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。     

Dispositional implementation solves the superfluous structure problem

Consciousness supervenes on activity; computation supervenes on structure. Because of this, some argue, conscious states cannot supervene on computational ones. If true, this would present serious difficulties for computationalist analyses of consciousness (or, indeed, of any domain with properties that supervene on actual activity). I argue that the computationalist can avoid the Superfluous Structure Problem (SSP) by moving to a dispositional theory of implementation. On a dispositional theory, the activity of computation depends entirely on changes in the intrinsic properties of implementing material. As extraneous structure is not required for computation, a system can implement a program running on some but not all possible inputs. Dispositional computationalism thus permits episodes of computational activity that correspond to potential episodes of conscious awareness. The SSP cannot be motivated against this account, and so computationalism may be preserved.     

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.     

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.     

Enhanced proliferation of progenitor cells following long-term potentiation induction in the rat dentate gyrus

The dentate gyrus (DG) is among the few areas in the mammalian brain where production of new neurons continues in the adulthood. Although its functional significance is not completely understood, several lines of evidence suggest the role of DG neurogenesis in learning and memory. Considering that long-term potentiation (LTP) is a prime candidate for the process underlying hippocampal learning and memory, these results raise the possibility that LTP and neurogenesis are closely related. Here, we investigated whether or not LTP induction in the afferent pathway triggers enhanced proliferation of progenitor cells in the DG. LTP was induced by tetanic stimulation in perforant path-DG synapses in one hemisphere, and the number of newly generated progenitor (BrdU-labeled) cells in the DG was quantified. Compared with the control hemisphere (stimulated with low-frequency pulses), the LTP-induced hemisphere contained a significantly higher number of newly generated progenitor cells in the dorsal as well as ventral DG. When CPP, an NMDA receptor antagonist, was administered, tetanic stimulation neither induced LTP nor enhanced progenitor cell proliferation, indicating that NMDA receptor activation, rather than tetanic stimulation per se, is responsible for enhanced progenitor proliferation in the control animal. Our results show that tetanic stimulation of perforant path sufficient to induce LTP increases progenitor proliferation in adult DG in an NMDA receptor-dependent manner.     

A small look at the ear recognition process using a hybrid approach

The purpose of this document is to offer a combined approach in biometric analysis field, integrating some of the most known techniques using ears to recognize people. This study uses Hausdorff distance as a pre-processing stage adding sturdiness to increase the performance filtering for the subjects to use it in the testing process. Also includes the Image Ray Transform (IRT) and the Haar based classifier for the detection step. Then, the system computes Speeded Up Robust Features (SURF) and Linear Discriminant Analysis (LDA) as an input of two neural networks to recognize a person by the patterns of its ear. To show the applied theory experimental results, the above algorithms have been implemented using Microsoft C#. The investigation results showed robustness improving the ear recognition process.