Neurotechnological Natives and the Neurotechnology Shift: A Research Agenda for the Brain-as-Interface Era

José M. Muñoz¹, ²

¹ International Neurotechnology Governance Observatory (INEGOV), International Center for Neuroscience and Ethics (CINET), Tatiana Foundation, Madrid, Spain

² CEU Human-Centered Neurotechnology Hub, Universidad Cardenal Herrera-CEU, CEU Universities, Alfara del Patriarca, Spain

Correspondence: José M. Muñoz (jose.munozortega@colaborador.ceu.es)

Received: 16 March 2026  Revised: 30 June 2026  Accepted: 2 July 2026

Keywords: brain-as-interface | cognitive liberty | direct-to-consumer neurotechnologies | neurodata | neurotechnological natives | neurotechnologies |

neurotechnology shift

ABSTRACT

Neurotechnologies are increasingly moving beyond clinical and laboratory environments into a growing range of everyday contexts, including consumer markets, workplaces, and educational settings. This article presents the concept of the neurotech-nology shift , understood as a technoscientific, cognitive, and sociocultural transformation through which neurotechnologies progressively expand into daily life, turning the brain itself into a shared interface between biological cognition and digital systems. This condition may eventually give rise to neurotechnological natives , a concept that is introduced here as a heuristic hypothesis intended to guide interdisciplinary research. Rather than predicting the emergence of a homogeneous generation shaped by neurotechnologies, the concept functions as an analytical tool to structure inquiry into the implications of brain– technology interaction in everyday contexts. To this end, the article outlines a research agenda organized across three dimensions of the neurotechnology shift: technoscientific infrastructures enabling scalable brain–technology interaction and neurodata ecosystems; cognitive implications related to neural plasticity, identity, impulse control, and language processes; and sociocultural transformations concerning governance, inequality and access, and collective imaginaries. Understanding the dynamics and bidirectional interactions across these three dimensions is essential for anticipating the ethical, legal, and societal implications of the emerging brain-as-interface era.

1 | Introduction

People talking without speaking People hearing without listening People writing songs that voices never share The Sound of Silence, by Paul Simon

Neurotechnologies—tools used to monitor and/or manipulate the nervous system’s structure or activity—are increasingly mov-ing beyond clinical and laboratory environments into a growing

range of everyday contexts. Advances in brain–computer inter-faces (BCIs), wearable electroencephalography (EEG) systems, noninvasive brain stimulation devices, and emerging brain-to-text technologies illustrate a broader technological trajectory in which interaction with neural processes is no longer confined to specialized medical settings. At the same time, the integration of artificial intelligence (AI) into neurodata processing is rapidly expanding the capacity to interpret, predict, and act upon neural activity. Although many of these systems remain experimental or limited in scope, their rapid development signals an important transition in how technologies engage with the human brain and mind.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2026 The Author(s). Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of The New York Academy of Sciences.

Annals of the New York Academy of Sciences , 2026; 1562:e70381https://doi.org/10.1111/nyas.70381

Historically, digital technologies have shaped cognition primar- ily through mediated interaction—keyboards, screens, smart- phones, and online platforms that influence how individuals communicate, remember, and process information. Nevertheless, neurotechnologies introduce a qualitatively different mode of interaction: rather than influencing cognition indirectly through digital environments, they increasingly seek to measure, decode, or modulate neural activity itself. This emerging technological condition suggests a shift in which the brain is progressively treated not only as a biological organ but also as a shared bound- ary between biological cognition and technological systems.

This article describes this transformation as the neurotechnology shift .¹ The shift can be understood as unfolding across three inter- related dimensions. First, a technoscientific dimension, driven by advances in portable neurotechnologies, minimally invasive neural interfaces, and the integration of AI into neural signal analysis. Second, a cognitive dimension, as sustained interaction with neurotechnological systems may influence neural plasticity, self-modeling, impulse control, and language processes. Third, a sociocultural dimension, as neurotechnologies increasingly inter- sect with settings such as workplaces, educational environments, consumer markets, and digital infrastructures.

