Dynamic systems as tools for analysing human judgement

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review on this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, it is shown how the task demands of system identification and system control can be realized in these environments and how psychometrically acceptable dependent variables can be derived

Mirror neuron activity is no proof for action understanding

We focus on the thesis that action understanding is a function of the mirror neuron system. According to our opinion, understanding is a process that runs through hermeneutic circles from the “Vorverständnis” (“previous understanding”) to steps of deeper understanding. Our critique relates to the narrow neuroscientific definition of action understanding as the capacity to recognize several movements as belonging to one action. After a reconstruction of the model's developments, we will challenge the claims of the model by Rizzolatti and Sinigaglia (2010). By analyzing the relation between the experimental results and its interpretation, we will conclude that there is no proof that mirror neuron activity leads to action understanding

You Cannot Have Your Cake and Eat It, too: How Induced Goal Conflicts Affect Complex Problem Solving

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 problem- solving 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 im- paired, 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

Dealing with dynamic systems: Research strategy, diagnostic approach and experimental results

The method of computer-simulated scenarios has recently been introduced to study how people solve complex problems. This article describes a special approach to constructing such microworlds by means of linear structural equation systems. The subjects' task is to first identify in a knowledge application phase the causal structure of a hitherto unknown system. In a later knowledge application phase they try to control this system with respect to a given goal state. Verbalizable knowledge that was acquired on the task is assessed both my means of causal diagrams as well as by the degree of successful control performance. Five experiments on special attributes of such systems illustrate the approach. The experiments investigated effects of active interventions versus observation only, effects of different degrees of Eigendynamik, the influence of different degrees of side effects, the role of prior knowledge, the amount of controllability and number of variables to be controlled. These factors have considerable effects on identification of the system structure and control of its states, these being two central indicators of complex problem solving. Three topics are identified as main goals for future research: (1) separation of different sources of variance (person, system, situation); (2) research on reliability and validity of performance indicators; (3) development of measures for an operators' heuristic and strategic knowledge

Microworlds based on linear equation systems: A new approach to complex problem solving and experimental results.

The method of computer-simulated scenarios has recently been introduced to study how people solve complex problems. This paper describes a special approach to constructing such microworlds by means of linear structural equation systems. Subjects' task in the experimental situation is to first identify in a knowledge acquisition phase the causal structure of an hitherto unknown system. In a later knowledge application phase they have to control this system with respect to a given goal state. Knowledge that was acquired on the task is assessed both by means of causal diagrams - a method developed within this project and proven to be very useful - as well as by the degree of successful control performance. Three experiments on special attributes of such systems (active interventions versus observations only, effects of different degrees of Eigendynamik, the influence of different degrees of side effects) illustrate the approach. The menioned factors have considerable influence on identification and control of the system SINUS. The conclusion deals with the advantages of an experimental approach in this area