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.     

Reversing the phonological similarity effect

The phonological similarity effect--poor retention of order for lists of similar-sounding items--is a benchmark finding in the short-term memory literature. In our first two experiments, we show that the effect actually reverses following relatively brief periods of distraction, yielding better order retention for similar than for dissimilar lists, provided that different items are used on every trial. In Experiment 3, the same items were used on every trial and similar lists produced poorer performance across all three retention intervals. The results are interpreted from a general discrimination framework: Items are viewed as occupying positions in a multidimensional space defined by list and within-list dimensions.     

The Dimensionality of Memory Strength Between Levels of Both Serial Position and Word Frequency Category

In recognition tasks where Signal Detection Theory (SDT) is applied, a suitable decision variable is required. This variable is typically assumed to be a global assessment of memory strength. A further assumption is that factors such as old-new, serial position (SP), and word frequency (WF) simply act to change the value of strength across levels within the factor. In terms of SDT, the decision variable is assumed to be uni-dimensional. Four experiments are reported that provide a direct empirical assessment of this assumption for SP and WF. Judgements of items (JOI), recency (JOR), and primacy (JOP) were measured for SP; and subjective frequency (JOF) was measured for WF. Multiple dimensions imply non-additive effects across factor levels and different retrieval processes or the influence of an extra process. A single dimension is consistent with an additive effect of factor levels and current theorizing. The data are in fairly good agreement with the uni-dimensional assumption across all four factors with the exception of JOR and JOP. These showed less uni-dimensionality for comparisons between beginning vs. end list items. The implications of this are discussed.     

The Sigmund Freud Museum Exhibit: The Price for a Bit of Good Luck

The Museum Exhibit on the life and works of Sigmund Freud and psychoanalysis opened at the Library of Congress in Washington, DC on October 15, 1998. The Exhibit includes both praise and critical commentary regarding Freud's many contributions. Original documents, artifacts and personal items from Freud collections are on display as well as film clips, photographs, and other recordings. Different periods in Freud's life are highlighted. After leaving Washington, DC, the Exhibit will travel to New York, Los Angeles, and on to Vienna.     

Book reviews

Classical Probability in the Enlightenment. LORRAINE DASTON, 1988. Princeton, Princeton University Press. xviii + 423 pp., US$19.95 (paperback). ISBN 0–691–08497–1, 0–691–00644‐X (paperback)

Following Form and Function: a Philosophical Archaeology of Life Science. STEPHEN T. ASMA, 1996. Evanston, IL, Northwestern University Press. xiii + 232 pp., price not available

Concepts and Methods in Evolutionary Biology. ROBERT N. BRANDON, 1996. Cambridge, Cambridge University Press. xiv+221 pp., $US59.95 (hardback), $Can 27.50 (paperback). ISBN 0–521–49545–9 (hardback), 0–521–49888–0 (paperback)

The Completeness of Scientific Theories: on the Derivation of Empirical Indicators within a Theoretical Framework: the Case of Physical Geometry. The University of Western Ontario Series in Philosophy of Science, Volume 53. MARTIN CARRIER, 1994. Dordrecht, Kluwer. xviii + 248 pp., price not available. ISBN 0–7923–24757 (hardback)

The Philosophy and History of Molecular Biology: New Perspectives. Boston Studies in the Philosophy of Science, Volume 183. SAHOTRA SARKAR (Ed.), 1996. Dordrecht, Kluwer. 264 pp., $US127. ISBN 0–7923–3947–9

Awareness: What it is, What it does. CHRIS NUNN, 1996. London and New York, Routledge. price not available. ISBN 0–415–13227–4     

Moral principles as moral dispositions

What are moral principles? In particular, what are moral principles of the sort that (if they exist) ground moral obligations or—at the very least—particular moral truths? I argue that we can fruitfully conceive of such principles as real, irreducibly dispositional properties of individual persons (agents and patients) that are responsible for and thereby explain the moral properties of (e.g.) agents and actions. Such moral dispositions (or moral powers) are apt to be the metaphysical grounds of moral obligations and of particular truths about what is morally permissible, impermissible, etc. Moreover, they can do other things that moral principles are supposed to do: explain the phenomena “falling within their scope,” support counterfactuals, and ground moral necessities, “necessary connections” between obligating reasons and obligations. And they are apt to be the truthmakers for moral laws, or “lawlike” moral generalizations.     

A cleansing fire: Moral outrage alleviates guilt and buffers threats to one’s moral identity

Why do people express moral outrage? While this sentiment often stems from a perceived violation of some moral principle, we test the counter-intuitive possibility that moral outrage at third-party transgressions is sometimes a means of reducing guilt over one’s own moral failings and restoring a moral identity. We tested this guilt-driven account of outrage in five studies examining outrage at corporate labor exploitation and environmental destruction. Study 1 showed that personal guilt uniquely predicted moral outrage at corporate harm-doing and support for retributive punishment. Ingroup (vs. outgroup) wrongdoing elicited outrage at corporations through increased guilt, while the opportunity to express outrage reduced guilt (Study 2) and restored perceived personal morality (Study 3). Study 4 tested whether effects were due merely to downward social comparison and Study 5 showed that guilt-driven outrage was attenuated by an affirmation of moral identity in an unrelated context.