龄差异及其与时间距离的关系。