If these transformations continue to expand across everyday environments, they may also generate new populational and generational dynamics. Just as the concept of “digital natives”was proposed to explore how growing up in a digitally mediated society might influence cognitive and social development, it becomes meaningful to consider the possibility of neurotechnolog- ical natives —individuals whose cognitive and social development unfolds in environments where they interact with neurotech- nologies in everyday activities. Within the broader context of the neurotechnology shift, this concept functions not as a prediction about the future but as a heuristic research hypothesis intended to guide interdisciplinary inquiry. The aim of this article is, therefore, to develop the concept of neurotechnological natives and to outline a structured research agenda for investigating how sustained interaction with neurotechnologies may reshape cognition, social relations, and institutional governance in what may be described as the emerging brain-as-interface era. Under- standing these transformations is not only a scientific challenge but also a matter with important implications for institutions, technological governance, and the future of human–technology interaction.

2 |  The Neurotechnology Shift: From Clinical Use to the Broader Society

Technological shifts from one field of application to another—ultimately creating in some cases a more robust and innovative research and development ecosystem in the latter—have taken place throughout history, as clearly exemplified by the internet. This technology, originally developed by the US Department of Defense ARPANET project, eventually gave rise to the highly innovative digital economy sector.² Similarly, rather than functioning solely as medical or experimental tools, neurotechnolo- gies are increasingly positioned within broader technological ecosystems that seek to measure, interpret, and sometimes modulate neural activity in real-world settings. Here, we refer to this phenomenon as the neurotechnology shift [ 1 ] (see definition in Table 2A ).³

This concept provides a framework through which ongoing trans- formations in brain–technology interaction can be systematically analyzed across technoscientific, cognitive, and sociocultural domains. It also builds upon a long trajectory of scientific and technological development. For several decades, neurotechnolo- gies such as EEG and functional magnetic resonance imaging (fMRI) have been widely used to measure neural activity in clinical and research contexts. More recent advances—including BCIs, transcranial stimulation techniques, and emerging neuro- modulation technologies such as optogenetics—have expanded the capacity not only to observe but also to influence and interact with neural processes. Table 1 illustrates the diversity of contemporary neurotechnologies, distinguishing between sys- tems designed to measure brain structure or function, those intended to intervene in brain structure or function, and hybrid systems that combine both functions. These developments are occurring alongside rapid growth in the global neurotechnology market. Although commercial market estimates tend to vary considerably in their absolute figures and forecasting methods, they consistently project continued double-digit annual growth over the coming decade, reflecting expanding investment in both clinical and nonclinical applications. In relation to this, while it is true that professionally supervised neurotechnology is widely used—for instance, as of April 2023, nearly 200,000 people worldwide had undergone deep brain stimulation (DBS) to treat their Parkinson’s disease⁴ —it cannot be ignored that non-expert–supervised devices such as direct-to-consumer neu- rotechnologies [ 2 ] are, in principle, addressed to a substantially broader population of potential users than clinically supervised systems.

