Cognition adaption to AI and overwhelm

Nuerasthenia – nervous exhaustion

“The chief and primary cause of this development and very rapid increase of nervousness is modern civilization, which is distinguished from the ancient by these five characteristics: steam-power, the periodical press, the telegraph, the sciences, and the mental activity of women.” (Beard, 1868, p. vi)

So much has changed since these words were written, and yet so little. As humans, are we destined to resist change to the point of neuroticism, or is it reasonable to be overwhelmed?

 Cognitive shifts in industrial revolutions one, two and three

The First Industrial Revolution, spanning roughly from 1760 to 1840, represented a seismic shift in the organisation of work, society, and human experience. It originated in Britain and rapidly spread across Western Europe and North America, transforming economies that were once dominated by agriculture and handcraft into industrial, mechanised systems of mass production.

At the heart of this transformation were a series of technological innovations. The invention of machines like the spinning jenny, the water frame, and the power loom radically accelerated textile production, while James Watt’s improvements to the steam engine enabled more efficient manufacturing and transport. This era also saw a major expansion in iron production and coal mining, providing the raw materials and energy sources necessary to fuel factories and railways. The factory system, characterised by centralised production and division of labour, replaced the slower, more autonomous cottage industries of the past.

These technological shifts had profound social consequences. Millions of people moved from rural areas into rapidly growing urban centres in search of work. Cities became crowded and often unsanitary, and the traditional rhythms of agrarian life gave way to the rigid schedules of industrial labour. Workers, including women and children, endured long hours in often dangerous conditions for low wages. The rise of the industrial working class prompted the early stirrings of labour movements, as well as increased awareness of social inequality and class divisions.

Economically, the revolution marked a decisive move toward capitalism and wage labour. Wealth is increasingly concentrated in the hands of industrialists, while the working class becomes more dependent on employment for survival. The resulting consumer markets helped to accelerate production and consumption cycles, setting the stage for the global economic systems that dominate today.

Culturally and cognitively, the revolution introduced new forms of discipline, structure, and time consciousness. Historian E. P. Thompson (1967) argued that industrial capitalism imposed a new temporal order based on clock time, displacing the more flexible, task-based time orientation of agrarian societies. Workers had to learn to measure their day not by the completion of a task but by the demands of a schedule, a change that likely affected internal temporal schemas, attentional control, and self-regulation.

From a cognitive psychology perspective, the structure of factory labour demanded a narrowing of attention and a suppression of creative or divergent thinking. Workers repeated the same task for hours on end, leading to what Harry Braverman (1974) later termed deskilling; a loss of autonomy, judgment, and embodied craftsmanship in favour of repetitive, mechanised labour. This may have reduced the need for higher-order executive functions like problem-solving, adaptability, and motor coordination, while increasing cognitive fatigue due to monotony and external pressure.

Moreover, the shift from village-based communities to urban anonymity likely influenced social cognition and identity formation. As Emile Durkheim (1897) observed, the fragmentation of traditional social bonds could lead to anomie, or a sense of normlessness, contributing to psychological distress and alienation. The relational, embedded sense of self tied to land and kinship was replaced by the roles and expectations of industrial society, a transition that had lasting effects on how individuals perceived themselves and others.

The First Industrial Revolution revolutionised minds introducing new demands on attention, discipline, and cognition, restructured social life, and laid the foundation for a new kind of human, one increasingly shaped by external systems of control, technological mediation, and the logic of production.

The Second Industrial Revolution (1870–1914) marked a profound transformation driven by advances in steel production, electricity, chemical engineering, and mass manufacturing. Innovations such as the internal combustion engine, telegraph, and assembly line (notably Ford’s model in 1913) redefined industrial productivity and global connectivity. This era catalysed the rise of corporate capitalism, urban expansion, and consumer culture, while also entrenching new forms of labour standardisation and managerial control. For individuals, the shift from artisanal to mechanised, time-regulated factory work meant increased alienation and task monotony.

