The Social Networks of Women Experiencing Domestic Violence

The research literature has demonstrated that battered women living in shelters experience impaired social support. This study examines this phenomenon among battered women living in the community. This study compared a group of pregnant battered women (n = 145) and a group of pregnant nonbattered women (n = 58) in terms of their structural [e.g., total number of supporters, network members in violent relationships with their partners (an index of homophily or nonhomophily) and functional (e.g., emotional)] social support. Four reasons for impaired support in battered women were proposed and examined, including social isolation, failure to disclose abuse, homophily, and low SES. Only homophily was related to impaired support among battered women. In addition, the relationship between structural and functional support and mental health outcomes (e.g., depression, self-esteem) was examined. Criticism, practical support, homophily, and disclosure were all significant predictors of mental health for battered women. Implications for community-based interventions are discussed in the context of current intervention strategies with battered women.     

The mind beyond our immediate awareness: Freudian, Jungian, and cognitive models of the unconscious

Abstract: Several branches of cognitive science now focus on the nature of the unconscious. This paper explores some of the findings and models from this research. By introducing formulations based on non‐clinical data, the cognitive scientists—in neural linguistics, computational modelling, and neuroscience—may depart from the older psychoanalytic formulations. An understanding of unconscious neural processes is nevertheless emerging showing how synapses are modified by experience and how learning, conscious and unconscious, is due to this important aspect of brain plasticity. Freud and Jung's formulations about the unconscious psyche, representing the main tenets of depth psychology, are also based on a conception of the mind as extending beyond immediate awareness. However, their models are more hypothetical in that their data, almost exclusively, come from treatments of psychotherapy patients and their verbal accounts. So how do these two conceptions of the unconscious match, where do they differ? And how does the neural understanding in the present research support theories and practices of analytic treatments?     

Modeling recognition memory using the similarity structure of natural input

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images).     

Dissociable parietal systems for primacy and subsequent memory effects

The frequently observed superior memory for the first items on a list is referred to as primacy. The aetiology of this effect in terms of cognitive processes and their neural substrate is subject to an ongoing debate. However, the brain areas generally involved in successful encoding are well described by subsequent memory studies in which activity during encoding is correlated with memory performance. We employed an object-location association paradigm to differentiate the neural correlate of the primacy from the subsequent memory effect. Activity in the intraparietal sulcus predicted memory performance across all encoding positions. Increased activity in the inferior parietal lobe and angular gyrus resulted exclusively in a more efficient encoding of the first item presented. These areas are part of the ventral frontoparietal network involved in stimulus driven attention. Our results implicate the relatively elevated attention to the first item probably due to its contextual distinctiveness, as a major contributor to the primacy effect.     

On the consistency problem for the calculus

In this paper, we further investigate the consistency problem for the qualitative temporal calculus introduced by Pujari et al. [A.K. Pujari, G.V. Kumari, A. Sattar, INDU: An interval and duration network, in: Australian Joint Conference on Artificial Intelligence, 1999, pp. 291–303]. We prove the intractability of the consistency problem for the subset of pre-convex relations, and the tractability of strongly pre-convex relations. Furthermore, we also define another interesting set of relations for which the consistency problem can be decided by the -closure method, a method similar to the usual path-consistency method. Finally, we prove that the -closure method is also complete for the set of atomic relations of implying that the intervals have the same duration.     

Semantic interpretation as computation in nonmonotonic logic: the real meaning of the suppression task

Interpretation is the process whereby a hearer reasons to an interpretation of a speaker's discourse. The hearer normally adopts a credulous attitude to the discourse, at least for the purposes of interpreting it. That is to say the hearer tries to accommodate the truth of all the speaker's utterances in deriving an intended model. We present a nonmonotonic logical model of this process which defines unique minimal preferred models and efficiently simulates a kind of closed-world reasoning of particular interest for human cognition. Byrne's "suppression" data (Byrne, 1989) are used to illustrate how variants on this logic can capture and motivate subtly different interpretative stances which different subjects adopt, thus indicating where more fine-grained empirical data are required to understand what subjects are doing in this task. We then show that this logical competence model can be implemented in spreading activation network models. A one pass process interprets the textual input by constructing a network which then computes minimal preferred models for (3-valued) valuations of the set of propositions of the text. The neural implementation distinguishes easy forward reasoning from more complex backward reasoning in a way that may be useful in explaining directionality in human reasoning.     

Uncovering the richness of the stimulus: structure dependence and indirect statistical evidence

The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses of child-directed speech showing that there is indirect statistical information useful for correct auxiliary fronting in polar interrogatives and that such information is sufficient for distinguishing between grammatical and ungrammatical generalizations, even in the absence of direct evidence. We further show that there are simple learning devices, such as neural networks, capable of exploiting such statistical cues, producing a bias toward correct AUX questions when compared to their ungrammatical counterparts. The results suggest that the basic assumptions of the poverty of stimulus argument may need to be reappraised.     

The large-scale structure of semantic networks: statistical analyses and a model of semantic growth

We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.     

蛋白质组学与神经科学:从蛋白质到网络

多蛋白质信号复合物在所有细胞中都很重要,它们在脑内信息处理也有着重要作用。质谱、双向凝胶电泳、蛋白亲和技术、高通量的免疫组化方法和蛋白质组信息学都蛋白质组学研究的主要手段。应用蛋白质组学方法可以研究多蛋白信号复合的功能,多样性与组成的动态变化。因此,蛋白质组学发展使得人们从单个蛋白质的研究上升到对蛋白质网络的研究。蛋白质组学研究将为神经与精神疾病的诊断技术与治疗手段提供一条新的道路。     

拖延行为的认知神经模型及干预

拖延是一种普遍存在, 具有跨时间和跨情景稳定性的问题行为, 它会危害到人们的学习、工作和身心健康。然而目前拖延行为的认知神经机制仍不清晰, 且缺乏因果证据, 本项目拟从拖延的时间决策模型和三重神经结构网络模型出发, 构建拖延的认知神经模型, 并利用认知干预和神经调控技术, 检验和完善拖延行为的认知神经模型, 进而试图制定拖延的精准化干预方案。本项目分为3部分：(1)从记录与关联研究的视角出发, 利用多模态神经影像方法系统考察拖延行为的认知神经机制; (2)从因果/近因果研究视角出发, 利用认知干预和神经调控技术, 验证并完善拖延的认知神经模型; (3)从临床应用的视角出发, 建立拖延行为障碍的临床筛查-诊断体系, 并制定精准化治疗方案。本项目的开展对于探明拖延产生的核心认知神经机制具有十分重要的理论贡献, 同时对于拖延行为的有效预防和精准治疗具有重要的现实意义。