Joachim Funke

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 of 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, the article sets out how the task demands of system identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived.

The Process of Solving Complex Problems

This article is about Complex Problem Solving (CPS), its history in a variety of research domains (e.g., human problem solving, expertise, decision making, and intelligence), and a formal definition and a process theory of CPS applicable to the interdisciplinary field. CPS is portrayed as (a) knowledge acquisition and (b) knowledge application concerning the goal-oriented control of systems that contain many highly interrelated elements (i.e., complex systems). The impact of implicit and explicit knowledge as well as systematic strategy selection on the solution process are discussed, emphasizing the importance of (1) information generation (due to the initial intransparency of the situation), (2) information reduction (due to the overcharging complexity of the problem’s structure), (3) model building (due to the interconnectedness of the variables), (4) dynamic decision making (due to the eigendynamics of the system), and (5) evaluation (due to many, interfering and/or ill-defined goals).

Dynamic Problem Solving: A New Assessment Perspective

This article addresses two unsolved measurement issues in dynamic problem solving (DPS) research: (a) unsystematic construction of DPS tests making a comparison of results obtained in different studies difficult and (b) use of time-intensive single tasks leading to severe reliability problems. To solve these issues, the MicroDYN approach is presented, which combines (a) the formal framework of linear structural equation models as a systematic way to construct tasks with (b) multiple and independent tasks to increase reliability. Results indicated that the assumed measurement model that comprised three dimensions, information retrieval, model building, and forecasting, fitted the data well (n = 114 students) and could be replicated in another sample (n = 140), showing excellent reliability estimates for all dimensions. Predictive validity of school grades was excellent for model building but nonexistent for the other two MicroDYN dimensions and for an additional measure of DPS. Implications are discussed.

Complex Problem Solving

Complex problem solving takes place for reducing the barrier between a given start state and an intended goal state with the help of cognitive activities and behavior. Start state, intended goal state, and barriers prove complexity, change dynamically over time, and can be partially intransparent. In contrast to solving simple problems, with complex problems at the beginning of a problem solution the exact features of the start state, of the intended goal state, and of the barriers are unknown. Complex problem solving expects the efficient interaction between the problem-solving person and situational conditions that depend on the task. It demands the use of cognitive, emotional, and social resources as well as knowledge (see Frensch and Funke 1995).

The role of emotions in complex problem-solving

The assumption that positive affect leads to a better performance in simple cognitive tasks has become well established. We address the question whether positive and negative emotions differentially influence performance in complex problem-solving in the same way. Emotions were induced by positive or negative feedback in 74 participants who had to manage a computer-simulated complex problem-solving scenario. Results show that overall scenario performance is not affected, but positive and negative emotions elicit distinguishable problem-solving strategies: Participants with negative emotions are more focused on the seeking and use of information. We discuss methodological requirements for investigating emotion influences in complex and dynamic cognitive tasks.The assumption that positive affect leads to a better performance in simple cognitive tasks has become well established. We address the question whether positive and negative emotions differentially influence performance in complex problem-solving in the same way. Emotions were induced by positive or negative feedback in 74 participants who had to manage a computer-simulated complex problem-solving scenario. Results show that overall scenario performance is not affected, but positive and negative emotions elicit distinguishable problem-solving strategies: Participants with negative emotions are more focused on the seeking and use of information. We discuss methodological requirements for investigating emotion influences in complex and dynamic cognitive tasks.

Negative Correlations between Control Performance and Verbalizable Knowledge: Indicators for Implicit Learning in Process Control Tasks?

Negative correlations between task performance in dynamic control tasks and verbalizable knowledge, as assessed by a post-task questionnaire, have been interpreted as dissociations that indicate two antagonistic modes of learning, one being “explicit”, the other “implicit”. This paper views the control tasks as finite-state automata and offers an alternative interpretation of these negative correlations. It is argued that “good controllers” observe fewer different state transitions and, consequently, can answer fewer post-task questions about system transitions than can “bad controllers”. Two experiments demonstrate the validity of the argument by showing the predicted negative relationship between control performance and the number of explored state transitions, and the predicted positive relationship between the number of explored state transitions and questionnaire scores. However, the experiments also elucidate important boundary conditions for the critical effects. We discuss the implications of these findings, and of other problems arising from the process control paradigm, for conclusions about implicit versus explicit learning processes.

Finite-state automata: Dynamic task environments in problem-solving research.

This paper presents a new research paradigm for analysing human learning in dynamic task environments based on the theory of finite-state automata. Some of the advantages of the approach are outlined. (1) It is possible to design classes of formally well-described dynamic task environments instead of idiosyncratic microworlds that are difficult if not impossible to compare. (2) The approach suggests assumptions about the mental representation of a discrete dynamic system. (3) The finite-state automata formalism suggests systematic and appropriate diagnostic procedures. (4) Using finite-state automata to design dynamic task environments, one does not have to give up the “ecological validity” appeal of computer-simulated scenarios. An experiment on the utility of an external memory support system with system complexity and type of memory support as independent variables is reported to illustrate the application of the formal framework. Systematically derived dependent variables reflect both system knowledge and control performance. The results suggest that the benefits due to the availability of the external aid vary as a function of the complexity of the task. Also, using reaction time measurements, priming phenomena have been found that point to the importance of sequentiality in the representation of discrete systems. It is concluded that the approach, although not entirely new in experimental psychology, awaits further exploration in research on human learning in dynamic task environments and promises to be a stimulating paradigm for both basic and applied research.

Perspectives on Problem Solving in Educational Assessment: Analytical, Interactive, and Collaborative Problem Solving

Problem solving has received broad public interest as an important competency in modern societies. In educational large-scale assessments paper-pencil based analytical problem solving was included first (e.g., Programme for International Student Assessment, PISA 2003). With growing interest in more complex situations, the focus has shifted to interactive problem solving (e.g., PISA 2012) requiring identification and control of complex systems. In the future, collaborative problem solving represents the next step in assessing problem solving ability (e.g., PISA 2015). This paper describes these different approaches to assessing problem solving ability in large-scale assessments considering theoretical questions as well as assessment issues. For each of the three types of problem solving, the definition and understanding of the construct is explained, items examples are shown together with some empirical results, and limitations of the respective approach are discussed. A final discussion centers on the connection of cognitive and differential psychology within educational research and assessment.

Negative affective environments improve complex solving performance

Based on recent affect–cognition theories (Bless et al., 1996; Fiedler, 2001; Sinclair, 1988), the present study predicted and showed a differentiated influence of nice and nasty environments on complex problem solving (CPS). Environments were constructed by manipulating the target value “capital” of a complex scenario: Participants in the nice environment (N=42) easily raised the capital and received positive feedback, whereas those in the nasty environment (N=42) hardly enhanced the capital and got negative feedback. The results showed that nasty environments increased negative and decreased positive affect. The reverse was true for nice environments. Furthermore, nasty environments influenced CPS by leading to a higher information retrieval and a better CPS performance. Surprisingly, the influence of environment on CPS was not mediated through affect (cf. Soldat & Sinclair, 2001), as recent affect–cognition theories suggest. The missing influence of affect and the strong impact of environment are discussed.

Computer-based testing and training with scenarios from complex problem-solving research: Advantages and disadvantages

The use of PC-based simulations for selection and training for jobs which require complex problem solving skills is ever-increasing. This paper gives a short review of such simulations along with a list of advantages and disadvantages of their use. Possible future developments are sketched.