Cognitive assemblages redefine how humans think in algorithmic age

Algorithms now determine what billions of people read, watch, buy and even believe. From search engines to predictive analytics and large language models (LLMs), artificial intelligence (AI) systems are shaping attention, filtering information and structuring decision-making at a scale never seen before.

That transformation is examined in Cognitive Assemblages: Living with Algorithms, published in the journal Big Data and Cognitive Computing. In the study, the author argues that cognition itself has become distributed across humans, machines and institutions, creating hybrid systems in which thinking is no longer confined to individual minds.

From individual minds to distributed cognition

Classical Enlightenment thought focused on the autonomous rational subject, capable of independent reasoning and self-governance. Over time, this view was challenged by theories that recognized cognitive limits and environmental dependence.

Herbert Simon's theory of bounded rationality showed that human decision-making is constrained by limited information and computational capacity. Later frameworks such as the extended mind thesis and distributed cognition theory argued that cognition is supported by tools, symbols, and social interaction. the author situates contemporary AI systems within this intellectual trajectory, arguing that algorithms now function as active cognitive partners rather than passive instruments.

Unlike earlier tools such as notebooks or calculators, modern AI systems are adaptive and recursive. Machine learning models update themselves through feedback loops, processing data volumes far beyond human cognitive capacity. As a result, algorithmic systems do not merely store or retrieve information; they classify, prioritize, and generate knowledge.

This creates what the study describes as epistemic asymmetry. While human cognition remains bounded and sequential, algorithms operate in parallel across massive datasets. Knowledge increasingly emerges from computational inference rather than direct human deliberation. Recommendation systems shape cultural exposure, predictive policing models influence risk assessments, and language models assist in drafting text and summarizing research.

The author argues that these systems form cognitive assemblages in which agency is distributed. Decision-making outcomes arise not from isolated individuals but from interactions among users, data streams, algorithmic models, and institutional frameworks. Cognition becomes relational and ecological rather than strictly individual.

The study revisits Daniel Kahneman's dual-system model of cognition, which distinguishes between fast intuitive processes and slower deliberative reasoning. the author proposes adding a new layer, sometimes described as "System 0," representing algorithmic mediation that precedes and structures both intuitive and reflective thought. In this architecture, algorithmic systems pre-filter information and shape cognitive environments before individuals engage in conscious reasoning.

The algorithmic condition and its political implications

The study discusses the concept of algorithmic condition - a term that captures the pervasive integration of algorithms into social, economic, and political life. Search rankings influence public discourse. Social media feeds shape attention. Automated credit scoring systems determine financial access. Predictive analytics guide resource allocation in public services.

In this condition, autonomy can no longer be understood as purely self-generated reasoning. If cognitive environments are pre-structured by algorithmic systems, the boundaries of rational choice shift. Decision spaces are shaped before individuals consciously evaluate options.

This shift challenges classical liberal conceptions of responsibility and agency. If decisions are co-produced by human and machine components, accountability becomes more complex. The author suggests that agency should be reconceptualized as relational, emerging from interactions within assemblages rather than residing solely within individuals.

Based on both Western and East Asian philosophical traditions, the paper proposes moving beyond an individualistic model of autonomy. Concepts rooted in relational ethics and interdependence offer alternative ways to understand responsibility in algorithmically mediated environments. Governance, in this view, becomes less about asserting control over machines and more about shaping feedback systems and institutional structures.

Early cybernetic thinkers emphasized feedback and regulation in mechanical and biological systems. Later work in complex systems theory recognized that observers are embedded within the systems they study. Today's cyber–physical–social systems extend this integration, combining computational, physical, and human components into unified infrastructures.

Examples include planetary monitoring platforms that integrate satellites, sensors, AI models, and policy institutions. Urban governance systems incorporate real-time data streams to manage traffic, energy distribution, and public safety. Digital platforms orchestrate social interaction and economic exchange through continuous algorithmic modulation.

These infrastructures function as cognitive assemblages at scale. They produce collective situational awareness and coordinate action across distributed networks. Yet they also centralize power and create vulnerabilities.

Risks, opportunities, and the future of cognitive symbiosis

The paper depicts humans and algorithms as operating within a tightly coupled cognitive relationship. Humans shape algorithms through data input, design choices, and regulatory frameworks. In turn, algorithms reshape human perception, behavior, and institutional processes. Over time, these systems co-adapt, forming tightly coupled cognitive ecosystems.

This symbiosis offers significant opportunities. Distributed cognition can enhance collective intelligence, enabling faster responses to complex challenges such as climate change, public health crises, and supply chain disruptions. Large-scale data integration allows policymakers to model scenarios and anticipate systemic risks with unprecedented precision.

Tightly integrated cognitive assemblages simultaneously introduce new forms of fragility. Algorithmic bias can propagate through decision systems at scale. Manipulation and misinformation can be amplified by recommendation engines. Infrastructure failures can cascade across interconnected networks.

The paper highlights the importance of governance mechanisms capable of managing these risks. Transparency, participatory oversight, and institutional innovation are presented as essential components of responsible algorithmic integration. Instead of treating AI as an external threat or a neutral tool, policymakers must recognize it as an embedded cognitive partner.

The study also addresses debates about machine consciousness. While distinguishing between functional and phenomenal consciousness, the author argues that advanced AI systems may simulate many functional aspects of awareness. Even if subjective experience remains biologically grounded, the practical consequences of interacting with highly capable artificial agents reshape human self-understanding.

The study calls for a shift in both philosophical perspective and policy design. Preserving democratic accountability and pluralism in the algorithmic condition will require rethinking autonomy, responsibility, and governance within distributed cognitive systems.