The cognitive shift from the First to the Second Industrial Revolution replaced flexible, hands-on problem-solving and community-based work with specialised, fragmented, and machine-focused skills. Attention narrowed to sustained monitoring of repetitive processes, memory moved from rich procedural recall to rote rules and schedules, and time perception was shaped by rigid, clock-driven rhythms. Problem-solving became constrained to troubleshooting within strict protocols, while social cognition adapted to hierarchical factory structures, fostering compliance and coordination with strangers over collaborative, trust-based workshop dynamics.

The Third Industrial Revolution, spanning from the late 1950s to the early 2000s, was characterised by the rise of digital technology, automation, and information systems, catalysed by advances in semiconductors, computing, and telecommunications. This era saw the widespread adoption of personal computers, the internet, and robotic automation, fundamentally altering how information was processed, work was organised, and knowledge was valued (Manuel Castells, 2009; Rifkin, 2011)The shift from industrial production to information-based economies required new cognitive capacities: abstract reasoning, digital literacy, and multitasking across virtual environments. Unlike earlier eras that emphasised routine procedural work, the digital age favoured fluid problem-solving, continuous learning, and adaptive attention (Autor et al., 2003).However, it also introduced cognitive challenges, including information overload, reduced attentional control, and the externalisation of memory and calculation to digital systems(Beilock et al., 2002) . In essence, the Third Industrial Revolution marked a transition from physical to cognitive labour, reshaping the mental architecture of modern work and learning.

Cognitive shifts occurring due to the fourth AI revolution

The Fourth Industrial Revolution (4IR), a term popularised by (Klaus Schwab, 2016), is defined by the convergence of biological, digital, and physical systems, driven by technologies such as artificial intelligence (AI), machine learning, the Internet of things (IoT), robotics, and bioengineering. Unlike prior revolutions which mechanised or digitised specific domains, 4IR integrates intelligence into infrastructure, systems, and daily decision-making, creating a ubiquitous layer of computational agency. Human interaction with information is no longer limited to active retrieval; rather, AI anticipates, curates, and influences cognition in real-time (Klaus Schwab, 2016) This marks a profound epistemological shift: from knowledge production to knowledge orchestration, with humans increasingly in the role of adjudicators rather than generators of information.

The cognitive consequences of this transformation are multifaceted. Empirical studies suggest that reliance on generative AI tools can lead to reduced originality, externalised memory, and attenuated attention spans (Bai et al., 2023)Tasks once requiring deep processing are now offloaded to systems that automate reasoning, summarisation, and decision-making, potentially dulling the development of critical thinking, inductive reasoning, and epistemic vigilance (Singh et al., 2025). Simultaneously, there is an increasing demand for metacognitive regulation, the capacity to monitor, question, and refine one's own thought processes in the presence of powerful algorithmic feedback loops. Paradoxically, while AI expands our cognitive reach, it also fosters cognitive dependency, creating what some call "cognitive debt" an accumulation of underdeveloped mental faculties due to automation’s seductively efficient assistance (Kosmyna et al., n.d.)

Looking forward, the cognitive shifts of the Fourth Industrial Revolution may be even more profound. As AI systems grow increasingly autonomous and embedded in decision environments, human cognition may undergo a reconfiguration akin to extended mind theory (Clark et al., 1998), where boundaries between human and machine cognition dissolve. Cognitive labour may increasingly shift towards judgment under uncertainty, ethical reasoning, and AI alignment, with human roles focused on ensuring the integrity and social responsibility of machine outputs.

In essence, the Fourth Industrial Revolution challenges not only what we do but how we think. It compels a reconceptualisation of cognitive agency, demanding a balance between augmentation and erosion, autonomy and dependency, and ultimately between being informed and being shaped by intelligent systems. Undoubtedly, contemplating the possibilities of the future evokes a profound sense of overwhelm, one that is arguably comparable to the cognitive disruptions experienced during previous industrial revolutions.

From a Human Factors perspective, overwhelm refers to a state where a person experiences a high level of stress, emotional and/or cognitive intensity, leading to a feeling of being unable to function effectively or manage a situation. Overwhelm is characterised by being "flooded" by thoughts, emotions, and physical sensations, often related to a specific problem or situation, making it difficult to think clearly, make decisions, and cope effectively(Too Much, Too Soon: Information Overload, n.d.).

Table 1

Changes in cognitive domain across the four revolutions.

