Numerical stroop effect

2 male and 2 female students from an undergraduate class in experimental psychology were participants in a numerical Stroop effect experiment. Mean age for the students was 19.5 yr., with a SD of 2.4. The Stroop effect was investigated with a numerical, as opposed to a color, task. The four conditions were Symbols (-, + + +, ===, ::, :::), versus Alphabetic Characters (C, AAA, DD, BBB, DDD), versus Matched Digits and Number of Digits (1, 4444, 333, 22, 4444), versus Mismatched Digits and Number of Digits (2, 1111, 444, 33, 3333). Participants took longer to count Mismatched Digits than Symbols or Alphabetic Characters (analogous to the color Stroop effect). Participants took longer to read Mismatched Digits than Alphabetic Characters (analogous to the color reverse Stroop effect). Participants took less time to read or count Matched Digits than Alphabetic Characters (facilitation effect).     

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.     

Connectionist modelling of the separable processing of consonants and vowels

describe two aphasic patients, with impaired processing of vowels and consonants, respectively. The impairments could not be captured according to the sonority hierarchy or in terms of a feature level analysis. Caramazza et al. claim that this dissociation demonstrates separate representation of the categories of vowels and consonants in speech processing. We present two connectionist models of the management of phonological representations. The models spontaneously develop separable processing of vowels and consonants. The models have two hidden layers and are given as input vowels and consonants represented in terms of their phonological distinctive features. The first model is presented with feature bundles one at a time and the hidden layers have to combine their output to reproduce a unified copy of the feature bundle. In the second model a "fine-coded" layer receives information about feature bundles in isolation, and a "coarse-coded" layer receives information about each feature bundle in the context of the prior and subsequent feature bundle. Coarse-coding facilitated processing of vowels and fine-coding processing of consonants. These models show that separable processing of vowels and consonants is an emergent effect of modular processors operating on feature-based representations. We argue that it is not necessary to postulate an independent level of representation for the consonant/vowel distinction, separate from phonological distinctive features.     

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.     

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.