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.     

Further evidence for feature correlations in semantic memory.

The role of feature correlations in semantic memory is a central issue in conceptual representation. In two versions of the feature verification task, participants were faster to verify that a feature (< is juicy >) is part of a concept (grapefruit) if it is strongly rather than weakly intercorrelated with the other features of that concept. Contrasting interactions between feature correlations and SOA were found when the concept versus the feature was presented first. An attractor network model of word meaning that naturally learns and uses feature correlations predicted those interactions. This research provides further evidence that semantic memory includes implicitly learned statistical knowledge of feature relationships, in contrast to theories such as spreading activation networks, in which feature correlations play no role.     

Espbase: A microsoft access tool for selecting symbol and icon sets for usability

The ESPbase provides a tool for storing symbols and icons along with information about their characteristics. Information about a wide range of symbol characteristics is included on the database to facilitate the selection of symbol sets for research and design. The database includes information about the graphical characteristics and functions of symbols. It also includes ratings of symbol concreteness, complexity, familiarity, and meaningfulness. Symbols and icons can be accessed on the basis of each of these characteristics or any combination of characteristics. This makes it easier to select symbols on the basis of usability and design requirements. It also means that symbols can be easily selected for research while controlling their characteristics on a number of dimensions.     

Milestones in the development of infant numeracy

This article deals with the development of numeracy among young children of the four to seven year age group. The research investigation described had two aims: to see how early numeracy develops in children of different ages; and to see whether there are fixed points which might serve as milestones in the development of infant numeracy. The results of this investigation involving 96 children indicate a strong development of numeracy skills during this period. With reference to the literature, it is also shown that certain items of a test previously developed for this purpose may be taken as core items that do indeed represent milestones in the development of infant numeracy.     

When Good Intentions Are Not Enough: Modeling Postdecisional Cognitive Correlates of Condom Use1

Measures of intention usually leave substantial proportions of the variance in behavior unexplained. It has been suggested that improved behavioral prediction could be achieved by identifying postdecisional cognitive processes capable of distinguishing between intenders who do act and those do not act. Condom-related self-report measures of postdecisional cognitive processes were developed and tested in a cross-sectional questionnaire study involving 447 heterosexual students. A discriminant function composed of postdecisional measures was found to significantly distinguish between intenders who reported use and non-use and to correctly classify 80% of intenders. The results indicate that measures of the relative importance of competing intentions, prior planning of specific preparatory actions, and action-specific self-efficacy may enhance the prediction of condom use among intenders.     

The assessment of obsessionality using the Padua inventory: its validity in a British non-clinical sample

The Padua Inventory (PI) is a measure of obsessive–compulsive phenomena, which permits measurement of both number and severity of symptoms. Data is available from Italy, the US, the Netherlands and Australia on non-clinical samples. The present study provides a psychometric evaluation of the PI on a British non-clinical sample (N=1855). Comparisons are made with other data and further suggestions for the use of the PI are made. Results from the present study indicate that the PI has similar, though not identical, factor structure to previous studies, adequate internal reliability and construct validity. Four factors emerged, containing clusters of items broadly relating to ‘impaired control over mental activities/doubting', ‘contamination', ‘checking' and ‘worries about losing control over motor behaviour' and these included most of the items loading on the same factors in earlier studies together with additional items.The relationships of age and gender with the PI scores are investigated. Differences were found from the Sanavio (1988) [Sanavio, E. (1988). Obsessions and compulsions: the Padua Inventory. Behaviour Research and Therapy, 26, 169–177.] Italian sample. These differences are in keeping with other available data from British samples and other data on the epidemiology of Obsessive Compulsive Disorder. The five factor structure found in the one study using clinical and non-clinical subjects is discussed in relation to the present analyses.