Several dynamics are driving the growing technological and economic capacity to establish operational interfaces between neural and digital systems. For example, advances in AI are dra- matically expanding the ability to decode neural activity and build adaptive closed-loop systems capable of responding dynamically to individual patterns of brain activity. This trend is contributing to the emergence of technological ecosystems in which neural signals increasingly function as a new category of data—that is, neurodata—integrated into broader digital infrastructures.⁵ In contrast, one of the most important challenges for neurotech- nologies intended for everyday use lies in reconciling three technical requirements: portability, minimal invasiveness, and functional efficacy. Portability is necessary for neurotechnologies to operate outside specialized clinical or research facilities and to be integrated into daily activities. Minimal invasiveness is equally important for widespread adoption, as most users are unlikely to accept technologies requiring highly intrusive surgical procedures. At the same time, these systems must achieve sufficient functional efficacy to produce reliable and meaningful information about neural activity in real-world contexts. These requirements often generate significant trade-offs. Neu- ral activity is often difficult to measure or modulate accu- rately through noninvasive interfaces because the skull and surrounding tissues act as physical barriers that reduce signal quality. As a result, the most accurate neurotechnologies—such as intracranial recording systems (e.g., intracranial EEG, electrocorticography, stereoelectroencephalography)—tend to be more invasive, whereas portable and noninvasive systems (using mainly extracranial EEG, functional near-infrared spectroscopy [fNIRS], transcranial direct current stimulation, and noninvasive vagal nerve stimulation) often sacrifice spatial resolution or signal precision (see Table 1 ). When two neurotechnologies take advantage of the same biological principles, the less (or non) portable one proves to be more efficacious. For example, “[w]hile fMRI, considered the gold standard in hemodynamic brain imaging, provides whole-brain measurements, fNIRS [which is also based in the hemodynamical properties of the brain] has limitations in terms of spatial resolution as it can only access the superficial cortex”[ 5 ]. As a consequence, achieving acceptable levels of accuracy while maintaining portability and minimal invasiveness represents one of the central engineering challenges in contemporary neurotechnology. In addition, long-term usabil- ity and device abandonment remain significant obstacles to the widespread adoption of neurotechnologies, particularly in the case of implant-based interfaces.⁶ Despite these challenges, the field continues to evolve as consumer and other nonclinical neurotechnologies develop. An expanding range of applications targets uses outside traditional medical supervision, including stress regulation,⁷ fatigue monitoring,⁸ and road safety,⁹ just to mention a few. Some of these technologies are beginning to appear in consumer devices and in workplace monitoring systems.¹⁰ What distinguishes the current moment from earlier phases of consumer neurotechnology [ 2 ] is not the mere existence of such devices, which have been commercially available for well over a decade without achieving mainstream adoption, but the convergence of two enabling conditions that were largely absent in earlier periods. First, advances in AI—particularly deep learning—have substantially increased the capacity to decode and interpret neural signals, enabling meaningful inferences to be drawn from lower-quality, noninvasive recordings that would previously have been considered insufficient [ 6 ]. Second, the entry of large technology firms and well-capitalized ventures into the neurotechnology space has begun to align neural sensing with established consumer and data infrastructures, as demonstrated by the fact that major consumer-electronics manufacturers have filed patents for EEG sensors integrated into devices such as earbuds [ 7 ].¹¹ Whether these conditions will ultimately produce mainstream adoption remains uncertain. However, the argument advanced here is not that such adoption is inevitable, but that this convergence makes the questions raised by the prospect of neurotechnological natives newly tractable and worth anticipating. The expansion of these applications reflects the growing per- ception that neurotechnologies may eventually operate not only as clinical tools but also as everyday systems interacting with neural activity in ordinary environments. While essential ethical and regulatory quandaries have been anticipated regarding this possibility [ 2 ], very little has been discussed about the potential effects on users’ biology, cognition, and social relations. In fact, the transformations brought by the neurotechnology shift can be understood as unfolding across three core dimensions (see also  [ 1 ]): technoscientific, cognitive, and sociocultural (see Figure 1 and Table 2B ).

These three dimensions are closely intertwined. The technosci- entific shift concerns the expansion of neurotechnologies beyond clinical and laboratory environments through processes of inno- vation, scalability, and convergence with digital infrastructures—including their growing integration with AI and data-driven platforms capable of interpreting and responding to neural signals in real time. This expansion is, in turn, what makes a cognitive shift consequential: as interaction with neurotechno- logical systems becomes more sustained, it may affect cognition and human experience, including neural plasticity and neurode- velopment, self-representation, impulse control, and language processes, raising broader questions about autonomy, mental privacy, and the formation of identity in everyday environments where such interaction becomes routine. These technoscientific and cognitive dimensions are inseparable from a sociocultural shift , as neurotechnologies become embedded in workplaces, educational settings, consumer markets, and digital systems, reconfiguring institutions, social norms, human relations, and collective imaginaries, and raising new questions about rights, inequalities, ethical frameworks, and governance structures.

