Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

A bidirectional heteroassociative memory for binary and grey-level patterns

Typical bidirectional associative memories (BAM) use an offline, one-shot learning rule, have poor memory storage capacity, are sensitive to noise, and are subject to spurious steady states during recall. Recent work on BAM has improved network performance in relation to noisy recall and the number of spurious attractors, but at the cost of an increase in BAM complexity. In all cases, the networks can only recall bipolar stimuli and, thus, are of limited use for grey-level pattern recall. In this paper, we introduce a new bidirectional heteroassociative memory model that uses a simple self-convergent iterative learning rule and a new nonlinear output function. As a result, the model can learn online without being subject to overlearning. Our simulation results show that this new model causes fewer spurious attractors when compared to others popular BAM networks, for a comparable performance in terms of tolerance to noise and storage capacity. In addition, the novel output function enables it to learn and recall grey-level patterns in a bidirectional way.     

Effects of backpack weight on the performance of basic short-term/working memory tasks during flat-surface standing

This study empirically investigated the effects of backpack weight on the performance of three basic short-term/working memory (STM/WM) tasks during flat-surface standing. Four levels of backpack weight were considered: 0, 15, 25 and 40% of the body weight. The three STM/WM tasks were the Corsi block, digit span and 3-back tasks, corresponding to the visuo-spatial sketchpad, phonological loop and central executive of WM, respectively. Thirty participants conducted the STM/WM tasks while standing with loaded backpack. Major study findings were that (1) increased backpack weight adversely affected the scores of all three STM/WM tasks; and, (2) the adverse effect of backpack weight was less pronounced for the phonological loop STM task than the other STM/WM tasks. The study findings may help understand and predict the impacts of body-worn equipment weight on the worker’s mental task performance for various work activities requiring simultaneous performance of mental and physical tasks.

Practitioner summary: The current study empirically examined the effects of backpack weight on the performance of three basic STM/WM tasks. The study findings entail that reduces the weight of body-worn equipment can positively impact the worker’s mental task performance in addition to reducing the worker's bodily stresses.

Abbreviations: ACC: anterior cingulate cortex; AP: anterior-posterior; BW: body weight; CoP: centre of pressure; C-S: central executive working memory task and standing; DLPFC: dorsolateral prefrontal cortex; HIP: human information processing; ML: medio-lateral; PMC: premotor cortex; P-S: phonological loop short-term memory task and standing; SMA: supplementary motor area; STM: short-term memory; VLPFC: ventrolateral prefrontal cortex; V-S: visuo-spatial short-term memory task and standing; WM: working memory     

模糊联想记忆网络的增强学习算法

针对 Kosko提出的最大最小模糊联想记忆网络存在的问题 ,通过对这种网络连接权学习规则的改进 ,给出了另一种权重学习规则 ,即把 Kosko的前馈模糊联想记忆模型发展成为模糊双向联想记忆模型 ,并由此给出了模糊快速增强学习算法 ,该算法能存储任意给定的多值训练模式对集 .其中对于存储二值模式对集 ,由于其连接权值取值 0或 1,因而该算法易于硬件电路和光学实现 .实验结果表明 ,模糊快速增强学习算法是行之有效的 .     

A Bidirectional Hetero-Associative Memory for True-Color Patterns

Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of true-color patterns.     

Scene analysis by integrating primitive segmentation andassociative memory

Scene analysis is a major aspect of perception and continues to challenge machine perception. This paper addresses the scene-analysis problem by integrating a primitive segmentation stage with a model of associative memory. The model is a multistage system that consists of an initial primitive segmentation stage, a multimodule associative memory, and a short-term memory (STM) layer. Primitive segmentation is performed by a locally excitatory globally inhibitory oscillator network (LEGION), which segments the input scene into multiple parts that correspond to groups of synchronous oscillations. Each segment triggers memory recall and multiple recalled patterns then interact with one another in the STM layer. The STM layer projects to the LEGION network, giving rise to memory-based grouping and segmentation. The system achieves scene analysis entirely in phase space, which provides a unifying mechanism for both bottom-up analysis and top-down analysis. The model is evaluated with a systematic set of three-dimensional (3-D) line drawing objects, which are arranged in an arbitrary fashion to compose input scenes that allow object occlusion. Memory-based organization is responsible for a significant improvement in performance. A number of issues are discussed, including input-anchored alignment, top-down organization, and the role of STM in producing context sensitivity of memory recall.     

