A linear optical trajectory informs the fielder where to run to the side to catch fly balls

P. McLeod, N. Reed, and Z. Dienes (2002) argued that the linear optical trajectory (LOT) strategy incorrectly cues fielders to run forward for balls headed beyond them. The authors of this article explain that the downward optical curvature found for balls landing beyond the fielder's initial position occurs because the balls reorient the direction the fielder is facing during pursuit. Thus, when downward optical curvature begins, the ball is headed to land in front of where the fielder is facing and running. This investigation of open-loop failure conditions has led to new insights such as the reorientation of the fielder, and it supports the use of maintaining matching rates of vertical and lateral optical ball movement consistent with primacy of the LOT control mechanism even when interception is unachievable.     

Transient manic behavior after pallidotomy

We report two cases of transient hypomanic behavior following pallidotomy. Both of the reported patients had lesions involving non-motor portions of the globus pallidus. Patient 1 had a lesion in the left anteromedial portion of GPi, while patient 2 had one lesion involving the anteromedial portion of GPi on the right and a second lesion involving the postero-ventral most portion of the putamen on the left. These cases emphasize the importance of placing lesions within the sensori-motor portion of GPi without infringing on adjacent non-motor portions. Cases involving transient manic behavior after pallidotomy have not been previously reported. Centers performing pallidotomy or DBS should be aware that lesions or stimulation too anterior in the GPi might lead to manic behavior.     

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.     

Treatment of bipolar depression

Bipolar disorder is underdiagnosed and often mistaken for unipolar depression. Bipolar patients spend 33% of their time in a state of depression compared to 11% of time spent in a manic state. Duration of time depressed and severity of depression are associated with increased risk for suicide, which occurs in 10% to 20% of bipolar patients. Antidepressants are increasingly being used as adjuncts in the depressed phase of bipolar disorder, although they provide a moderate risk for switch into mania. Lithium and some antiepileptics and atypical antipsychotics have shown antidepressant effects in the treatment of bipolar disorder. Other adjuncts for treatment-refractory patients include monoamine oxidase inhibitors and electroconvulsive therapy.     

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.     

Table of Contents of Volume 20

Volume Contents

Table of Contents of Volume 20     

Goal Imagery: Bridging the Gap Between Implicit Motives and Explicit Goals

Two studies examined the role of goal imagery (i.e., the perception-like mental representation of the pursuit and attainment of a goal) in establishing congruence between individuals' implicit motives and their inclination to pursue explicit goals assigned to them. Study 1 found that after a goal-imagery exercise, implicit needs for power and affiliation predicted participants' affective arousal and their commitment to a social-interaction goal furnished with power- and affiliation-related incentives. In Study 2, implicit power motivation predicted the level of performance participants achieved in pursuit of a competitive performance goal after a goal-imagery exercise. Without goal imagery, however, participants' motivation to pursue an explicit goal was independent of their implicit motive dispositions in both studies.     

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.