Together, these transformations suggest that the brain is increas- ingly being treated not only as an object of scientific investigation but also as an interface itself acting as a shared boundary between biological cognition and technological systems. As neurotech- nologies extend across everyday environments and potentially unfold across generations, they may begin to influence the condi- tions under which individuals develop cognitively and socially. In such contexts, it becomes meaningful to consider the possibility of neurotechnological natives—individuals whose cognitive and social development occurs in environments where interaction with neurotechnologies is routinely embedded in daily life. This concept provides a useful hypothesis for exploring the long- term cognitive and social implications of the neurotechnology shift.

¹ See https://www.cinetcenter.com/en/inegov/the-neurotechnology-shift/ .

² See https://www.telefonica.com/en/communication-room/blog/history-internet- how- come- being- how- evolved/.

³ See https://www.ft.com/content/d3463f5e- 2bc1- 4d88- a209- d9e3d174115e.

⁴ See https://radiology.ucsf.edu/news/parkinsons- disease- and- deep- brain- stimulation.

⁵ Recent work has begun to explore how such interfaces could extend beyond individual devices to larger sociotechnical systems, including urban infrastructures “that can read, anticipate, and respond to neural states in real time”[ 4 ].

TABLE 1 | Types of neurotechnologies, including those typically leveraged for nonclinical uses (in blue font).

Types of neurotechnologies

TABLE 2 | Core concepts and research priorities for neurotechnological natives within the neurotechnology shift framework.

(A) Core concepts 

Neurotechnology shift : A technoscientific, cognitive, and sociocultural transformation in which neurotechnologies move beyond laboratories and hospitals into everyday environments, turning the brain itself into an interface. Neurotechnological natives : Individuals whose cognitive and social development unfolds in environments where they routinely interact with neurotechnologies in everyday life. Brain-as-interface : A technological condition in which neurotechnologies enable direct interaction with neural processes, turning the brain itself into a shared boundary between biological cognition and digital systems

(B) Research priorities across the neurotechnology shift .

FIGURE 1 |The neurotechnology shift: three interdependent dimensions. Reproduced with permission from CINET/Tatiana Foundation. Source : https://www.cinetcenter.com/en/inegov/the- neurotechnology-shift/ .

3 | Neurotechnological Natives: The Concept

The possibility that emerging technologies may influence cogni- tive development across generations has previously been explored through the notion of digital natives. In a widely cited for- mulation, Marc Prensky proposed that individuals raised in environments saturated with digital technologies might develop distinctive cognitive habits and modes of information processing compared with earlier generations: “It is now clear that, as a result of this ubiquitous [digital] environment and the sheer volume of their interaction with it, today’s students think and process information fundamentally differently from their predecessors. [. . . ] [Therefore,] it is very likely that our students’ brains have physically changed—and are different from ours—as a result of how they grew up”[ 8 ].

While some researchers have argued that Internet use may be a modifying factor affecting cognitive skills (and their neuro- biological foundations) such as attention, memory, and self- concept/esteem [ 9 ], Prensky’s thesis has been widely debated and criticized in the literature, arguing oversimplification: “[T]here is scant evidence to support this idea [of digital natives], and that emerging research challenges notions of a homogenous generation with technical expertise and a distinctive learning style. Instead, it suggests variations and differences within this population, which may be more significant to educators than similarities”[ 10 ]; seealso[ 11 ]and[ 12 ].

The concept of digital natives has nevertheless proved useful as a working hypothesis for investigating whether and how technological environments may decisively shape cognitive and social development [ 9, 13, 14 ]. The present article proposes an analogous concept in the context of the neurotechnology shift: neurotechnological natives. Unlike the digital natives thesis, which posited the emergence of a distinct generational cohort shaped by digital technologies, the concept introduced here is not intended to assert the existence of a homogeneous group with specific cognitive traits. The purpose of this concept is to function as a research hypothesis that helps identify new questions arising from the increasing interaction between neurotechnological systems and human cognition. In this sense, neurotechnological natives should be understood pri- marily as a heuristic framework intended to guide empirical and interdisciplinary research on the potential long-term implications of the neurotechnology shift.

