Three experiments tested whether a modified version of the Clustered Conceptual Span task (H. J. Haarmann, E. J. Davelaar, & M. Usher, 2003), which ostensibly requires active maintenance of semantic representations, predicted individual differences in higher-order cognitive abilities better than short-term memory (STM) span tasks or nonsemantic versions of the "Conceptual" task did. Nonsemantic Conceptual tasks presented short word lists clustered by color, first letter, or initial vowel sound, and cued subjects to recall only 1 of 3 clusters from each list; the Semantic task clustered words by taxonomic category. The Semantic Conceptual task generally failed to predict incremental variance in either verbal abilities or general fluid intelligence beyond the other Conceptual tasks or STM span tasks. Although the Semantic task showed a stronger relation to working memory span tasks than did the nonsemantic tasks (Experiment 3), that stronger relation did not translate into strong prediction of cognitive individual differences.

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Nuclear power has a crucial role in providing safe, reliable, and economical carbon-free electricity for today and the future. For continued operation, many of the existing United States nuclear power plants will begin the subsequent license renewal process for extending their operating license periods. As plants extend their expected operating lifetimes, there is a significant opportunity to modernize. These plants have a much stronger business case with these extended mission periods to modernize and significantly enhance their economic viability in current and future energy markets by implementing digital technologies that support innovation, efficiency gains, and business-model transformation.Ensuring continued safety and reliability is crucial. Transformative digital technologies-including automation-that fundamentally change the concept of operation for the nuclear power plant operating models requires a critical focus on the human-technology integration element. Further, the nuclear industry has historically been reluctant to modernize due to a risk-adverse culture and lack of clarity for a transformative new-state vision. Common barriers include the perceived value and return on investment of digital technology; the perceived risk associated with licensing, regulatory, and cybersecurity; and insufficient guidance for performing digital modifications to power generation systems.This work presents a methodology to address these barriers and support the industry in adopting advanced automation and digital technology through developing a transformative vision and implementation strategy that will address the human-technology integration element. This research leverages previous Light Water Reactor Sustainability (LWRS) Program and industry results. It draws specifically on previous LWRS Program research in the areas of advanced alarm systems, computer-based procedures, model-informed decision support, and advanced human-system interface displays (e.g., overviews and task-based). The modernization methodology can be used to guide transformative thinking when integrating a set of vendor-specific capabilities to support a new concept of operations and a utility's end-state vision.The results of this research are organized into six major sections: − Section 1 introduces the need for supporting large-scale digital modifications that will renew the technology base for extended operating life beyond 60 years.− Section 2 describes the challenges that the nuclear industry is enduring with modernizing.− Section 3 summarizes the primary standards and guidance.− Section 4 presents earlier work from the LWRS Program regarding the development of a transformative conceptual design for an advanced control room of a hybrid plants.− Section 5 presents a methodology that is designed to address the challenges in the industry today in achieving a transformative newstate vision and concept of operations.

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