Common Process Demands of Two Complex Dynamic Control Tasks: Transfer Is Mediated by Comprehensive Strategies.

Wolfgang Schoppek,Andreas Fischer

doi:10.3389/fpsyg.2017.02145

Wolfgang Schoppek, Andreas Fischer

Open Access

https://doi.org/10.3389/fpsyg.2017.02145

Copy DOI

Abstract

Although individual differences in complex problem solving (CPS) are well–established, relatively little is known about the process demands that are common to different dynamic control (CDC) tasks. A prominent example is the VOTAT strategy that describes the separate variation of input variables (“Vary One Thing At a Time”) for analyzing the causal structure of a system. To investigate such comprehensive knowledge elements and strategies, we devised the real-time driven CDC environment Dynamis2 and compared it with the widely used CPS test MicroDYN in a transfer experiment. One hundred sixty five subjects participated in the experiment, which completely combined the role of MicroDYN and Dynamis2 as source or target problem. Figural reasoning was assessed using a variant of the Raven Test. We found the expected substantial correlations among figural reasoning and performance in both CDC tasks. Moreover, MicroDYN and Dynamis2 share 15.4% unique variance controlling for figural reasoning. We found positive transfer from MicroDYN to Dynamis2, but no transfer in the opposite direction. Contrary to our expectation, transfer was not mediated by VOTAT but by an approach that is characterized by setting all input variables to zero after an intervention and waiting a certain time. This strategy (called PULSE strategy) enables the problem solver to observe the eigendynamics of the system. We conclude that for the study of complex problem solving it is important to employ a range of different CDC tasks in order to identify components of CPS. We propose that besides VOTAT and PULSE other comprehensive knowledge elements and strategies, which contribute to successful CPS, should be investigated. The positive transfer from MicroDYN to the more complex and dynamic Dynamis2 suggests an application of MicroDYN as training device.

Highlights

Complex problem solving (CPS) is a phenomenon that is investigated in many domains, ranging from scientific discovery learning over industrial process control to decision making in dynamic economical environments
Our hypotheses are supported by the data: Performance in MicroDYN explains a unique proportion of variance in Dynamis2
We found positive transfer from MicroDYN to Dynamis2, but not in the opposite direction

Summary

Introduction

Complex problem solving (CPS) is a phenomenon that is investigated in many domains, ranging from scientific discovery learning over industrial process control to decision making in dynamic economical environments. Research on CPS is dominated by attempts to construe it as one-dimensional ability construct, which means that a single measure represents a person’s ability to solve complex problems. To this end, Greiff and Funke (2010) and Greiff et al (2012) have developed the minimal complex systems test MicroDYN. MicroDYN yields reliable measures of knowledge acquisition and knowledge application (Fischer et al, 2015a). As both variables are highly correlated, they are often combined to obtain a measure of CPS ability (e.g., Greiff and Fischer, 2013). The typical result of these studies is that the combined CPS measure accounts for 5% variance in school grades incremental to figural reasoning (Schoppek and Fischer, 2015)

Objectives

Methods

Results

Conclusion