Importantly, the concept does not presuppose the universal adop- tion of neural interfaces or the implantation of neurotechnologies during childhood. Rather, it refers to the possibility that individ- uals may grow up routinely interacting with neurotechnologies in their everyday lives, whether through wearable devices, BCIs, neuroadaptive technologies, or other forms of neural monitoring and stimulation. In such contexts, neurotechnologies would not simply surround individuals as part of the broader technological environment, but would constitute regular tools through which users measure, interpret, or modulate their own neural processes. Under these conditions, it becomes meaningful to consider the possibility of neurotechnological natives (see definition in Table 2A ).

Because many contemporary neurotechnologies rely heavily on digital tools, machine learning, and data-driven systems, one might argue that neurotechnological natives simply represent a subset of digital natives. However, the concept proposed here differs from Prensky’s formulation in several important respects. First, it is not limited to questions of education or pedagogy but refers more broadly to individuals whose routine interaction with neurotechnologies can present in any setting, including the home, educational institutions, the workplace, entertainment, and more. Second, it does not assume the existence of specific cognitive effects; rather, it introduces a research hypothesis aimed at identifying possible questions that may arise as neu- rotechnological environments evolve. Third, the concept shifts attention toward potential interactions between neurotechnology and cognition that have received little scrutiny in discussions of digital natives, which have largely focused on multitasking, memory changes, the emotional implications of social networks, and privacy issues.

If such environments emerge, they would raise new ques- tions regarding the relationship between neurotechnology and cognitive development, including potential effects on neural plasticity, self-regulation, language processes, identity formation, and broader patterns of social interaction. At the same time, the embedding of neurotechnology across diverse social and everyday contexts may reshape the normative conditions under which cognitive and social development unfolds. Rather than serving as a prediction about the future, the concept of neurotechnological natives should be understood as an analytical tool for structur- ing research on the implications of the neurotechnology shift. The core question is, therefore, not whether neurotechnological natives already exist, but rather how emerging neurotechnolog- ical environments might reshape the conditions under which cognitive and social development unfolds—as well as how these developmental processes may in turn influence the design and use of neurotechnologies.

A further clarification concerns what is distinctively neurotech- nological about the questions raised here, as opposed to broader concerns associated with AI, digital platforms, or behavioral self-tracking devices. Whereas most digital and self-tracking technologies infer cognitive or emotional states indirectly—from behavioral traces such as clicks, location, movement, or physio- logical proxies—neurotechnologies seek to measure, decode, or modulate neural activity more directly. This distinction matters because direct access to neural signals raises questions about mental privacy, mental integrity, and cognitive liberty [ 3, 15, 16 ] that do not arise, at least not in the same immediate form, from the analysis of behavioral data. The research agenda proposed here is, therefore, not a restatement of existing debates on AI or digital technologies, but an attempt to identify the questions that become salient specifically when the brain itself, rather than its behavioral outputs, becomes a site of technological interaction.

4 | Neurotechnological Natives: A Research Agenda

Building on the conceptual framework outlined above, this sec- tion proposes a research agenda for investigating the implications of the neurotechnology shift and the possible emergence of neurotechnological natives. This agenda is addressed primarily to researchers in neuroethics, cognitive science, and technology governance, as well as to the policymakers and institutional actors who will need anticipatory evidence before these technologies become widespread.