基于广义逆的指数型联想存储器模型

基于Ｋｏｈｏｎｅｎ的广义逆联想存储模型ＧＩＡＭ（ｇｅｎｅｒａｌｉｚｅｄｉｎｖｅｒｓｅａｓｏｃｉａｔｉｖｅｍｅｍｏｒｙ）和Ｍｕｒａｋａｍｉ的最小平方联想存储ＬＳＡＭ（ｌｅａｓｔｓｑｕａｒｅｓａｓｓｏｃｉａｔｉｖｅｍｅｍｏｒｙ）原理，本文提出了一个指数型联想存储器．该模型的存储性能经计算机模拟证实，远远优于ＧＩＡＭ和ＬＳＡＭ，通过适当地调节参数，几乎可达到完全的联想．对输入噪声方差，无需先验假设，同时还实现了一定程度的非线性映射特性.     

基于记忆的人工鱼认知模型

为丰富虚拟海洋环境中人工鱼的认知能力，以自然鱼的生物原理和记忆机制为理论依据，提出了一种基于记忆的人工鱼认知模型。信息在瞬时记忆、短时记忆和长时记忆3个阶段传输和存储，人工鱼通过聚焦器提取所关注的感知信息，通过决策器和短时记忆进行行为选择，将经验知识存储在长时记忆中，被短时记忆调用以优化当前决策。动画结果表明，采用此模型后人工鱼能产生记忆指导下的行为，表现出更真实更智能的生命特征。     

Feature extraction using fuzzy inverse FDA

This paper proposes a new method of feature extraction and recognition, namely, the fuzzy inverse Fisher discriminant analysis (FIFDA) based on the inverse Fisher discriminant criterion and fuzzy set theory. In the proposed method, a membership degree matrix is calculated using FKNN, then the membership degree is incorporated into the definition of the between-class scatter matrix and within-class scatter matrix to get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experimental results on the ORL, FERET face databases and pulse signal database show that the new method outperforms Fisherface, fuzzy Fisherface and inverse Fisher discriminant analysis.     

A Brain-Inspired Method of Facial Expression Generation Using Chaotic Feature Extracting Bidirectional Associative Memory

Human cognitive system adapts many different environments by exhibiting a broad range of behaviors according to the context. These behaviors vary from general abstractions referred as prototypes to specific perceptual patterns referred as exemplars. A chaotic feature extracting associative memory is proposed to mimic human brain in generating prototype and exemplar facial expressions. This model automatically extracts features of each category of images related to a specific subject and expression. In the training phase, the features are extracted as fixed points. In recall phase, the output attractor of the network ranges from fixed point which results in a prototype facial image, to chaotic attractors which lead to generating exemplar faces. The generative model is applied to enrich a facial image dataset in terms of variability by generating various virtual patterns, in case that only one image per subject is provided. A face recognition task is implemented to compare the enriched and original dataset in training classifiers. Our results show that recognition accuracy increases from 32 to 100% when exemplars generated by the proposed model are used to enrich the training dataset.     

模糊蕴含运算和模糊推理研究

1 引言模糊推理是一种利用模糊集合论研究不确定性问题的方法。实际上多输入多输出系统的推理问题的关键是单输入单输出系统的模糊推理问题,因此本文只考虑单输入单输出系统的模糊推理问题。模糊推理中涉及到两个主要的问题:一是模糊蕴涵算子的选择,二是推理合成方式的选择。模糊蕴涵算子将规则转换成     

一个具有完备性和鲁棒性的模糊规则提取算法

从实际检测数据中提取模糊规则进而建立有效的模糊模型对实现复杂系统的智能建模与控制具有重要意义. 在一些文献中对该问题进行了较深入的研究, 并提出了有效的从数值数据中提取模糊规则的算法(简称为WM 和iWM算法). 对WM和iWM算法的进一步分析研究表明, 该算法在完备性和鲁棒性方面还有进一步改进的可能. 本文采用数据挖掘技术提出一个改进的提取模糊规则的算法(简称DM 算法), 并在完备性和鲁棒性方面与WM 和iWM算法进行了比较研究. 模糊建模实例表明, 本文提出的DM算法具有更好的逼近能力和对不确定数据干扰的鲁棒性.     

Weak atomicity for the x86 memory consistency model

We consider the interaction of weakly atomic Software Transactional Memory (STM) providing single global lock atomicity with the x86 memory consistency model. We show that a practical design for such an STM requires that some program behaviour be disallowed, due to the strictness of the x86 memory consistency model in comparison to the language level memory models hitherto considered in weakly atomic STM designs. We present the design and construction of such an STM that disallows races between a transactional read and a non-transactional write. We also report on a practical application of this STM to elide legacy locks in x86 binaries. This allows software transactional memory to be applied without requiring software to be a priori written with awareness of transactional memory and without any restriction on source language or compiler. As an example, we show how a mainstream multiplayer game can use transactional memory with zero changes and 11% overhead over language level transactional memory, which requires over 700 annotations and severely restricts software development.     

