Der Sinn Pessimistischer Sätze

Ohne Zusammenfassung     

Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

In the research area of multiple criteria decision making, very few publications exist that explicitly design the simulation of a decision maker (DM) in an interactive approach. For this reason, we outline some methods widely used in the literature to identify common assumptions of simulating the DM's responses and the required input preference information. Our paper aims at covering the identified gap by introducing experimental concepts. Such concepts are used for theoretical analyses of a combined search‐and‐decision‐making procedure. Simulating the DM is a fruitful idea because the algorithm can be tested without integrating a human decision maker. Finally, we conduct experiments based on the proposed settings for a multiobjective inventory routing problem, which is a relevant and challenging logistic problem. Copyright © 2014 John Wiley & Sons, Ltd.     

Disentangling dimensions in the dimensional change card-sorting task

The dimensional change card-sorting task (DCCS task) is frequently used to assess young children's executive abilities. However, the source of children's difficulty with this task is still under debate. In the standard DCCS task, children have to sort, for example, test cards with a red cherry or a blue banana into two boxes marked with target cards showing a blue cherry and a red banana. Typically, 3-year-olds have severe problems switching from sorting by one dimension (e.g. color) to sorting by the other dimension (e.g. shape). Three experiments with 3- to 4-year-olds showed that separating the two dimensions as properties of a single object, and having them characterize two different objects (e.g. by displaying an outline of a cherry next to a red filled circle on the card) improves performance considerably. Results are discussed in relation to a number of alternative explanations for 3-year-olds' difficulty with the DCCS task.     

An automated version of the operation span task

We present an easy-to-administer and automated version of a popular working memory (WM) capacity task (operation span; Ospan) that is mouse driven, scores itself, and requires little intervention on the part of the experimenter. It is shown that this version of Ospan correlates well with other measures of WM capacity and has both good internal consistency (alpha=.78) and test-retest reliability (.83). In addition, the automated version of Ospan (Aospan) was shown to load on the same factor as two other WM measures. This WM capacity factor correlated with a factor composed of fluid abilities measures. The utility of the Aospan was further demonstrated by analyzing response times (RTs) that indicated that RT measures obtained in the task accounted for additional variance in predicting fluid abilities. Our results suggest that Aospan is a reliable and valid indicator of WM capacity that can be applied to a wide array of research domains.     

Decision-making impairments in patients with Parkinson's disease

A high percentage of Parkinson's disease (PD) patients show cognitive impairments in addition to the cardinal motor symptoms. These deficits primarily concern executive functions most probably linked to dysfunctions in prefrontal regions due to decreased dopaminergic transmission in fronto-striatal loops. To investigate possible associations between decision-making and executive functions in PD, we examined 20 non-demented PD patients and 20 healthy control subjects with a neuropsychological test battery and the Game of Dice Task. In this computerised decision-making task, the rules for gains and losses and the winning probabilities are obvious and stable. Thus, strategic components besides feedback processing might influence decision-making in this task. We found that PD patients were impaired in the Game of Dice task performance and that the frequency of disadvantageous choices correlated with both executive functions and feedback processing. We suggest that decision-making deficits of PD patients in explicit gambling situations might be associated with dysfunctions in two different fronto-striatal loops: the limbic-orbitofrontal-striatal loop, involved in feedback processing, and the dorsolateral prefrontal-striatal loop, involved in executive functions.     

Knowledge level learning and the power law: A soar model of skill acquisition in scheduling

Zusammenfassung. Das hier vorgestellte Modell lernt graduell, Planungsaufgaben aus der Klasse der Maschinenbelegungsprobleme (job-shop-scheduling problems) zu lösen. Mit Hilfe des Chunking-Mechanismus von Soar wird episodisches Wissen über die Belegungsreihenfolge von Aufträgen auf Maschinen memoriert. Bei der Entwicklung des Modells wurden zahlreiche qualitative (z. B. Transfereffekte) und quantitative Befunde (z. B. Bearbeitungszeiten) aus einer früheren empirischen Untersuchung berücksichtigt. In einer Validierungsstudie wurden dieselben Aufgaben von 14 Probanden und dem Modell bearbeitet. Die Passung von Simulationsdaten und empirischen Ergebnissen fiel insgesamt gut aus. Allerdings löst das Modell die Aufgaben schneller und zeigt auch einen etwas besseren Lernverlauf als die Probanden. Das Modell liefert eine Erklärung für das Rauschen, das typischerweise bei Bearbeitungszeiten zu beobachten ist: es handelt sich um erworbenes Wissen, das mehr oder weniger gut und auch unterschiedlich häufig auf neue Situationen übertragen wird. Der Lernverlauf der Probanden entspricht nur für aggregierte Daten einer Potenzfunktion (power law). Der vorgestellte Mechanismus zeigt, wie ein symbolisches Modell der Informationsverarbeitung graduelle Verhaltensänderungen generiert und wie der offensichtliche Erwerb allgemeiner Prozeduren ohne explizites Lernen von deklarativen Regeln erfolgen kann. Es wird nahegelegt, daß es sich hier um die Modellierung einer Form impliziten Lernens handelt. Summary. The model presented here gradually learns how to perform a job-shop scheduling task. It uses Soar's chunking mechanism to acquire episodic memories about the order to schedule jobs. The model was based on many qualitative (e.g., transfer effects) and quantitative (e.g., solution time) regularities found in previously collected data. The model was tested with new data where scheduling tasks were given to the model and to 14 subjects. The model generally fit these data with the restrictions that the model performs the task (in simulated time) faster than the subjects, and its performance improves somewhat more quickly than the subjects' performance. The model provides an explanation of the noise typically found in problem solving times - it is the result of learning actual pieces of knowledge that transfer more or less to new situations but rarely by an average amount. Only when the data are averaged (i.e., over subjects) does the smooth power law appear. This mechanism demonstrates how symbolic models can exhibit a gradual change in behavior and how the apparent acquisition of general procedures can be performed without resorting to explicit declarative rule generation. We suggest that this may represent a type of implicit learning.