This study formalizes the concept of national cognitive resilience as the capacity of a country to preserve and regenerate metacognitive friction, epistemic novelty density, and human interpretive effort despite increasing AI integration. In the AI era, the boundaries between thinking and automation, learning and consumption, and originality and replication are quietly dissolving. The silent consequence is GCA, the recursive weakening of human epistemic agency through sustained reliance on AI-mediated knowledge production. This erosion is not accidental; it arises from design logics that privilege frictionless efficiency and predictive optimization over ambiguity, novelty, and reflection.

The results show clearly that AI readiness does not equate to cognitive resilience. Nations with advanced AI infrastructure, such as China and the United States, may record low CDI scores when novelty is eroded and automation reliance is high. Conversely, countries like Singapore, and Finland outside this dataset, sustain high CDI scores by embedding cognitive scaffolding into their educational and governance systems, even with similar AI maturity levels. Historical precedents such as the Gutenberg press, industrial schooling, and the screen revolution demonstrate that cognitive systems can recover from compression and conformity, but only through deliberate pedagogical reform and institutional innovation.

The broader implication is that cognition must be treated as a civilizational asset, equal in strategic importance to ecological sustainability or democratic stability. Just as environmental degradation prompted coordinated global climate action, cognitive degradation demands epistemic governance that is transdisciplinary, generational, and anticipatory. Embedding GCAL–CDI metrics into AI policy, designing reflective human–AI interfaces, and prioritizing epistemic plurality over algorithmic conformity are essential to achieving this. Artificial intelligence need not be inherently corrosive.

Lisanne Bainbridge's 1983 paper, the Ironies of Automation (Bainbridge 1983), was a telling and prescient summary of the many challenges that arise from automation. She pointed out the ways in which automation, paradoxically, make the human's job more crucial and more difficult, rather than easier and less essential as so many engineers believe. Not only does automation introduce new design errors into the control of systems, but it creates very different jobs that have many new problems, with the result that people may be less able to perform when needed. They need to be more skilled to understand and operate the automation, while simultaneously the automation leads to skill atrophy. Additional system complexity is introduced as well as vigilance problems that interfere with peoples' ability to oversee the automation. And while manual workload may be decreased much of the time, cognitive workload is often increased at critical times.

Transcranial Low-Intensity Focused Ultrasound (LIFU) offers unique opportunities for precisely neuromodulating small and/or deep targets within the human brain, which may be useful for treating psychiatric and neurological disorders. This paper presents a novel ultrasound system that delivers focused ultrasound through the forehead to anterior brain targets and evaluates its safety and usability in a volunteer study. ... The presented system was successfully used to safely deliver LIFU through the forehead to the amPFC in all volunteers, and was well-tolerated. With the capabilities validated here and positive results of the study, this technology appears well-suited to explore LIFU’s efficacy in clinical neuromodulation contexts.

Lisanne Bainbridge's 1983 paper, the Ironies of Automation (Bainbridge 1983), was a telling and prescient summary of the many challenges that arise from automation. She pointed out the ways in which automation, paradoxically, make the human's job more crucial and more difficult, rather than easier and less essential as so many engineers believe. Not only does automation introduce new design errors into the control of systems, but it creates very different jobs that have many new problems, with the result that people may be less able to perform when needed. They need to be more skilled to understand and operate the automation, while simultaneously the automation leads to skill atrophy. Additional system complexity is introduced as well as vigilance problems that interfere with peoples' ability to oversee the automation. And while manual workload may be decreased much of the time, cognitive workload is often increased at critical times.

Lisanne Bainbridge's 1983 paper, the Ironies of Automation (Bainbridge 1983), was a telling and prescient summary of the many challenges that arise from automation. She pointed out the ways in which automation, paradoxically, make the human's job more crucial and more difficult, rather than easier and less essential as so many engineers believe. Not only does automation introduce new design errors into the control of systems, but it creates very different jobs that have many new problems, with the result that people may be less able to perform when needed. They need to be more skilled to understand and operate the automation, while simultaneously the automation leads to skill atrophy. Additional system complexity is introduced as well as vigilance problems that interfere with peoples' ability to oversee the automation. And while manual workload may be decreased much of the time, cognitive workload is often increased at critical times.

Lisanne Bainbridge's 1983 paper, the Ironies of Automation (Bainbridge 1983), was a telling and prescient summary of the many challenges that arise from automation. She pointed out the ways in which automation, paradoxically, make the human's job more crucial and more difficult, rather than easier and less essential as so many engineers believe. Not only does automation introduce new design errors into the control of systems, but it creates very different jobs that have many new problems, with the result that people may be less able to perform when needed. They need to be more skilled to understand and operate the automation, while simultaneously the automation leads to skill atrophy. Additional system complexity is introduced as well as vigilance problems that interfere with peoples' ability to oversee the automation. And while manual workload may be decreased much of the time, cognitive workload is often increased at critical times.

Lisanne Bainbridge’s 1983 paper, the Ironies of Automation (Bainbridge 1983), was a telling and prescient summary of the many challenges that arise from automation. She pointed out the ways in which automation, paradoxically, make the human’s job more crucial and more difficult, rather than easier and less essential as so many engineers believe. Not only does automation introduce new design errors into the control of systems, but it creates very different jobs that have many new problems, with the result that people may be less able to perform when needed. They need to be more skilled to understand and operate the automation, while simultaneously the automation leads to skill atrophy. Additional system complexity is introduced as well as vigilance problems that interfere with peoples’ ability to oversee the automation. And while manual workload may be decreased much of the time, cognitive workload is often increased at critical times.

Automated Teller Machines (ATMs) represent the most used system for withdrawing cash. The European Central Bank reported more than 11 billion cash withdrawals and loading/unloading transactions on the European ATMs in 2019. Although ATMs have undergone various technological evolutions, Personal Identification Numbers (PINs) are still the most common authentication method for these devices. Unfortunately, the PIN mechanism is vulnerable to shoulder-surfing attacks performed via hidden cameras installed near the ATM to catch the PIN pad. To overcome this problem, people get used to covering the typing hand with the other hand. While such users probably believe this behavior is safe enough to protect against mentioned attacks, there is no clear assessment of this countermeasure in the scientific literature. This paper proposes a novel attack to reconstruct PINs entered by victims covering the typing hand with the other hand. We consider the setting where the attacker can access an ATM PIN pad of the same brand/model as the target one. Afterward, the attacker uses that model to infer the digits pressed by the victim while entering the PIN. Our attack owes its success to a carefully selected deep learning architecture that can infer the PIN from the typing hand position and movements. We run a detailed experimental analysis including 58 users. With our approach, we can guess 30% of the 5-digit PINs within three attempts — the ones usually allowed by ATM before blocking the card. We also conducted a survey with 78 users that managed to reach an accuracy of only 7.92% on average for the same setting. Finally, we evaluate a shielding countermeasure that proved to be rather inefficient unless the whole keypad is shielded.