An associative framework for probability judgment: an application to biases

Three experiments show that understanding of biases in probability judgment can be improved by extending the application of the associative-learning framework. In Experiment 1, the authors used M. A. Gluck and G. H. Bower's (1988a) diagnostic-learning task to replicate apparent base-rate neglect and to induce the conjunction fallacy in a later judgment phase as a by-product of the conversion bias. In Experiment 2, the authors found stronger evidence of the conversion bias with the same learning task. In Experiment 3, the authors changed the diagnostic-learning task to induce some conjunction fallacies that were not based on the conversion bias. The authors show that the conjunction fallacies obtained in Experiment 3 can be explained by adding an averaging component to M. A. Gluck and G. H. Bower's model.     

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.     

Theories of reasoned action and planned behavior as models of condom use: a meta-analysis

To examine how well the theories of reasoned action and planned behavior predict condom use, the authors synthesized 96 data sets (N = 22,594) containing associations between the models' key variables. Consistent with the theory of reasoned action's predictions, (a) condom use was related to intentions (weighted mean r. = .45), (b) intentions were based on attitudes (r. = .58) and subjective norms (r. = .39), and (c) attitudes were associated with behavioral beliefs (r. = .56) and norms were associated with normative beliefs (r. = .46). Consistent with the theory of planned behavior's predictions, perceived behavioral control was related to condom use intentions (r. = .45) and condom use (r. = .25), but in contrast to the theory, it did not contribute significantly to condom use. The strength of these associations, however, was influenced by the consideration of past behavior. Implications of these results for HIV prevention efforts are discussed.     

The recognition potential: An ERP index of lexical access

Recognition potential (RP) is a brain electrical response that appears when a subject views recognizable images of words. However, it has yet to be determined whether the processes reflected by RP are related to orthographic or to semantic analysis. This study aimed to resolve this question by studying the RP evoked by orthographically correct stimuli that were devoid of meaning. Results showed RP not only to this type of stimuli, but also to others achieving lower levels in the reading process. Strikingly, however, the RP amplitude significantly differed in parallel with the levels of the reading processes attained by the stimuli, the amplitude of the RP progressively increasing as the level approached the semantic one, which showed the highest amplitude. These results not only confirm the replicability of RP, but also its promise of potential usefulness in the study and assessment of language perception.     

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.