Positive explorers: modeling dynamic control in normal aging

Situations in which there are multiple changes occurring all at once and which demand complex decisions to be made are common throughout life, but little is known about how normal aging influences performance on these types of scenarios. To determine performance differences associated with normal aging, we test older and younger adults in a dynamic control task. The task involves the control of a single output variable over time via multiple and uncertain input controls. The Single Limited Input, Dynamic Exploratory Responses (SLIDER) computational model, is implemented to determine the behavioral characteristics associated with normal aging in a dynamic control task. Model-based analysis demonstrates a unique performance signature profile associated with normal aging. Specifically, older adults exhibit a positivity effect in which they are more influenced by positively valenced feedback, congruent with previous research, as well as enhanced exploratory behavior.     

Age and schooling effects on early literacy and phoneme awareness

Previous research on age and schooling effects is largely restricted to studies of children who begin formal schooling at 6 years of age, and the measures of phoneme awareness used have typically lacked sensitivity for beginning readers. Our study addresses these issues by testing 4 to 6 year-olds (first 2 years of formal schooling in the United Kingdom) on a sensitive dynamic measure of phoneme awareness and tests of early literacy. There were significant effects of both age and schooling on the dynamic measure of phoneme awareness, word reading, spelling, and letter name knowledge, but there were no significant Age × Time interactions. This indicates that older children within this age group generally outperform their younger classmates (although they do not make faster progress) and that this advantage is developed prior to the start of school.     

Beyond IQ: A latent state-trait analysis of general intelligence, dynamic decision making, and implicit learning

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed two standard intelligence tests, two dynamic decision making tasks, and two implicit learning tasks at two measurement occasions each. We used structural equation models to test latent state-trait measurement models and the relation between constructs. The results suggest that dynamic decision making and implicit learning are substantially related with general intelligence. Furthermore, general intelligence is the best predictor for income, social status, and educational attainment. Dynamic decision making can predict supervisor ratings even beyond general intelligence.     

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis

Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.     

Decision support for real-time, dynamic decision-making tasks

By definition, dynamic decision making dictates that multiple and interrelated decisions be made in a continuously changing environment. Such decision making is difficult and often taxes individuals’ cognitive resources. Here I investigated ways in which to support decision making in these environments. I evaluated three forms of decision support: outcome feedback, cognitive feedback, and feedforward that incorporated (to varying degrees) common features of learning theories associated with dynamic tasks. Participants in a laboratory experiment performed a real-time, dynamic decision-making task while receiving one of the different types of decision support. During the first 2 days, individuals received one type of decision support, but on the third day they performed the task without this support. Participants who received feedforward improved their performance considerably and continued to exhibit improved performance even after discontinuation of the decision support on the third day. Neither outcome feedback nor cognitive feedback resulted in improved performance. More research is necessary to conclusively identify the best forms of dynamic decision-making support and their durability when transferred to new tasks.     

Understanding dynamic and static displays: using images to reason dynamically

We examined expert meteorologists as they created a weather forecast while working in a naturalistic environment. We examined the type of external representation they chose to examine (a static image, a sequence of static images, or a dynamic display) and the kind of information they extracted from those representations (static or dynamic). We found that even though weather is an extremely dynamic domain, expert meteorologists examined very few animations, examining primarily static images. However, meteorologists did extract large amounts of dynamic information from these static images, suggesting that they reasoned about the weather by mentally animating the static images rather than letting the software do it for them.     

Deductive databases for computing certain and consistent answers from mediated data integration systems

We address the problem of retrieving certain and consistent answers to queries posed to a mediated data integration system under the local-as-view paradigm with open sources and conjunctive and disjunctive view definitions. For obtaining certain answers a query program is run under the cautious stable model semantics on top of a normal deductive database with choice operator that specifies the class of minimal legal instances of the integration system. This methodology works for all monotone Datalog queries. To compute answers to queries that are consistent with respect to given global integrity constraints, the specification of minimal legal instances is combined with another disjunctive deductive database that specifies the repairs of those legal instances. This allows to retrieve the answers to any Datalog^¬ query that are consistent with respect to global universal and referential integrity constraints.     

Logic-based XML data integration: a semi-materializing approach

We first describe the approach to XML data integration in the XPathLog/LoPiX project that uses a warehouse strategy. We show that the DOM model and the XML Query Data Model are not suitable for this task since the integrated database is not necessarily a tree, but a set of overlapping (original and integrated) trees. The problem is solved by using a node-labeled graph-based data model, called XTreeGraph, for the internal XML database that represents multiple, overlapping XML trees, or tree views.In the second part, we return to the standard XML data model—by still keeping the overlapping tree idea by “simulating” it: The data is internally represented by XML where the “overlayed” resulting tree is represented by XLink elements that refer to the original sources. By using a logical, transparent data model for XLinks as investigated in [May, in: 11th WWW Conference, 2002], all queries behave as stated against the XTreeGraph. The use of links for partial materialization also turns the approach from a warehouse approach into a mixed approach that combines the advantages of the warehouse approach and of the virtual approach. The approach is again illustrated by using XPathLog as data integration language.     

Cut Elimination for S4C: A Case Study

S4C is a logic of continuous transformations of a topological space. Cut elimination for it requires new kind of rules and new kinds of reductions     

Second-Order Characterizable Cardinals and Ordinals

The notions of finite and infinite second-order characterizability of cardinal and ordinal numbers are developed. Several known results for the case of finite characterizability are extended to infinite characterizability, and investigations of the second-order theory of ordinals lead to some observations about the Fraenkel-Carnap question for well-orders and about the relationship between ordinal characterizability and ordinal arithmetic. The broader significance of cardinal characterizability and the relationships between different notions of characterizability are also discussed. AMS subject classification : 03B15 and 03C85 Presented by Melvin Fitting