Processing of Grammatical Gender in a Three-Gender System: Experimental Evidence from Russian

Four experiments investigated the effect of grammatical gender on lexical access in Russian. Adjective–noun pairs were presented auditorily, using a cued-shadowing technique in which subjects must repeat the second word (the target noun), following adjectives that are either concordant or discordant with the noun's gender. Experiment 1 demonstrates gender priming with unambiguous adjectives and phonologically transparent masculine or feminine nouns. Experiment 2 examines priming for transparent nouns against a neutral baseline (possible only for feminines and neuters), revealing that priming is due primarily to inhibition from discordant gender. Experiment 3 demonstrates gender priming with phonologically opaque masculine and feminine nouns. Experiment 4 returns to transparent masculine and feminine nouns with a different kind of baseline, using three versions of a single word root (prost—simple, in the feminine adjectival form prostaja, masculine adjectival form prostoj, and the adverbial form prosto ), and shows that gender can also facilitate lexical access, at least for feminine nouns. We conclude that Russian listeners can exploit gender agreement cues on-line, helping them to predict the identity of an upcoming word.     

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.     

The Role of Schools in Outcomes for Youth: Implications for Children's Mental Health Services Research

We explore the role of schools in children's mental health services research. Recent literature has suggested that schools play an important role in delivering services to children and adolescents with emotional and behavioral problems. Research in services research, though, has taken a fairly narrow view of which dimensions of school environments are relevant for inclusion in studies. We suggest that a broader view of school environments is appropriate and potentially beneficial to the field. Using Bronfenbrenner's ecological model as a guide, we conceptualize schools as microsystems. Such an approach suggests that all aspects of school environments (treatment as well as non-treatment) are likely to influence many of the outcomes that children's mental health services research frequently targets (e.g., behavioral problems, problematic peer relationships, academic achievement, school attendance). We review literature from a variety of disciplines to suggest relevant features of schools, with particular attention to the role of peer dynamics within schools. We conclude with implications of this expanded conceptualization of schools for children's mental health services research.     

Perceived area and the luminosity threshold

Observers made forced-choice opaque/luminous responses to targets of varying luminance and varying size presented (1) on the wall of a laboratory, (2) as a disk within an annulus, and (3) embedded within a Mondrian array presented within a vision tunnel. Lightness matches were also made for nearby opaque surfaces. The results show that the threshold luminance value at which a target begins to appear self-luminous increases with its size, defined as perceived size, not retinal size. More generally, the larger the target, the more an increase in its luminance induces grayness/blackness into the surround and the less it induces luminosity into the target, and vice versa. Corresponding to this luminosity/grayness tradeoff, there appears to be an invariant: Across a wide variety of conditions, a target begins to appear luminous when its luminance is about 1.7 times that of a surface that would appear white in the same illumination. These results show that the luminosity threshold behaves like a surface lightness value—the maximum lightness value, in fact—and is subject to the same laws of anchoring (such as the area rule proposed by Li & Gilchrist, 1999) as surface lightness.     

Stimulus-determined discrimination mechanisms for color search

Visual attention can be goal driven, stimulus driven, or a combination of the two. Here we report evidence for an unexpectedly stimulus-driven component of visual search for a target defined by color. Observers demonstrated a surprisingly cost-free ability to incorporate multiple classifiers in search for a target of one color from among distractors of other colors. A target color was presented among distractors that could change from trial to trial (intermixed presentation) or that remained constant across all trials in a block (blocked presentation). For blocked presentation, a single search classifier (a mechanism that segregates the target from distractors in color space) could be adopted, whereas for intermixed presentation different classifiers had to be used when the distractor colors changed. The benefit of blocked presentation was very small, suggesting that the appropriate classifier was determined very quickly in trials for which the classifier changed. The results suggest that the stimulus-driven activation of an appropriate stimulus classifier can be very efficient.