The Fourth Industrial Revolution is driving humanity into an unprecedented state of cognitive overwhelm, echoing patterns seen in every past industrial revolution (G M Beard, 1881; Too Much, Too Soon: Information Overload, n.d.)but at a scale, speed, and reach never before experienced. The relentless pace of technological change, coupled with the constant demands of an always-connected world, creates excessive workloads intensified by dual-task interference, where competing demands on attention reduce accuracy and efficiency(Alami et al., 2025). Digital platforms amplify cognitive overload by flooding individuals with more information than can be processed, evaluated, or acted upon effectively, while rapid organisational shifts, algorithmic decision-making, and technology-induced job uncertainty introduce unpredictability and erode the sense of control. Emotionally intense experiences compound these effects, leading to lapses in focus, increased errors, and poorer decision-making, with chronic overwhelm ultimately threatening both mental and physical health.

Table 2

Potential positive and negative effects of AI on cognition.

The Fourth Industrial Revolution (4IR) is reshaping cognitive work in ways that frequently exceed human limits, creating an unprecedented risk of mental overload and compromised wellbeing. Human factors literature has long warned that when task demands outstrip cognitive resources, mental workload rises to unsustainable levels, resulting in degraded performance and heightened psychological strain (Wickens, 2008; Hart & Staveland, 1988). In 4IR workplaces, AI and automation promise to ease cognitive burdens, but paradoxically leave humans to handle the ambiguous, unpredictable, and high-risk decisions that machines cannot resolve (Lee & See, 2004)This elevates attentional demands and increases mental effort, producing a mismatch between technological pace and cognitive capacity. Dual-task interference compounds the problem: workers are expected to simultaneously monitor AI outputs, assess contextual relevance, and engage in human-to-human interactions, forcing rapid task-switching that introduces reaction time delays, higher error rates, and cognitive fatigue(Squire & Parasuraman, 2010) Over time, this cycle undermines productivity, situational awareness, and the psychological resources needed for sustained wellbeing.

Table 3

The evolution of cognition type by revolution

Wellbeing research supports these findings, showing that high cognitive load, fragmented attention, and low perceived control are strongly linked to stress, anxiety, and burnout(Calvo et al., 2020). Beyond pure cognition, 4IR environments introduce emotional and social strain. Algorithmic management and constant monitoring reduce autonomy, while AI-mediated interactions weaken human connection, a key component of wellbeing (Naswell, K.; Wong, J, Malinen, 2021). Emotional labour remains high, with healthcare, education, and customer-facing roles now demand empathy for humans and vigilance for machines simultaneously, creating dual-channel cognitive-affective load (Wickens, 2002)Chronic exposure to these conditions reduces intrinsic motivation, disrupts flow states, and undermines eudaimonic wellbeing the sense of purpose and flourishing that sustains long-term performance (Jörs & De Luca, n.d.).

Figure 1. The Cognitive Collapse Curve - The tipping point where human mental capacity buckles under machine-paced demands.

What’s the point of all this reflection? Perhaps it’s simply to recognise that we are living through the Fourth Industrial Revolution, and to take comfort in knowing that every previous revolution felt equally overwhelming, just as G. M. Beard described in the 19th century. AI is undeniably complex, often beyond our ability to fully comprehend, but then again, most of us don’t truly understand how our cars work, or how the internet functions, yet we use them and trust them every day.

We certainly need to design strategy and AI infrastructure to support cognitive capacity. As the cognitive collapse curve shows, we are not cognitively capable of operating under certain dual task conditions, workload and complexity levels. The hope is our leaders understand this and develop work for humans, (assuming they want to keep humans in work).

The question is whether this will become something we eventually look back on as another phase we adapted to, with human resilience, or whether it will be an unending wave of change, tilting towards either the utopian visions or dystopian warnings of science fiction. History suggests it’s more likely to land somewhere in between: not catastrophic, not utopian, but messy, imperfect, and full of lessons learned. The key is to consciously apply what we already know, our frameworks for resilience, managing stress, navigating change, and protecting wellbeing. The challenge is ensuring enough people understand and use these tools, and in doing so, bringing the conversation back to people.

References

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