Community, Culture and Sustainability in Multilevel Dynamic Systems Intervention Science

This paper addresses intertwined issues in the conceptualization, implementation and evaluation of multilevel dynamic systems intervention science (MDSIS). Interventions are systematically planned, conducted and evaluated social science-based cultural products intercepting the lives of people and institutions in the context of multiple additional events and processes (which also may be referred to as interventions) that may speed, slow or reduce change towards a desired outcome. Multilevel interventions address change efforts at multiple social levels in the hope that effects at each level will forge synergistic links, facilitating movement toward desired change. This paper utilizes an ecological framework that identifies macro (policy and regulatory institutions), meso (organizations and agencies with resources, and power) and micro (individuals, families and friends living in communities) interacting directly and indirectly. An MDSIS approach hypothesizes that change toward a goal will occur faster and more effectively when synchronized and supported across levels in a social system. MDSIS approaches by definition involve “whole” communities and cannot be implemented without the establishments of working community partnerships This paper takes a dynamic systems approach to science as conducted in communities, and discusses four concepts that are central to MDSIS—science, community, culture, and sustainability. These concepts are important in community based participatory research and to the targeting, refinement, and adaptation of enduring interventions. Consistency in their meaning and use can promote forward movement in the field of MDSIS, and in community-based prevention science.     

Dynamic epistemic logic with branching temporal structures

van Bentham et al. (Merging frameworks for interaction: DEL and ETL, 2007) provides a framework for generating the models of Epistemic Temporal Logic (ETL: Fagin et al., Reasoning about knowledge, 1995; Parikh and Ramanujam, Journal of Logic, Language, and Information, 2003) from the models of Dynamic Epistemic Logic (DEL: Baltag et al., in: Gilboa (ed.) Tark 1998, 1998; Gerbrandy, Bisimulations on Planet Kripke, 1999). We consider the logic TDEL on the merged semantic framework, and its extension with the labeled past-operator “P _ϵ” (“The event ϵ has happened before which. . .”). To axiomatize the extension, we introduce a method for transforming a given model into a normal form in a suitable sense. These logics suggest further applications of DEL in the theory of agency, the theory of learning, etc.     

Keep ‘hoping’ for rationality: a solution to the backward induction paradox

We formalise a notion of dynamic rationality in terms of a logic of conditional beliefs on (doxastic) plausibility models. Similarly to other epistemic statements (e.g. negations of Moore sentences and of Muddy Children announcements), dynamic rationality changes its meaning after every act of learning, and it may become true after players learn it is false. Applying this to extensive games, we “simulate” the play of a game as a succession of dynamic updates of the original plausibility model: the epistemic situation when a given node is reached can be thought of as the result of a joint act of learning (via public announcements) that the node is reached. We then use the notion of “stable belief”, i.e. belief that is preserved during the play of the game, in order to give an epistemic condition for backward induction: rationality and common knowledge of stable belief in rationality. This condition is weaker than Aumann’s and compatible with the implicit assumptions (the “epistemic openness of the future”) underlying Stalnaker’s criticism of Aumann’s proof. The “dynamic” nature of our concept of rationality explains why our condition avoids the apparent circularity of the “backward induction paradox”: it is consistent to (continue to) believe in a player’s rationality after updating with his irrationality.     

Bridging learning theory and dynamic epistemic logic

This paper discusses the possibility of modelling inductive inference (Gold 1967) in dynamic epistemic logic (see e.g. van Ditmarsch et al. 2007). The general purpose is to propose a semantic basis for designing a modal logic for learning in the limit. First, we analyze a variety of epistemological notions involved in identification in the limit and match it with traditional epistemic and doxastic logic approaches. Then, we provide a comparison of learning by erasing (Lange et al. 1996) and iterated epistemic update (Baltag and Moss 2004) as analyzed in dynamic epistemic logic. We show that finite identification can be modelled in dynamic epistemic logic, and that the elimination process of learning by erasing can be seen as iterated belief-revision modelled in dynamic doxastic logic. Finally, we propose viewing hypothesis spaces as temporal frames and discuss possible advantages of that perspective.     

The right hemispheric dominance for face perception in preschool children depends on the visual discrimination level

The developmental origin of human adults’ right hemispheric dominance in response to face stimuli remains unclear, in particular because young infants’ right hemispheric advantage in face‐selective response is no longer present in preschool children, before written language acquisition. Here we used fast periodic visual stimulation (FPVS) with scalp electroencephalography (EEG) to test 52 preschool children (5.5 years old) at two different levels of face discrimination: discrimination of faces against objects, measuring face‐selectivity, or discrimination between individual faces. While the contrast between faces and nonface objects elicits strictly bilateral occipital responses in children, strengthening previous observations, discrimination of individual faces in the same children reveals a strong right hemispheric lateralization over the occipitotemporal cortex. Picture‐plane inversion of the face stimuli significantly decreases the individual discrimination response, although to a much smaller extent than in older children and adults tested with the same paradigm. However, there is only a nonsignificant trend for a decrease in right hemispheric lateralization with inversion. There is no relationship between the right hemispheric lateralization in individual face discrimination and preschool levels of readings abilities. The observed difference in the right hemispheric lateralization obtained in the same population of children with two different paradigms measuring neural responses to faces indicates that the level of visual discrimination is a key factor to consider when making inferences about the development of hemispheric lateralization of face perception in the human brain.     

高功能自闭症个体对威胁性情绪面孔的注意偏向

高功能自闭症(High-Functioning Autism, HFA)个体智力正常, 但也面临着严重的社会功能障碍。对威胁性情绪面孔的注意偏向与HFA个体社会功能的发展密切相关。梳理相关研究发现, HFA个体在自动加工阶段和情绪目标参与阶段, 不存在威胁性情绪面孔注意偏向; 而在任务与情绪无关的控制加工阶段, 存在威胁性情绪面孔注意偏向。针对HFA个体威胁性情绪面孔注意偏向的理论解释主要有杏仁核理论、强烈世界理论和执行功能理论等。神经生理机制方面, HFA个体对威胁性情绪面孔的注意偏向可能与其异常的皮下及皮层通路功能有关, 同时可能会受5-羟色胺系统基因及催产素水平等的影响。未来研究可在综合考虑研究方法及个体因素的基础上, 进一步探究其加工特征及神经生物机制, 着力开发科学有效的干预策略。     

计算机模拟动态心理测验

本文通过对心理测验的回顾与计算机模拟文献综合,把传统的纸笔测验与动态心理测验相比较,认为动态心理测验具有目标性、过程性与情境性三个特点,能够适应动态复杂的环境与群体作业任务,并且能够对阶段性的问题作出考察,具有传统的纸笔测验所不具备的优点.同时也提出,计算机模拟测验要更加注意外部效度与构思效度,注意计算机与人的交互作用的局限,才能够避免实验室实验的缺点,达到良好的模拟.     

A cognitive architecture for modeling emotion dynamics: Intensity estimation from physiological signals

Current approaches to emotion recognition do not address the fact that emotions are dynamic processes. This work concerns itself with the development of a cognitive architecture for modeling the dynamics of emotions with specific focus on a gray-box model for dynamic emotion intensity estimation that can incorporate findings from appraisal models, specifically Scherer’s Component Process Model. It is based on Dynamic Field Theory which allows the combination of theoretical knowledge with data-driven experimental approaches. A user study is conducted applying the proposed model to estimate intensity of negative emotions from physiological signals. Results show significant improvements of the proposed model to common methodology and baselines. The flexible cognitive architecture opens a wide field of experiments and directions to deepen the understanding of emotion processes as a whole.