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.     

The puzzle of language evolution

Linguistics must again concentrate on the evolutionary nature of language, so that language models are more realistic with respect to human natural languages and have a greater explanatory force. Multi-agent systems are proposed as a possible route to develop such evolutionary models and an example is given of a concrete experiment in the origins and evolution of word-meaning based on a multi-agent approach.     

What is the speed to transparent and kinetic-boundary displays?

To compare transparent motion and kinetic boundaries with unidirectional motion, in many studies the relative motion is generated by superimposing or adjoining unidirectional motions oriented in opposite directions. The presumption, tacitly underlying this comparison, is that the two oppositely directed velocities are independent of one another as far as their speed is concerned, i.e. the speed of the relative motion is presumed to be equivalent to the speed of the unidirectional components. Here we report that the relative motion between dots moving in opposite directions augments perceived speed. A constant-stimuli procedure was used to pair transparent-motion or kinetic-boundary displays with unidirectional motion, and human observers were asked to match the speed of the relative and unidirectional motions. The results show that transparency and kinetic boundaries increase the perceived visual speed by about 50%, compared with the speed of the individual components.     

The import of infancy for individual, family, and societal development: commentary on the special section: does infancy matter?

Across their life spans humans actualize a rich potential for cognitive and behavioral plasticity, a set of developmental changes that require regulation of organism-context relations on the part of both the individual and society. An individual’s action on the context must be started in infancy and maintained in subsequent periods of life for adaptive functioning to occur across ontogeny. The process of the individual acting as an agent of his or her own development is continuous, although the particular ways in which such control is manifested will change in developmentally appropriate ways. As demonstrated by the papers in this special issue, the significance of infancy for the rest of the life-span rests on an integration of this continuity and discontinuity within the person-context developmental system.