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。     

A learning rule that explains how rewards teach attention

Many theories propose that top-down attentional signals control processing in sensory cortices by modulating neural activity. But who controls the controller? Here we investigate how a biologically plausible neural reinforcement learning scheme can create higher order representations and top-down attentional signals. The learning scheme trains neural networks using two factors that gate Hebbian plasticity: (1) an attentional feedback signal from the response-selection stage to earlier processing levels; and (2) a globally available neuromodulator that encodes the reward prediction error. We demonstrate how the neural network learns to direct attention to one of two coloured stimuli that are arranged in a rank-order. Like monkeys trained on this task, the network develops units that are tuned to the rank-order of the colours and it generalizes this newly learned rule to previously unseen colour combinations. These results provide new insight into how individuals can learn to control attention as a function of reward contingency.     

When Birds of a Feather Flock Together: The Role of Core-Self Evaluations and Moral Intensity in the Relationship Between Network Unethicality and Unethical Choice

Leveraging perspectives from social cognitive theory, the attention-based view, and social networks literatures, we tested the relationship between unethical choice and network unethicality, which we define as respondents’ perceptions of their peer advisors’ unethical choices. Although social cognitive theory predicts that perceptions of peer advisor unethical choice are positively associated with unethical choice, we theorize that the nature of this relationship depends on the personality of the actor (core self-evaluation) and the situation (moral intensity). Results from a lagged study suggest that individual and situational variables may act as key buffers to the adverse impact of unethical social influence on ethical choice. Strengths, limitations, and directions for future research are discussed.     

Contextualizing the Psychosocial Well‐being of Military Members and Their Partners: The Importance of Community and Relationship Provisions

Evidence of the impact of communities has been documented for a variety of individual and relational outcomes, including mental and physical health as well as the quality of romantic and parent–child relationships. The military represents a rather unique work context; in that, it is generally considered a lifestyle with a distinct culture and community. Yet, military families are also members of their broader, comprehensive community. Drawing from the social organizational theory of action and change (SOC) (Mancini & Bowen, 2013), and relationship provisions theory (Weiss, 1969) and utilizing a sample of 266 active duty military families, this study examined connectedness with the military community and the broader, comprehensive community. A dyadic model was evaluated whereby each partner's perspective of their comprehensive and military community was hypothesized to influence their own psychosocial well‐being as well as their partner's psychosocial well‐being. The role of relationship provisions (that is, having relationship needs met) as a mechanism linking community connections to psychosocial well‐being was also examined. Overall, the findings supported the hypothesized model, particularly for intra‐individual effects and military members. Findings emphasize the importance of considering what is gained from connections within a community rather than a focus solely on the connections themselves.     

Social Network Characteristics,Social Support,and Cigarette Smoking among Asian/Pacific Islander Young Adults

Cigarette smoking may be one of the factors contributing to the high levels of cancer‐related mortality experienced by certain Asian/Pacific Islander (A/PI) subgroups (e.g., Native Hawaiian). Given the collectivist cultural orientation attributed to A/PI groups, social strategies are recommended for substance abuse or smoking cessation treatment among A/PI. However, research examining how social network characteristics and social support relate to smoking across A/PI subgroups has been lacking. This study investigated the associations between social network characteristics (e.g., size, composition), perceived social support, and recent cigarette use across Native Hawaiian, Filipino, and East Asian (e.g., Japanese, Chinese) young adults (18–35 year old). Cross‐sectional, self‐report data were collected from N = 435 participants (M age = 25.6, SD = 8.3; 61% women). Ethnic differences were found in a number of pathways linking social network characteristics, perceived social support, and cigarette smoking. Larger network size was strongly associated with higher perceived social support and lower recent cigarette smoking among Native Hawaiians but not Filipinos or East Asians. Higher perceived social support was associated with lower recent smoking among East Asians and Filipinos but not Native Hawaiians. Implications are discussed with regard to smoking prevention and cessation among A/PI.     

Accuracy Motivations,Predispositions, and Social Information in Political Discussion Networks

This analysis studies how variation in individuals' motivation to form accurate judgments affects the process of political discussion. I use a small‐group experiment in which participants compete to elect the simulated candidate who best represents their true preferences. I manipulate economic incentives to control participants' accuracy motivations. The results show that accuracy‐motivated participants, compared to those with weaker accuracy goals, seek discussants with more expertise and a more diverse set of viewpoints, place greater emphasis on socially provided messages, and reduce emphasis on political predispositions. As a result of these differences, however, accuracy‐motivated participants rely more heavily on biased information. Hence, accuracy motivations do not produce more accurate judgments or better decisions. Although previous work on political discussion has largely ignored the role of motivations, these results suggest that accuracy motivations play an important but nuanced role in this process. Strengthened accuracy motivations increase individuals' exposure to political expertise and ideological diversity but also increase their potential to be misled.