Individual differences in ERPs during mental rotation of characters: Lateralization,and performance level

The cognitive process of imaging an object turning around is called mental rotation. Many studies have been put forward analyzing mental rotation by means of event-related potentials (ERPs). Event-related potentials (ERPs) were measured during mental rotation of characters in a sample (N = 82) with a sufficient size to obtain even small effects. A bilateral ERP amplitude modulation as a function of angular displacement was observed at parietal leads without any lateralization. Sex had no effect on mental rotation of characters, neither with respect to performance nor with respect to brain potentials. When the sample was split into groups of high- and low-performers, however, the results indicated (a) in line with the idea of neural efficiency substantially larger amplitudes for low-performers; (b) that the smaller amplitudes of the high-performers involved larger parietal networks; and (c) a left-parietal disengagement of neural activity for high-performers but a right-parietal disengagement for low-performers. The latter finding became evident through an analysis of internal consistencies of ERP amplitudes across conditions and performance levels, showing that internal consistencies were reduced at all three leads in the high-performance group for the biggest rotation angle, relative the other conditions and the low-performing group.     

Investigation of psycho-physiological interactions between patient and therapist during a psychodynamic therapy and their relation to speech using in terms of entropy analysis using a neural network approach

In the present contribution we investigate in an exemplary single-case study the behavior of psycho-physiological variables in psychotherapy sessions. The values are measured continously during a single session at the same time for both patient and therapist. The analysis of the data is done using an artificial neural network approach for non-linear principal component analysis and faithful data representation/visualization and compression required for subsequent process analysis. The used network (growing self-organizing map, GSOM) thereby uses a kernel smoothing for improved data density estimation. In this way, we are able to generate an entropy model of psycho-physiological variability detecting emotionally instable phases during the therapy process. We relate our finding to results obtained by speech analysis of the therapy sessions according to the cycle model invented by Mergenthaler. Thus, we get preliminary suggestions how psycho-physiological reactions are related to the therapeutic process.     

情绪识别研究中被“冷落”的线索:躯体表情加工的特点、神经基础及加工机制

躯体和面孔是个体情绪表达与识别的重要线索。与面部表情相比,躯体表情加工的显著特点是补偿情绪信息,感知运动与行为信息,及产生适应性行为。情绪躯体与面孔加工的神经基础可能相邻或部分重合,但也存在分离;EBA、FBA、SPL、IPL等是与躯体表情加工相关的特异性脑区。今后应系统研究面孔、躯体及语音情绪线索加工潜在的神经基础,探讨躯体情绪加工的跨文化差异,考察情绪障碍患者的躯体表情加工特点。     

为什么会出现“舌尖现象”?——TOT现象发生机制之争

舌尖现象(tip-of-the-tongue,TOT)是一种普遍发生的现象,但是发生TOT的对象、情景、机制等却是特殊和复杂的。因此,它引起了研究者的关注,尤其是在其产生机制上更是存在着很多争论。国外研究者提出了部分激活理论、传递缺陷理论、障碍物假说、神经网络模型、元认知理论等。但研究者大多都基于单一条件或情景来探讨TOT的产生,因此提出的理论的使用范围,解释广度等均有局限。关于TOT依然有许多疑惑,同时也存在值得探讨的课题。     

Criteria for the design and evaluation of cognitive architectures

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation.     

Extending Dynamical Systems Theory to Model Embodied Cognition

We define a mathematical formalism based on the concept of an ‘‘open dynamical system” and show how it can be used to model embodied cognition. This formalism extends classical dynamical systems theory by distinguishing a ‘‘total system’’ (which models an agent in an environment) and an ‘‘agent system’’ (which models an agent by itself), and it includes tools for analyzing the collections of overlapping paths that occur in an embedded agent's state space. To illustrate the way this formalism can be applied, several neural network models are embedded in a simple model environment. Such phenomena as masking, perceptual ambiguity, and priming are then observed. We also use this formalism to reinterpret examples from the embodiment literature, arguing that it provides for a more thorough analysis of the relevant phenomena.     

Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images

Alzheimer’s disease, the most common form of dementia is a neurodegenerative brain order that has currently no cure for it. Hence, early diagnosis of such disease using computer-aided systems is a subject of great importance and extensive research amongst researchers. Nowadays, deep learning or particularly convolutional neural network (CNN) is getting more attention due to its state-of-the-art performances in variety of computer vision tasks such as visual object classification, detection and segmentation. Several recent studies, that have used brain MRI scans and deep learning have shown promising results for diagnosis of Alzheimer’s disease. However, most common issue with deep learning architectures such as CNN is that they require large amount of data for training. In this paper, a mathematical model P_FS_EC_TL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task. Experimentation is performed on data collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The accuracy of the 3-way classification using the described method is 95.73% for the validation set.     

Learning representations in a gated prefrontal cortex model of dynamic task switching

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients.     

Hemispheric asymmetries in the split-fovea model of semantic processing

We report a series of neural network models of semantic processing of single English words in the left and the right hemispheres of the brain. We implement the foveal splitting of the visual field and assess the influence of this splitting on a mapping from orthography to semantic representations in single word reading. The models were trained on English four-letter words, presented according to their frequency in all positions encountered during normal reading. The architecture of the model interacted with the training set to produce processing asymmetries comparable to those found in behavioral studies. First, the cueing effects of dominant and subordinate meanings of ambiguous words were different for words presented to the left or to the right of the input layer. Second, priming effects of groups of related words were stronger in the left input than the right input of the model. These effects were caused by coarser-coding in the right half compared with the left half of the model, an emergent effect of the split model interacting with informational asymmetries in the left and right parts of words in the lexicon of English. Some or all of the behavioral data for reading single words in English may have a similar origin.     

Exploring the effects of EEG signals on collision cases happening in the process of young drivers’ braking

Detecting mental states in drivers offers an opportunity to reduce accidents by triggering alerts and signaling the need for rest or renewed focus. Here we used electroencephalography (EEG) to measure brain signals in young drivers operating a driving simulator to detect mental states and predict accidents. We measured reaction times to unexpected hazardous events and correlated them with EEG signals measured from the frontal, parietal, and temporal cortices as well as the central sulcus (corresponding to motor cortex). We found that EEG signals in the relative beta (power in beta (13–30 Hz) relative to total power of the EEG (0.5–30 Hz)), alpha/delta, alpha/theta, beta/delta, beta/theta frequency bands were higher for collisions than successful collision avoidance, and that the key decision-making period is the 2nd second before braking. Importantly, a decision tree classifier trained on these neural signals predicted collision avoidance outcomes. Then based on random forest model, we extracted three critical neural signals (beta/delta_frontal, relative beta_parietal and relative beta_central Sulcus) to classify collision avoidance outcomes. Our findings suggest measuring EEG during driving may provide useful signals for enhancing driver safety.