What response times tell of children's behavior on the balance scale task

Analysis of accuracy of responses to balance scale problems gives a global idea of the cognitive processes that underlie problem-solving behavior on this task. We show that response times (RTs) provide additional detailed information about the kind and duration of these processes. We derive predictions about the RTs from Siegler's (1981) model for the balance scale task, including the counterintuitive prediction that young adults are slower than children in solving particular balance scale problems. The predictions were tested in a study in which 191 6- to 22-year-old participants were presented with a computerized balance scale task. RTs were analyzed with regression models. In addition to qualitative differences between items, we also modeled quantitative differences between items in the regression models. Analyses supported the predictions and provided additional knowledge on the rules. Rule II was reformulated as a rule that always involves the encoding, but not always the correct application of the distance cue. RTs provided evidence for the use of a buggy-rule and not an addition-rule. Finally, a relation between rule inconsistency and increased RT was found.     

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.     

对人眼色觉异常的定量测量研究

依据Ｒａｙｌｅｉｇｈ颜色匹配方法研制人眼颜色视觉定量测量实验装置,并对２０名色觉正常和３０名色觉异常大学生的颜色视觉进行测量。结果表明,色觉异常被试在Ｒａｙｌｅｉｇｈ匹配中点和匹配范围测量值上均与色觉正常被试者存在明显的差异。     

FOK判断等级及其准确性的实验研究

通过实验集中探讨了FOK判断等级及其准确性的影响因素。实验以中文字串为学习材料,使用组内设计的实验方法研究了被激活信息的量和强度对FOK等级及准确性的影响。实验结果表明：FOK判断的等级是由被激活信息的总量决定的,和被激活信息的正确与否无关;FOK判断的准确性是由被激活信息的强度决定的;且二者之间是有内在联系的。     

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.     

耳科学的发展与展望

耳科学在人类与耳部疾病的斗争中兴起了１９世纪,在本世纪随科学技术的发展而获得迅速发展,特别是抗菌素和手术显微镜的出现,使耳显同外科技术不断提高,不仅能彻底地祛除耳部感染,而且可以有效地提高听力,耳科学家在聋病的治疗过程中,已能够有效地治疗传导性耳聋,但仍面临着治疗神经性耳聋的挑战,在现代科学技术革命的推动下,随着分析子生物学,细胞生物学等新技术的发展,在２１世纪,人类必定能够彻底治疗聋病。