Further Support for a Hierarchical Model of Affect: Reply to Green and Salovey

Green and Salovey agree with us that happiness and sadness are strongly bipolar, and that Positive and Negative Activation are not. However, Green and Salovey subscribe to the two-dimensional circumplex, a model we find wanting. Actually, the problems they discuss arise from the limitations of the circumplex rather than from issues fundamental to affective structure. And they do not consider the evidence, reported in our target article, that supports an alternative to the circumplex: a three-level hierarchical model. In this reply, we take a closer look at the empirical findings supporting this model, and we emphasize its heuristic promise.     

"I" Value Freedom, But "We" Value Relationships: Self-Construal Priming Mirrors Cultural Differences in Judgment

The distinction between relatively independent versus interdependent self-construals has been strongly associated with several important cultural differences in social behavior. The current studies examined the causal role of self-construal by investigating whether priming independent or interdependent self-construals within a culture could result in differences in psychological worldview that mirror those traditionally found between cultures. In Experiment 1, European-American participants primed with interdependence displayed shifts toward more collectivist social values and judgments that were mediated by corresponding shifts in self-construal. In Experiment 2, this effect was extended by priming students from the United States and Hong Kong with primes that were consistent and inconsistent with their predominant cultural worldview. Students who received the inconsistent primes were more strongly affected than those who received the consistent primes, and thus shifted self-construal, and corresponding values, to a greater degree.     

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.     

Reinventing the University: From Institutions to Communities of Higher Education

This article responds to three questions: What if the primary mission of the university is to (i) nourish the intellect, (ii) commit to the sacredness of all life, and (iii) foster spirituality? Responses examine multiple forms of literacy, instruction that is ecologically valid and thus oriented toward the unity of knowledge, and teaching as a moral activity. The suggestion here is that meaningful reform will emphasize education of the whole person in relation to the physical, symbolic, and social environments. Curricula focused on forms of representation, modes of treatment, and syntactic structure, adaptive understandings, constructed meanings, critical thinking, real problems, connected knowing, creativity, inspiration, service learning, and the development of declarative procedural and poetic knowledge are discussed.     

Constrained maximum likelihood estimation of two-level covariance structure model via EM type algorithms

In this paper, the constrained maximum likelihood estimation of a two-level covariance structure model with unbalanced designs is considered. The two-level model is reformulated as a single-level model by treating the group level latent random vectors as hypothetical missing-data. Then, the popular EM algorithm is extended to obtain the constrained maximum likelihood estimates. For general nonlinear constraints, the multiplier method is used at theM-step to find the constrained minimum of the conditional expectation. An accelerated EM gradient procedure is derived to handle linear constraints. The empirical performance of the proposed EM type algorithms is illustrated by some artifical and real examples.This research was supported by a Hong Kong UCG Earmarked Grant, CUHK 4026/97H. We are greatly indebted to D.E. Morisky and J.A. Stein for the use of their AIDS data in our example. We also thank the Editor, two anonymous reviewers, W.Y. Poon and H.T. Zhu for constructive suggestions and comments in improving the paper. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Verbal Strategies for Seeking Help in Organizations1

The verbal strategies used to frame requests for help are integral to the help-seeking process. Drawing on politeness theory, it was predicted that gender, power, and norms affect usage of verbal strategies in seeking help, and verbal strategies predict interpersonal outcomes. Two studies showed that: (a) individuals used more strategies under collectivistic than individualistic norms; (b) under individualistic norms, men used more strategies when seeking help upward, and women used more strategies when seeking help laterally; and (c) those who used more strategies provided higher quality information, and were perceived more favorably by others. These results suggest that verbal strategies not only reflect important relational and contextual factors, but also serve multiple social functions.