Lexical organization and competition in first and second languages: computational and neural mechanisms

How does a child rapidly acquire and develop a structured mental organization for the vast number of words in the first years of life? How does a bilingual individual deal with the even more complicated task of learning and organizing two lexicons? It is only until recently have we started to examine the lexicon as a dynamical system with regard to its acquisition, representation, and organization. In this article, I outline a proposal based on our research that takes the dynamical approach to the lexicon, and I discuss how this proposal can be applied to account for lexical organization, structural representation, and competition within and between languages. In particular, I provide computational evidence based on the DevLex model, a self-organizing neural network model, and neuroimaging evidence based on functional magnetic resonance imaging (fMRI) studies, to illustrate how children and adults learn and represent the lexicon in their first and second languages. In the computational research, our goal has been to identify, through linguistically and developmentally realistic models, detailed cognitive mechanisms underlying the dynamic self-organizing processes in monolingual and bilingual lexical development; in the neuroimaging research, our goal has been to identify the neural substrates that subserve lexical organization and competition in the monolingual and the bilingual brain. In both cases, our findings lead to a better understanding of the interactive dynamics involved in the acquisition and representation of one or multiple languages.     

Why don’t well-educated adults understand accumulation? A challenge to researchers,educators, and citizens

Accumulation is a fundamental process in dynamic systems: inventory accumulates production less shipments; the national debt accumulates the federal deficit. Effective decision making in such systems requires an understanding of the relationship between stocks and the flows that alter them. However, highly educated people are often unable to infer the behavior of simple stock–flow systems. In a series of experiments we demonstrate that poor understanding of accumulation, termed stock–flow failure, is a fundamental reasoning error. Persistent poor performance is not attributable to an inability to interpret graphs, lack of contextual knowledge, motivation, or cognitive capacity. Rather, stock–flow failure is a robust phenomenon that appears to be rooted in failure to appreciate the most basic principles of accumulation, leading to the use of inappropriate heuristics. We show that many people, including highly educated individuals with strong technical training, use what we term the “correlation heuristic”, erroneously assuming that the behavior of a stock matches the pattern of its flows. We discuss the origins of stock–flow failure and implications for management and education.     

Object correspondence across brief occlusion is established on the basis of both spatiotemporal and surface feature cues

The correspondence problem is a classic issue in vision and cognition. Frequent perceptual disruptions, such as saccades and brief occlusion, create gaps in perceptual input. How does the visual system establish correspondence between objects visible before and after the disruption? Current theories hold that object correspondence is established solely on the basis of an object’s spatiotemporal properties and that an object’s surface feature properties (such as color or shape) are not consulted in correspondence operations. In five experiments, we tested the relative contributions of spatiotemporal and surface feature properties to establishing object correspondence across brief occlusion. Correspondence operations were strongly influenced both by the consistency of an object’s spatiotemporal properties across occlusion and by the consistency of an object’s surface feature properties across occlusion. These data argue against the claim that spatiotemporal cues dominate the computation of object correspondence. Instead, the visual system consults multiple sources of relevant information to establish continuity across perceptual disruption.     

Sort out your neighbourhood

Axelrod (The evolution of cooperation, 1984) and others explain how cooperation can emerge in repeated 2-person prisoner’s dilemmas. But in public good games with anonymous contributions, we expect a breakdown of cooperation because direct reciprocity fails. However, if agents are situated in a social network determining which agents interact, and if they can influence the network, then cooperation can be a viable strategy. Social networks are modelled as graphs. Agents play public good games with their neighbours. After each game, they can terminate connections to others, and new connections are created. Cooperative agents do well because they manage to cluster with cooperators and avoid defectors. Computer simulations demonstrate that group formation and exclusion are powerful mechanisms to promote cooperation in dilemma situations. This explains why social dilemmas can often be solved if agents can choose with whom they interact.     

Information–movement coupling in developing cricketers under changing ecological practice constraints

Changing informational constraints of practice, such as when using ball projection machines, has been shown to significantly affect movement coordination of skilled cricketers. To date, there has been no similar research on movement responses of developing batters, an important issue since ball projection machines are used heavily in cricket development programmes. Timing and coordination of young cricketers (n = 12, age = 15.6 ± 0.7 years) were analyzed during the forward defensive and forward drive strokes when facing a bowling machine and bowler (both with a delivery velocity of 28.14 ± 0.56 m s⁻¹). Significant group performance differences were observed between the practice task constraints, with earlier initiation of the backswing, front foot movement, downswing, and front foot placement when facing the bowler compared to the bowling machine. Peak height of the backswing was higher when facing the bowler, along with a significantly larger step length. Altering the informational constraints of practice caused major changes to the information–movement couplings of developing cricketers. Data from this study were interpreted to emanate from differences in available specifying variables under the distinct practice task constraints. Considered with previous findings, results confirmed the need to ensure representative batting task constraints in practice, cautioning against an over-reliance on ball projection machines in cricket development programmes.     

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.