An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal

Learning about me and you : Only deterministic stimulus associations elicit self-prioritization

Open Access via the Elsevier agreement Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Peer reviewedPublisher PD

Bayesian participatory-based decision analysis : an evolutionary, adaptive formalism for integrated analysis of complex challenges to social-ecological system sustainability

Includes bibliographical references (pages. 379-400).This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Dynamic Agent Based Modeling Using Bayesian Framework for Addressing Intelligence Adaptive Nuclear Nonproliferation Analysis

Realistically, no two nuclear proliferating or defensive entities are exactly identical; Agent Based Modeling (ABM) is a computational methodology addressing the uniqueness of those facilitating or preventing nuclear proliferation. The modular Bayesian ABM Nonproliferation Enterprise (BANE) tool has been developed at Texas A &M University for nuclear nonproliferation analysis. Entities engaged in nuclear proliferation cover a range of activities and fall within proliferating, defensive, and neutral agent classes. In BANE proliferating agents pursue nuclear weapons, or at least a latent nuclear weapons capability. Defensive nonproliferation agents seek to uncover, hinder, reverse, or dismantle any proliferation networks they discover. The vast majority of agents are neutral agents, of which only a small subset can significantly enable proliferation. BANE facilitates intelligent agent actions by employing entropy and mutual information for proliferation pathway determinations. Factors including technical success, resource expenditures, and detection probabilities are assessed by agents seeking optimal proliferation postures. Coupling ABM with Bayesian analysis is powerful from an omniscience limitation perspective. Bayesian analysis supports linking crucial knowledge and technology requirements into relationship networks for each proliferation category. With a Bayesian network, gaining information on proliferator actions in one category informs defensive agents where to expend limited counter-proliferation impeding capabilities. Correlating incomplete evidence for pattern recognition in BANE using Bayesian inference draws upon technical supply side proliferation linkages grounded in physics. Potential or current proliferator security, economic trajectory, or other factors modify demand drivers for undertaking proliferation. Using Bayesian inference the coupled demand and supply proliferation drivers are connected to create feedback interactions. Verification and some validation for BANE is performed using scenarios and historical case studies. Restrictive export controls, swings in global soft power affinity, and past proliferation program assessments for entities ranging from the Soviet Union to Iraq demonstrates BANE’s flexibility and applicability. As a newly developed tool, BANE has room for future contributions from computer science, engineering, and social scientists. Through BANE the framework exists for detailed nonproliferation expansion into broader weapons of mass effect analysis; since, nuclear proliferation is but one option for addressing international security concerns

Constructions emerging : a usage-based model of the acquisition of grammar

This dissertation is concerned with the development of grammar. Starting from a usage-based perspective, which holds that children use domain-general learning mechanisms to acquire the grammatical patterns of their mother tongue, Beekhuizen shows how to operationalize various concepts from this tradition in a computational model. In order to arrive at a sound set of assumptions, Beekhuizen compares and criticizes various earlier usage-based modeling approaches and scrutinizes the concepts of a usage-based theory of language acquisition from the perspective of a computational modeler. As the model should be able to produce utterances on the basis of a meaning to be expressed, as well as to interpret utterances, the availability of meaning from the situational context is studied empirically. The resulting model, the Syntagmatic-Paradigmatic Learner, simulates an increasing ability to understand utterances on the basis of a grammar of constructions, as well as to produce utterances on the basis of this grammar. Several developmental effects are simulated and the internal states of the model are carefully examined.NWO (grant 322.70.001)Language Use in Past and Presen

IFPOC Symposium:Discovering antecedents and consequences of complex change recipients' reactions to organizational change.

IFPOC symposium: Discovering antecedents and consequences of complex change recipients' reactions to organizational change Chairs: Maria Vakola (Athens University of Economics and Business) & Karen Van Dam (Open University) Discussant: Mel Fugate (American University, Washington, D.C) State of the art Organisations are required to continuously change and develop but there is a high failure rate associated with change implementation success. In the past two decades, change researchers have started to investigate change recipients' reactions to change recognizing the crucial role of these reactions for successful change. This symposium aims at identifying and discussing the complex processes that underlie the relationships among antecedents, reactions and outcomes associated with organizational change. New perspective / contributions This symposium consists of five studies that extend our knowledge in the field by (i) providing an analysis of change recipients' reactions going beyond the dichotomous approaches (acceptance or resistance) (ii) revealing understudied antecedents-reactions and reactions-consequences patterns and relationships (iii) shedding light on the role of contextual factors i.e team climate and individual factors i.e emotion regulation on the adaptation to change. This symposium is based on a combination of both quantitative (i.e diary, survey) and qualitative (i.e interviews) research methodology. Research / practical implications This symposium aims to increase our understanding of the complex processes associated with change recipients' reactions to change. Discovering how these reactions are created and what are their results may reveal important contingencies that can explain how positive organizational outcomes during times of change can be stimulated which is beneficial for both researchers and practitioners

Qigong at Work: Where East Meets West

State of the Art Qigong is part of Traditional Chinese Medicine (TMC) and aims to balance body and mind. The roots of Qigong lie in China, where Confucian and Daoist scholars (500-400 B.C.) noted that one must learn to balance and relax one’s thoughts and emotions to avoid illness (Yang, 1997). In the Western world, Qigong is best known for its slow and coordinated movements. According to TCM, these movements will help regulate one’s ‘qi’, or life energy, through the body to improve the health and harmony of mind and body. During the practice of Qigong, one’s breathing, attention and movement are aligned. As such, Qigong is sometimes considered Mindfulness in movement (although there are many important differences). New perspectives / contributionsQigong has many positive outcomes on health and wellness. In both patient groups and healthy individuals, Qigong has been shown to improve psychological well-being, quality of life, immune function, balance and related risk-factors, and bone density (Jahnke et al., 2010). As such, Qigong offers a validated way to reduce the physical and mental activation that results from a person's work. While recent research has demonstrated how important it is to take short breaks at work, there is still little attention for Qigong exercises as a means to recover at work.Practical Implications In this presentation, we will explain and practice several Qigong exercises. These movements can be used at work (and at home) to recover from (hormonal) activation, ‘empty’ one’s head, and restore the body-mind balance. <br/