No abstract

Managing multiple and conflicting goals is a demand typical to both everyday life and complex coordination tasks. Two experiments (N = 111) investigated how goal conflicts affect motivation and cognition in a complex problemsolving paradigm. In Experiment 1, participants dealt with a game-like computer simulation involving a predefined goal relation: Parallel goals were independent, mutually facilitating, or interfering with one another. As expected, goal conflicts entailed lowered motivation and wellbeing. Participants' understanding of causal effects within the simulation was impaired, too. Behavioral measures of subjects' interventions support the idea of adaptive, self-regulatory processes: reduced action with growing awareness of the goal conflict and balanced goal pursuit. Experiment 2 endorses the hypotheses of motivation loss and reduced acquisition of system-related knowledge in an extended problem-solving paradigm of four conflicting goals. Impairing effects of goal interference on motivation and wellbeing were found, although less distinct and robust as in Experiment 1. Participants undertook fewer interventions in case of a goal conflict and acquired less knowledge about the system. Formal complexity due to the interconnectedness among goals is discussed as a limiting influence on inferring the problem structure.

Teaching and theorizing in psychology has long been torn between targeting general underlying principles by observing dynamics in the individual or focusing on average behavior. As dealing with group averages is common practice in psychology, it is important for students to understand how individual learning curves relate to group average curves. In two experiments, we explore whether posing questions about the individual time course of learning can help psychology students to generate valid representations of the average time course of learning. Attempting to foster learning as a generative process, we provided students in Experiment 1 (N ¼ 83) with vignettes asking them to draw hypothetical learning curves of individuals vs. averages over individuals (order of vignettes varied as experimental manipulation) into an empty coordinate system (time on the x-axis, performance on the y-axis; fixed start and endpoints). However, students who worked on the individual-time-course vignette first did not draw better average curves than those undertaking the reverse order of tasks. Experiment 2 (N ¼ 36) found tentative evidence that providing students with a metaphor (falling leaves) can guide attention towards the variability of individual time courses.

Zusammenfassung. Die Produktion von Kausaldiagrammen gilt als eine Methode zur Wissensdiagnostik beim Bearbeiten komplexer Probleme. Ein Experiment sollte untersuchen, inwieweit Kausaldiagramm-Analysen den Umgang mit dynamischen Systemen beeinflussen. Es wurde vermutet, dass prozessbegleitende Kausaldiagramm-Analysen einen hypothesentestenden Problemlösestil sensu Klahr und Dunbar (1988) unterstützen und intensivieren. Als beobachtbare Konsequenz wurden ein erhöhter Erwerb von Strukturwissen und gesteigerte Leistungen im Steuern des Systems erwartet. 64 studentische Versuchspersonen bearbeiteten das Szenario “Ökosystem“ über fünf Durchgänge. Personen, die nach jedem Durchgang ein Kausaldiagramm anfertigten, zeigten sich im abschließend erworbenen Strukturwissen Vergleichspersonen überlegen, die einen oberflächlichen Rekognitionstest, eine nicht szenariobezogene Aufgabe oder gar keine zusätzliche Aufgabe absolviert hatten. Dies spricht für einen Reaktivitätseffekt. Allerdings benötigten kausal-instruierte Personen weder mehr Zeit noch explorierten sie das System geschickter. Auch im Steuern des Systems profitierten sie nicht von ihrem Wissensvorteil, sondern erbrachten Leistungen auf dem Niveau der Kontrollprobanden. Die abschließende Diskussion sieht in der Reaktivität des Instruments einen diagnostischen Gewinn.

Abstract. Scatterplots are ubiquitous data graphs and can be used to depict how well data fit to a quantitative theory. We investigated which information is used for such estimates. In Experiment 1 ( N = 25), we tested the influence of slope and noise on perceived fit between a linear model and data points. Additionally, eye tracking was used to analyze the deployment of attention. Visual fit estimation might mimic one or the other statistical estimate: If participants were influenced by noise only, this would suggest that their subjective judgment was similar to root mean square error. If slope was relevant, subjective estimation would mimic variance explained. While the influence of noise on estimated fit was stronger, we also found an influence of slope. As most of the fixations fell into the center of the scatterplot, in Experiment 2 ( N = 51), we tested whether location of noise affects judgment. Indeed, high noise influenced the judgment of fit more strongly if it was located in the middle of the scatterplot. Visual fit estimates seem to be driven by the center of the scatterplot and to mimic variance explained.