A simple method for identification of singleton fuzzy models

This article presents a simple method for constructing a singleton fuzzy model from a given set of input/output data. The method consists of three computational steps: the initial phase, the growth phase, and the optional refining phase. The universe of discourse and two linguistic terms for each input variable and a rule base are established during the initial phase. Additional linguistic terms and rules are then appended sequentially during the growth phase to modify the model structure and to elevate the performance. During the optional refining phase the overall modelling performance can be further improved by adjusting the singleton outputs of the rule set in the sense of least squares. The proposed identification method can simultaneously provide an appropriate model structure and parameters without any time-consuming optimisation. Several numerical examples demonstrate the effectiveness of the proposed identification method.     

基于模糊树模型的自适应直接逆控制

基于模糊树模型, 结合神经网络中的逆向学习和专门化学习, 提出了自适应直接逆控制方法. 首先离线辨识对象的逆模型作为初始的控制器, 然后与对象串联, 用最小均方差 (Least mean square, LMS) 算法在线调节控制器中的线性参数. 本方法辨识得到的逆模型控制器可以减少需要的模糊规则数目, 同时达到较好的跟踪控制效果. 仿真结果表明了方法的有效性.     

基于CI-HMM的运动想象脑电信号分类

针对隐马尔科夫模型在运动想象脑电信号分类应用中,其独立性假设与脑电信号间相关性的不一致问题,提出一种基于Choquet 模糊积分隐马尔科夫模型的脑电信号分类方法。该模型应用模糊积分的单调性取代了概率测度的可加性,放宽了隐马尔科夫模型的独立性假设。利用重叠滑动窗对脑电信号分段,然后对每段数据提取绝对均值、波长和小波包相对能量特征,构成特征序列用于CI-HMM的训练和分类。选取2008年BCI竞赛Datasets 1的两类运动想象数据进行分类,实验结果表明,该方法有效提高了隐马尔科夫模型方法对运动想象脑电信号分类的性能。     

基于正交最小二乘和UD分解的模糊建模方法

通过改进模糊聚类方法确定模糊模型的前件结构，并对模糊推理关系矩阵进行正交最小二乘估计。通过分析正交向量在模型中贡献的大小确定聚类规则的有效性，然后采用基于UD分解的最小二乘确定模糊模型的后件参数，实现模糊模型的结构和参数的优化。该方法已成功地应用于Box-Jenkins煤气炉的数据系统建模。     

基于Lukasiewicz t-模的模糊双向联想记忆网络的有效学习算法

利用t-模的伴随蕴涵算子，为基于Max和TL合成的模糊双向联想记忆网络Max-TLFBAM提供了一种新的学习算法，此处TL是Lukasiewicz t-模算子。从理论上严格证明了，只要存在有连接权矩阵对使得任意给定的模式对集成为Max-TLFBAM的平衡态集，则依该学习算法所确定的连接权矩阵对是所有这样的连接权矩阵对中的最大者。并用实验验证该学习算法的有效性。     

Block Cartesian abstraction of a geometric model and its application in hexahedral mesh generation

In this work, a fuzzy logic approach is proposed to transform a geometric model of arbitrary shape to its block Cartesian abstraction. This abstraction is topologically similar to the original model and it contains geometric sub-entities which are all aligned in the Cartesian directions. This is achieved by calculating the modifications made to the face normal vectors as a result of the influences of the adjacent faces. A fuzzy logic inference engine is developed by combining heuristics to emulate the local changes in face normal vectors with respect to the changes in the global space. A three-dimensional field morphing algorithm is used to position the features of this block Cartesian abstraction so that a congruent geometric model can be reconstructed. Such a model is useful for the generation of structured quadrilateral boundary element meshes or structured hexahedral meshes based on grid-based meshing method, mesh mapping or sweeping. This approach is also able to overcome the traditional problem of having poorly shaped elements at the boundary using the grid-based method of mesh generation. As the topology of the block Cartesian abstraction is congruent to the original model, the mesh can be mapped back to the original model by employing an inverse operation of the transformation.     

Handling forecasting problems based on fuzzy time series model and model error learning

Classical fuzzy time series forecasts are comprised of three steps: fuzzification, identification of fuzzy relation, and defuzzification. In this paper, we propose a new approach and add an error learning step to improve forecasts. In the fuzzification step, a hybrid method, based on the fuzzy c-means clustering and the fuzzy Silhouette criterion, is employed to determine the optimal number of intervals, which avoids time-consuming iterations of the whole algorithm. In the defuzzification step, an optimization model is set up to explain the rule of defuzzification. In the model structure, an error term is assembled into the traditional model to express model error, which is predicted by linear fitting and abnormal errors processing. Learning of model errors and considering of data characteristics guarantee good interpretability and accuracy. The numerical results show that the proposed approach has superior forecast performance to existing methods.