As mentioned above, the priorities identified here should not be understood as predictions about future technological trajectories, but rather as research hypotheses intended to guide empirical and interdisciplinary inquiry. Moreover, they are not intended to exhaust the range of possible implications, but rather to identify analytically tractable entry points for research. Finally, unlike much of the literature on digital natives—which has largely focused on multitasking and the implications of digital and social media [ 8, 10–12 ]—the agenda proposed here concentrates on forms of interaction between neurotechnologies and human cognition that remain comparatively unexplored. The following priorities are organized across the three dimensions of the neu- rotechnology shift: technoscientific, cognitive, and sociocultural (see Table 2B ).

into adaptive cognitive, emotional, and behavioral responses. Machine learning techniques are increasingly used to decode complex brain activity, detect patterns in neural data, and enable closed-loop interactions between users and neurotechnological devices [ 17 ]. These systems may allow neurotechnologies not only to monitor neural states but also to respond dynamically to them, creating new forms of human–technology interaction. This raises the question of how much of what a neuroadaptive system attributes to a user originates in the neural signal itself, and how much in the model’s priors and training data. This is an issue with direct implications for the reliability of these inferences and for who is accountable if and when they are wrong.

Priority 3. Neurodata ecosystems : The emergence of neurodata ecosystems introduces new questions about how neural signals may be collected, processed, and integrated into broader digital infrastructures. As neurotechnologies such as wearable and neuroadaptive systems expand beyond clinical contexts, these ecosystems are often embedded within broader infrastructures that combine neurodata with biometric, behavioral, and digital data streams [ 7, 18 ]. Understanding these developments requires examining the infrastructures, standards, and platforms that govern the circulation and interpretation of neurodata either in isolation or in combination with non-neural data. Research in this area should explore how neurodata ecosystems are structured and, specifically, how standards for neurodata storage, inter- operability, and transfer—and the absence of such standards—determine who can access neural signals, for what purposes, and under what safeguards.

4.1 | Technoscientific Research Agenda

The technoscientific shift concerns the infrastructures that enable sustained interaction between neural processes and technological systems. Understanding this dimension of the neurotechnology shift requires examining the conditions under which neurotech- nologies may expand beyond clinical and laboratory settings into everyday environments. This expansion is driven both by the translation of clinical neurotechnologies into broader con- texts and by the emergence of devices specifically designed for nonclinical applications. The following priorities highlight key technoscientific questions regarding the scalability, technological convergence, and data infrastructures that may underpin the neurotechnology shift.

Priority 1. Scaling neurotechnological interfaces : A central techno- scientific question involves the conditions under which BCIs and other neurotechnologies may scale from specialized appli- cations to everyday contexts. While many devices originate in clinical research, an increasing number of neurotechnologies are being developed specifically for nonclinical environments such as consumer markets, workplaces, or educational settings. Achieving sustained interaction outside controlled environments requires addressing engineering challenges related to wearability, reliability, usability, and affordability. These developments also involve trade-offs between signal quality, portability, and minimal invasiveness. In concrete terms, this means identifying which nonclinical domains—such as workplace monitoring, educa- tional attention-tracking, or consumer wellness—are most likely to see early deployment, and why. Mapping these trajectories matters because the domains where neurotechnologies first become routine will shape which governance questions arise first.

Priority 2. AI –neurotechnology convergence : The growing conver- gence between neurotechnology and AI raises important research questions regarding how neurodata are interpreted and translated

4.2 | Cognitive Research Agenda

The cognitive shift concerns the potential effects of sustained interaction with neurotechnologies on cognition and human experience. These include possible changes in neural plasticity and neurodevelopment, self-representation, impulse control, and language processes, as well as broader questions related to autonomy and mental privacy. At a deeper level, these devel- opments also raise a question reminiscent of Thomas Nagel’s classic inquiry into subjective experience [ 19 ]: What will it be like to be a neurotechnological native? As interaction with neurotechnologies becomes increasingly embedded in everyday environments, investigating how brain–technology interaction may shape cognitive development and lived experience becomes a central research challenge.

