Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Müller, Romy; Kessler, Franziska; Humphrey, David W.; Rahm, Julian

doi:10.3390/fi13060156

Cited by 6 publications

(3 citation statements)

References 187 publications

(203 reference statements)

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“…To bridge these gaps, it would be interesting to investigate how presentation formats influence the point at which people stop searching for solutions in Adapt/Exchange scenarios. This is a relevant question in modular plants, because digital transformation allows making previous solutions available in large databases that can be searched when a similar problem reoccurs [ 44 ]. In such case-based reasoning, the selection of suitable solutions is a major challenge [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format

Müller

2024

Behavioral Sciences

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Problem solvers often need to choose between adapting a current solution and exchanging it for a new one. However, previous studies have not considered how such decisions might depend on structural and surface features of the task. Therefore, the present study investigated the interplay between the costs of the two solutions (a structural feature) and the format in which this information is presented (a surface feature). In a computer-based modular plant scenario, participants chose between modifying process parameters (Adapt) and reconfiguring the module setup (Exchange). The solution costs were presented either as graphs depicting parameter relations, separate numbers for each parameter, or integrated numbers for each solution. It was hypothesised that graphs induce satisficing (i.e., basing decisions only on Adapt), whereas the numeric formats foster a comparison of the solutions (i.e., basing decisions on the Adapt/Exchange ratio). The hypothesised effects were restricted to situations with medium Adapt costs. A second experiment replicated these findings while adjusting the scale of the numeric formats. In conclusion, Adapt/Exchange decisions are shaped by an interaction of structural and surface features of the task. These findings contribute to a more detailed understanding of the influences on decision strategies in complex scenarios that require a balance between stability and flexibility.

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Section: Discussionmentioning

confidence: 99%

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format

Müller

2024

Behavioral Sciences

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“…We therefore argue that the abstraction hierarchy could be used to guide the modeling activities in Systems Engineering. Other technologies, such as the description of production environments with ontologies, may also be able to provide a common ground for integrating information from different perspectives (Müller et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Schmidt

Müller

2023

Hum Ftrs & Erg Mfg Svc

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Interdisciplinary collaboration frequently comes into play when existing problems cannot be solved by one discipline alone. However, the interlocking of contributions from different disciplines is by no means trivial. This exploratory study examines one foundation of successful teamwork, namely shared mental models. To this end, we compared the contents of mental models between members of different but interdependent disciplines who collaboratively solve knowledge‐intensive, creative tasks. Five automation and five mechanical engineers were recruited from a company that produces packaging machines. In semi‐structured interviews, participants reported their approach to evaluating the process behavior of a packaging machine, and their mental models were represented in concept maps. Quantitative analyses revealed that the maps of automation engineers were smaller than those of mechanical engineers. In qualitative analyses, the focus on different levels of abstraction and on contents from the two disciplines was examined. Automation engineers represented a large proportion of rather abstract machine functions, whereas mechanical engineers additionally represented the physical implementation of these functions. The disciplinary focus also differed in the sense that automation engineers mainly attended to automated machine processes, while mechanical engineers attended to both mechanical and automated processes. Overall, automation engineers' focus was narrower than that of mechanical engineers. We explain these results by considering typical tasks and reasoning processes in both disciplines, and discuss how shared mental models can aid the integration of different disciplinary perspectives, for instance, during Systems Engineering.

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“…High MC EV provides greater clarity for the user and tends to be easier to comprehend by reducing his cognitive load but at the cost of faithfully depicting the model's behavior. On the other hand, low MC EV might cause humans to misuse or ignore the explanation altogether by giving them too much information to process (Sundararajan et al, 2019;Müller et al, 2021). This concept of information overload (i.e., too much available information or too much high-quality information) has been shown to decrease the user's decision effectiveness and can even make the user less confident about their decision (Keller and Staelin, 1987;Müller et al, 2021).…”

Section: Hypothesis Development and Research Modelmentioning

confidence: 99%

Designing for Confidence: The Impact of Visualizing Artificial Intelligence Decisions

et al. 2022

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Explainable artificial intelligence aims to bring transparency to artificial intelligence (AI) systems by translating, simplifying, and visualizing its decisions. While society remains skeptical about AI systems, studies show that transparent and explainable AI systems can help improve the Human-AI trust relationship. This manuscript presents two studies that assess three AI decision visualization attribution models that manipulate morphological clarity (MC) and two information presentation-order methods to determine each visualization’s impact on the Human-AI trust relationship through increased confidence and cognitive fit (CF). The first study, N = 206 (Avg. age = 37.87 ± 10.51, Male = 123), utilized information presentation methods and visualizations delivered through an online experiment to explore trust in AI by asking participants to complete a visual decision-making task. The second study, N = 19 (24.9 ± 8.3 years old, Male = 10), utilized eye-tracking technology and the same stimuli presentation methods to investigate if cognitive load, inferred through pupillometry measures, mediated the confidence-trust relationship. The results indicate that low MC positively impacts Human-AI trust and that the presentation order of information within an interface in terms of adjacency further influences user trust in AI. We conclude that while adjacency and MC significantly affect cognitive load, cognitive load alone does not mediate the confidence-trust relationship. Our findings interpreted through a combination of CF, situation awareness, and ecological interface design have implications for the design of future AI systems, which may facilitate better collaboration between humans and AI-based decision agents.

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Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Cited by 6 publications

References 187 publications

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Designing for Confidence: The Impact of Visualizing Artificial Intelligence Decisions

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