Sep 2026· Frontiers in Cognition· Vol 5· 45 references
Abstract
Scientific writing has historically been conceptualized as a communicative act—the registration of knowledge elaborated through independent cognitive processes. This article challenges that assumption and proposes that scientific writing constitutes an active neurocognitive system of knowledge construction and a privileged domain for metacognitive self-regulation in advanced learning contexts. We present the Integrated Neuropsychological Model of Scientific Writing (MNIEC), a theoretical framework integrating four neurocognitive systems—the Constructive Executive System (CES), the Extended Working Memory System (EWMS), the Semantic Simulation System (SSS), and the Epistemological Metacognitive Monitoring System (EMMS)—within a unified functional architecture. The central mechanism is the Epistemological Construction Cycle (ECC), a recursive five-phase process—conceptual activation, working memory simulation, symbolic externalization, metacognitive evaluation, and conceptual reorganization—through which scientific writing can function as an active mechanism of knowledge construction, particularly when recursive metacognitive evaluation and conceptual reorganization are engaged. The model integrates evidence from executive function neuropsychology, working memory architecture, large-scale brain network dynamics, epistemological metacognition, and predictive coding, grounded in five nuclear principles. Four testable hypotheses and experimental paradigms are derived. Implications are discussed for metacognitive training in scientific education, academic impostor syndrome, and the cognitive impact of large language models. The MNIEC proposes a neuropsychological framework for a systematic research agenda on scientific writing as metacognitive knowledge construction.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
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