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.     

Dissociable stages of problem solving (II): first evidence for process-contingent temporal order of activation in dorsolateral prefrontal cortex

In a companion study, eye-movement analyses in the Tower of London task (TOL) revealed independent indicators of functionally separable cognitive processes during problem solving, with processes of building up an internal representation of the problem preceding actual planning processes. These results imply that processes of internalization and planning should also be distinguishable in time and space with respect to concomitant brain activation patterns. To investigate this possibility, here we conducted analyses of fMRI data for left and right dorsolateral prefrontal cortex (dlPFC) during problem solving in the TOL task by accounting for the trial-by-trial variability of onsets and durations of the different cognitive processing stages. Comparisons between stimulus-locked and response-locked modeling approaches affirmed that activation in left dlPFC was elicited particularly during early processes of internalization, comprising the extraction of goal information and the generation of an internal problem representation, whereas activation in right dlPFC was predominantly attributable to later processes of mental transformations on this representation, that is planning proper. Thus, present data corroborate the proposal that often observed bilateral dlPFC activation patterns during complex cognitive tasks such as problem solving may reflect functionally and, to some extent, even temporally separable processes with opposing lateralizations.     

Alerting enhances attentional bias for salient stimuli: Evidence from a global/local processing task

The present study examined the role of alerting in modulating attentional bias to salient events. In a global/local processing task, participants were presented with a large arrow (global level) comprised of smaller arrows (local level) pointing in the same or opposite directions and had to indicate the direction of the large or small arrows in different blocks. Saliency of the global and local levels was manipulated, creating global-salient and local-salient conditions. Alerting signals were presented in half of the trials prior to the target. Results revealed a double dissociation in the effects of alerting on global/local interference effects. In a global salient condition, alerting increased global interference and decreased local interference. In a local salient condition, alerting reduced global interference and increased local interference. We demonstrate that within a single task, alerting can increase and reduce conflict based on perceptual saliency. These findings help to better understand disorders like hemispatial neglect in which both arousal and attention to salient events are impaired. These results also challenge previous theories suggesting that alerting acts to increase conflict interference. We argue that alerting is an adaptive mechanism that diverts attention to salient events, but comes at a cost when selective attention to less salient details is required.     

Commentary on Ted Ruffman’s “Belief or not belief: …”

I applaud Ruffman for cautioning us against interpreting early sensitivity to others’ beliefs as evidence for an innate theory of mind and for making room for learning. In turn, however, I caution against his claim that all infants need is to understand that people act depending on what they perceive. Instead, infants may keep experiential records (Perner & Roessler, 2010) for other people or records of what they have registered (Apperly & Butterfill, 2009), which makes it less obvious that all required knowledge can be acquired by statistical learning. As a general criticism I remonstrate with current theory of mind research on its lack of concern that we understand people as acting for reasons which goes beyond detecting lawful regularities in behaviour.     

Observed Variability and Values Matter: Toward a Better Understanding of Information Search and Decisions from Experience

The search for different options before making a consequential choice is a central aspect of many important decisions, such as mate selection or purchasing a house. Despite its importance, surprisingly little is known about how search and choice are affected by the observed and objective properties of the decision problem. Here, we analyze the effects of two key properties in a binary choice task: the options' observed and objective values, and the variability of payoffs. First, in a large public data set of a binary choice task, we investigate how the observed value and variability relate to decision‐makers' efforts and preferences during search. Furthermore, we test how these properties influence the chance of correctly identifying the objectively maximizing option, and how they affect choice. Second, we designed a novel experiment to systematically analyze the role of the objective difference between the options. We find that a larger objective difference between options increases the chance for correctly identifying the maximizing option, but it does not affect behavior during search and choice. Copyright © 2013 John Wiley & Sons, Ltd.     

Der Sinn Pessimistischer Sätze

Ohne Zusammenfassung