Disentangling the neural mechanisms involved in Hinduism- and Buddhism-related meditations

The most diffuse forms of meditation derive from Hinduism and Buddhism spiritual traditions. Different cognitive processes are set in place to reach these meditation states. According to an historical-philological hypothesis (Wynne, 2009) the two forms of meditation could be disentangled. While mindfulness is the focus of Buddhist meditation reached by focusing sustained attention on the body, on breathing and on the content of the thoughts, reaching an ineffable state of nothigness accompanied by a loss of sense of self and duality (Samadhi) is the main focus of Hinduism-inspired meditation. It is possible that these different practices activate separate brain networks. We tested this hypothesis by conducting an activation likelihood estimation (ALE) meta-analysis of functional magnetic resonance imaging (fMRI) studies. The network related to Buddhism-inspired meditation (16 experiments, 263 subjects, and 96 activation foci) included activations in some frontal lobe structures associated with executive attention, possibly confirming the fundamental role of mindfulness shared by many Buddhist meditations. By contrast, the network related to Hinduism-inspired meditation (8 experiments, 54 activation foci and 66 subjects) triggered a left lateralized network of areas including the postcentral gyrus, the superior parietal lobe, the hippocampus and the right middle cingulate cortex. The dissociation between anterior and posterior networks support the notion that different meditation styles and traditions are characterized by different patterns of neural activation.     

Singular Clues to Causality and Their Use in Human Causal Judgment

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities.     

SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.     

应用分子靶向治疗的时机选择

分子靶向药物的出现显示出肿瘤治疗传统模式的重要进展。与传统放疗、化疗相比,分子靶向治疗因其特异性高、不良反应轻微,在恶性肿瘤个体化治疗中扮演重要角色。十五年来,分子靶向药物的出现,给肿瘤的治疗模式带来重大改变,也给临床医生带来新的挑战。准确把握分子靶向药物应用时机,获得最大临床效果,已成为肿瘤治疗领域关注热点。本文就非小细胞肺癌、消化系统肿瘤、乳腺癌、肾癌治疗中分子靶向药物如何选择应用时机进行阐述。     

医学影像学见习应用PACS教学法与传统读片教学法的对比研究

将中南大学湘雅医学院2010级临床医学五年制197名学生随机分成两组,在医学影像学见习教学过程中分别应用影像存储及传输系统（PACS）教学法（98人）及传统读片教学法（99人）,学期结束时对两组学生考试成绩进行分析,并对PACS组学生进行问卷调查及结果分析.统计数据显示,PACS组学生读片测验及卷面考试中,病例分析题成绩显著高于传统读片组,问卷调查显示PACS组学生大多数认可该教学方法.因此,应用PACS教学有助于提高学生的临床分析能力及主动学习的能力,是学生欢迎的教学方式.但对医学影像学基本知识的传授,应用PACS法较传统读片法并无明显优势.     

人工肝支持系统治疗肝衰竭的研究进展

目前肝衰竭发病率和病死率仍然较高,肝衰竭成为威胁患者生命的严重疾病。基于肝脏具有强大再生能力,人工肝支持系统作为一种“过渡”措施,为肝衰竭患者的肝功能恢复赢得时间。人工肝支持系统根据性质不同分为非生物型人工肝、生物型人工肝和混合型人工肝。目前非生物型人工肝已在肝病治疗领域被广泛应用,但生物型及混合型人工肝的临床应用还存在很多问题需要进一步研究和解决。因此本文对人工肝的作用、临床应用现状、存在问题及研究进展进行综述。     

The development of causal categorization

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

Effects of self–other decision-making on time-based intertemporal choice

The effects of self–other decision-making on intertemporal choice have been revealed in many studies using a monetary outcome. However, the outcome of intertemporal choice is not restricted to money; time is also a scarce and nonrenewable resource outcome. Thus, we conducted a series of experiments to address the effects of self–other decision-making on time-based intertemporal choice, a type of intertemporal choice that uses time as an outcome. Over the course of three experiments, differences in self–other decision-making were evidenced. Participants who made decisions for others were more likely to prefer the smaller but sooner (SS) option over the larger but later (LL) option and considered the gain of the SS option to be significantly greater than that of the LL option. Participants who made decisions for themselves were likely to prefer the LL option over the SS option. However, they considered the gains of the LL and the SS option to be indifferent. Changing the role of decision-making could affect the ability of individuals to consider the future consequences of their decisions. The effects of self–other decision-making on time-based intertemporal choice could be explained by the accounts of economic reasoning and construal level theory. The findings indicated that the effects of self–other decision-making on time-based intertemporal choice, which could be generated simply by rewording questions, can help individuals make optimal long-term choices without the need for increased control.