Horse-race model simulations of the stop-signal procedure

In the stop-signal paradigm, subjects perform a standard two-choice reaction task in which, occasionally and unpredictably, a stop-signal is presented requiring the inhibition of the response to the choice signal. The stop-signal paradigm has been successfully applied to assess the ability to inhibit under a wide range of experimental conditions and in various populations. The current study presents a set of evidence-based guidelines for using the stop-signal paradigm. The evidence was derived from a series of simulations aimed at (a) examining the effects of experimental design features on inhibition indices, and (b) testing the assumptions of the horse-race model that underlies the stop-signal paradigm. The simulations indicate that, under most conditions, the latency, but not variability, of response inhibition can be reliably estimated.     

Pathways to violence in the children of mothers who were depressed postpartum

The impact of postnatal depression on a child's risk for violent behavior was evaluated in an urban British community sample (N=122 families). Mothers were interviewed during pregnancy, at 3 months postpartum, and when the child was 1, 4, and 11 years of age. Mothers, teachers, and children reported on violent symptoms at age 11. Structural equation modeling revealed that the child's violence was predicted by the mother's postnatal depression even when her depression during pregnancy, her later history of depression, and family characteristics were taken into account. Violence was associated with symptoms of attention-deficit/hyperactivity disorder and problems with anger management. Children were most violent if mothers had been depressed at 3 months and at least once thereafter.     

Dual prism adaptation: calibration or alignment?

Dual adaptation to different amounts or directions of prismatic displacement, or both, can be acquired and maintained with little mutual interference. Associative recalibration of the regional task- or workspace, contingent on differentiation of distinguishing sensory information, can explain such adaptation. In contrast, nonassociative realignment restores dimensional mapping among spatial representations. Methods for measuring the separate contributions of those 2 kinds of prism adaptation are identified in the present article. On the basis of a critique of dual-adaptation studies, the authors suggest that recalibration can explain the data but that the method used in those experiments confounded realignment and might have obscured the effectiveness of dual-calibration training.     

Inhibitory attentional control in patients with frontal lobe damage

The performance of a group of frontal lobe lesion and a group of frontal lobe dementia patients was compared with the performance of their respective matched normal control groups on two tests of inhibitory attentional control-the stop-signal reaction time task and a negative priming task. Both patient groups responded significantly slower than their respective normal control groups, but they showed only marginally significant selective impairments on the measures of inhibition. The data suggest that the specific inhibitory processes evaluated by these two tests are, in general, spared in patients with focal frontal lobe lesions or frontal lobe degeneration.     

Confirmatory factor analysis of the WMS-III in patients with temporal lobe epilepsy

Five competing models specifying the factor structure underlying the Wechsler Memory Scale-Third Edition (D. Wechsler, 1997b) primary subtest scores were evaluated in a sample of patients with intractable temporal lobe epilepsy (N = 254). Models specifying separate immediate and delayed constructs resulted in inadmissible parameter estimates and model specification error. There were negligible goodness-of-fit differences between a 3-factor model of working memory, auditory memory, and visual memory and a nested--more parsimonious--2-factor model of working memory and general memory. The results suggest that specifying a separate visual memory factor provides little advantage for this sample--an unexpected finding in a population with lateralized dysfunction, for which one might have predicted separate auditory and visual memory dimensions.     

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.