The ability to recognize key causal models across situations is associated with expertise. The acquisition of schema-governed category knowledge of key causal models may underlie this ability. In an experimental study (n = 183), we investigated the effects of promoting the construction of schema-governed categories and how an enhanced ability to recognize the key causal models relates to performance in complex problem-solving tasks that are based on the key causal models. In a 2 × 2 design, we tested the effects of an adapted version of an intervention designed to build abstract mental representations of the key causal models and a tutorial designed to convey conceptual understanding of the key causal models and procedural knowledge. Participants who were enabled to recognize the underlying key causal models across situations as a result of the intervention and the tutorial (i.e., causal sorters) outperformed non-causal sorters in the subsequent complex problem-solving task. Causal sorters outperformed the control group, except for the subtask knowledge application in the experimental group that did not receive the tutorial and, hence, did not have the opportunity to elaborate their conceptual understanding of the key causal models. The findings highlight that being able to categorize novel situations according to their underlying key causal model alone is insufficient for enhancing the transfer of the according concept. Instead, for successful application, conceptual and procedural knowledge also seem to be necessary. By using a complex problem-solving task as the dependent variable for transfer, we extended the scope of the results to dynamic tasks that reflect some of the typical challenges of the 21st century.

The ability to read, understand, and comprehend visual information representations is subsumed under the term visualization literacy (VL). One possibility to improve the use of information visualizations is to introduce adaptations. However, it is yet unclear whether people with different VL benefit from adaptations to the same degree. We conducted an online experiment (n = 42) to investigate whether the effect of an adaptation (here: De-Emphasis) of visualizations (bar charts, scatter plots) on performance (accuracy, time) and user experiences depends on users' VL level. Using linear mixed models for the analyses, we found a positive impact of the De-Emphasis adaptation across all conditions, as well as an interaction effect of adaptation and VL on the task completion time for bar charts. This work contributes to a better understanding of the intertwined relationship of VL and visual adaptations and motivates future research.

To leverage the full potential of cyber-physical production systems (CPPS) in terms of flexibility and adaptability, the development of such systems must go beyond digitization and modularization. During the engineering and operation of CPPS, humans are essential enablers for the system’s changeability. In this paper, we propose a model of an iterative conducive design process that incorporates perspectives and competencies from several research disciplines such as process control, industrial engineering, computer science, and instructional and cognitive psychology. The goal of this approach is to enhance human-machine interaction and to realize efficient functioning of the system via a combination of the unique potentials provided by humans and the system. The proposed iterative approach is exemplified on a practical level in the engineering of a demonstration plant that tests safety systems for modular plants.

Author(s): Kessler, Franziska; Proske, Antje; Goldwater, Micah; Urbas, Leon; Greiff, Samuel; Narciss, Susanne | Abstract: Goldwater and Gentner (2015) showed that the sensitivity for causal structures can be promoted with an intervention combining explication of causal models and guided structural alignment of situations from disparate fields with the same underlying causal model. We extended this intervention with inference questions and combined it with a subsequent complex problem-solving (CPS) task, in order to investigate whether enhanced sensitivity for causal structures results in better performance in CPS. This study (N = 108) compares the CPS performance indicators knowledge acquisition and knowledge application among three experimental groups (intervention, intervention extended with inference questions, control group) and reveals the following results: 1) The effectiveness of the intervention in increasing the sensitivity for causal structures was replicated. 2) Sensitivity for causal structures and CPS performance indicators were significantly positively correlated. 3) There is no direct effect of the intervention on CPS performance, but an indirect-only effect via enhanced sensitivity.

Hazard & Operability (HAZOP) studies are a common expert-driven brainstorming methodology to analyze the safety of systems. In this paper, we propose an approach to integrate the content of HAZOP studies of (apparatus and process) by extracting and disclosing the causal structures implied in the HAZOPs. Thus, a methodology for data compression and integration of several HAZOP studies is supposed, resulting in a comprehensible summary of the underlying causal relationships withing the current system set-up. The resulting diagrams constitute a summary of the essential HAZOP studies by disclosing and visualizing the internal causal structures and are hence designed to facilitate interpretations and deductions performed by human operators and support machine-readability. The application of the methodology is implemented in the import of HAZOP-tables to Resource Description Framework (RDF). Using this formalized structure, the export to their original format as table is supported as well, so that the transformation can be conducted without the loss of information in both directions.

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems.

The increased flexibility resulting from the modularization of process plants requires an advanced strategy to supervise the distributed control systems (DCSs). The connection and coordination of multiple process equipment assemblies (PEAs) into a modular plant (MP) is called orchestration and conducted in a so-called process orchestration layer (POL). Standardized interfaces enable a seamless integration of PEAs into an MP. Current process control systems do not yet meet all required features regarding to interfaces and possibility of import/export of different data structures. Several aspects are presented which shall be taken into account when designing a POL. The POL's several layers are described in detail, resulting especially in flexibility and reusability of modular plants.