Phonological underspecification and mapping mechanisms in the speech recognition lexicon

The problem of recognizing phonological variations in the speech input has triggered numerous treatments in speech processing models. Two areas of current controversy are the possibility of phonological underspecification in the mental lexicon and the nature of the mapping mechanism from the speech signal to the abstract lexical entry. We present data from cross-modal repetition priming experiments in English designed to test the differing predictions of speech recognition models regarding tolerance of phonological mismatch in speech. The results show effects of underspecification, supporting models using underspecified lexical entries (e.g. Lahiri & Marslen-Wilson, 1991; Reetz & Lahiri, 2001) over those using fully specified lexical entries (e.g., McClelland & Elman, 1986). The results also show no effects of context, thus favoring context-independent mapping, such as that in the three-way matching mechanism of the Lahiri and Reetz FUL model ( Lahiri, 1999; Reetz, 1998 and Reetz, 1999; Reetz & Lahiri, 2001) over context-dependent mapping used in phonological inference rules ( Gaskell and Marslen-Wilson, 1996 and Gaskell and Marslen-Wilson, 1998).     

Perceptions of Ethical Behaviour among Business Faculty in Canada

Faculty members at Canadian business schools were surveyed regarding their ethical perceptions of behaviours related to undergraduate instruction. Fifty-five behavioural statements were listed and respondents were asked to rate the extent to which they felt each behaviour was ethical or unethical. The only item that respondents endorsed as unequivocally unethical (90% indicated it was definitely unethical) was Becoming sexually involved with an undergraduate in one of your classes. We also compared the results of our sample to those of an American sample. Overall, an interesting pattern of differences emerged between the responses obtained in Canada and the U.S. In general, the direction of the significant differences was such that Canadian professors viewed the behaviours in question as less ethical than did their American counterparts.     

Using video modeling and reinforcement to teach perspective-taking skills to children with autism

We evaluated video modeling and reinforcement for teaching perspective-taking skills to 3 children with autism using a multiple baseline design. Video modeling and reinforcement were effective; however, only 2 children were able to pass an untrained task, indicating limited generalization. The findings suggest that video modeling may be an effective technology for teaching perspective taking if researchers can continue to develop strategies for enhancing the generalization of these new skills.     

Visual perspective taking for avatars in a Simon task

In modern digital applications, users often interact with virtual representations of themselves or others, called avatars. We examined how these avatars and their perspectives influence stimulus–response compatibility in a Simon task. Participants responded to light/dark blue stimuli with left/right key presses in the presence of a task-irrelevant avatar. Changes in stimulus–response compatibility were used to quantify changes in the mental representation of the task and perspective taking toward this avatar. Experiments 1 and 2 showed that perspective taking for an avatar occurred in orthogonal stimulus–response mappings, causing a compatibility effect from the avatar’s point of view. In the following two experiments we introduced a larger variety of angular disparities between the participant and avatar. In Experiment 3, the Simon effect with lateralized stimulus positions remained largely unaffected by the avatar, pointing toward an absence of perspective taking. In Experiment 4, after avatar hand movements were added in order to strengthen the participants’ sense of agency over the avatar, a spatial compatibility effect from the avatar’s perspective was observed again, and hints of the selective use of perspective taking on a trial-by-trial basis were found. Overall, the results indicate that users can incorporate the perspective of an avatar into their mental representation of a situation, even when this perspective is unnecessary to complete a task, but that certain contextual requirements have to be met.     

Adolescent Substance Use: Preliminary Examinations of School and Neighborhood Context

In considering the influences of microsystems on adolescent substance use, familial and peer contexts have received the most extensive attention in the research literature. School and neighborhood settings, however, are other developmental contexts that may exert specific influences on adolescent substance use. In many instances, school settings are organized to provide educational services to students who share similar educational abilities and behavioral repertoires. The resulting segregation of students into these settings may result in different school norms for substance use. Similarly, neighborhood resources, including models for substance use and drug sales involvement, may play an important role in adolescent substance use. We briefly review literature examining contextual influences on adolescent substance use, and present results from two preliminary studies examining the contribution of school and neighborhood context to adolescent substance use. In the first investigation, we examine the impact of familial, peer, and school contexts on adolescent substance use. Respondents were 283 students (ages 13 to 18) from regular and special education classrooms in six schools. Although peer and parental contexts were important predictors of substance use, school norms for drug use accounted for variance in adolescent use beyond that explained by peer and parental norms. Data from a second study of 114 adolescents (mean age = 15) examines neighborhood contributions to adolescent substance use. In this sample, neighborhood indices did not contribute to our understanding of adolescent substance use. Implications for prevention are presented.     

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.