Priority 4. Neural plasticity and identity : Sustained interaction with neurotechnologies invites inquiry and reflection about neu- ral plasticity, cognitive development, and the formation of per- sonal identity. If individuals grow up routinely interacting with neurotechnological devices and systems, such environments may influence how neural circuits adapt over time and how the self is experienced and represented. Evidence from clinical contexts already suggests that neurotechnological interventions can affect aspects of personality and self-perception. For example, some patients undergoing DBS (see Table 1 ) for Parkinson’s disease motor symptoms have shown increased impulsive behaviors [ 20 ]. These observations raise broader questions about how continued brain–technology interaction might shape identity formation and whether such developments could eventually challenge philosophical and legal notions of personhood.¹² They also raise normative concerns about protecting individuals from “direct impact on their neural computation [that] result in direct harm to them”(mental integrity) and about preserving “the crucial requirement of personal identity consisting in experiencing one- self as persisting through time as the same person”(psychological continuity) [ 15 ]. What remains to be established is whether the kind of changes documented in some clinical contexts—where intervention is targeted and time-limited—would generalize to sustained, everyday interaction with neurotechnologies, and if so, at what threshold such changes would become legally or ethically salient.

Priority 5. Neurosurveillance and impulse control : Brain-reading technologies such as EEG-based systems (see Table 1 ) have begun to be used to track attention, fatigue, or emotional states in settings such as workplaces¹³ and educational environments,¹⁴ which raises concerns about mental privacy and neurosurveil- lance [ 21 ]. Additionally, from an interpersonal/relational per- spective, recent evidence shows that surveillance may have the effect of increasing the speed with which people become aware of being watched by others [ 22 ]. Based on this, it seems reasonable to consider that monitoring of neural states via neurotechnolo- gies may accelerate individuals’ awareness of being observed, potentially reshaping how people regulate their own thoughts and reactions. One possibility is that individuals accustomed to use neurotechnologies may develop new forms of impulse control aimed at managing what can be inferred from their neural signals. Alternatively, such contexts could foster what might be described as a society of impulses , in which thoughts, inten- tions, or emotions become objects of scrutiny. Because impulse control plays a crucial role in regulating social interaction—shaping how individuals manage emotions, reactions, and norms of coexistence—changes in this ability could have significant implications for everyday social life. In more extreme scenarios, such developments might even raise the possibility of holding individuals legally accountable for what they believe, feel, or intend rather than for what they actually do. The question here is ultimately empirical: whether individuals who know their neural states are being monitored develop measurable changes in self-regulation, and whether their adaptive responses differ from those produced by conventional behavioral surveillance.

Priority 6. Inner speech and language acquisition : Emerging neurotechnologies capable of decoding neural activity associ- ated with speech [ 23, 24 ] raise important questions about the development of inner speech and language processes. Tech- nologies enabling silent communication—such as brain-to-text systems,¹⁵ mental commands,¹⁶ or brain-to-brain interaction¹⁷ —may allow individuals to “talk without speaking and hear with- out listening”—to paraphrase Paul Simon’s lyrics¹⁸ —potentially reducing reliance on overt linguistic expression, handwriting, and even typing in daily life. According to Vygotsky’s sociocultural theory,¹⁹ inner speech “develops in childhood through the gradual internalization of linguistically mediated exchanges with others, allowing the individual to begin to employ language previously used to regulate the behavior of others in the autoregulation of cognition and behavior”[ 25 ]. If neurotechnologies reshape how linguistic exchanges occur, especially during early develop- ment, they may influence how inner speech forms, potentially producing effects analogous to forms of social deprivation (see [ 26 ]). Additionally, recent evidence shows that language may not be a necessary tool for complex thought [ 27, 28 ], which may make nonverbal forms of thought more prominent—both at the personal and populational levels—in environments where the use of silent-speech neurotechnologies becomes routine. This could have far-reaching implications for how human sociability, communication, and shared understanding are organized.

¹² See, for example, https://blogs.bcm.edu/2018/07/27/can- deep- brain- stimulation- make- you- a- different- person/ . ¹³ See again https://hbr.org/2023/03/neurotech- at- work .

¹⁴ See, for example, https://www.theguardian.com/world/2019/nov/01/chinese- primary- school- halts- trial- of- device- that- monitors- pupils- brainwaves .

¹⁵ See, for example, https://www.freethink.com/hard- tech/speech- bcis .

¹⁶ See, for example, https://emotiv.gitbook.io/emotivbci/mental-commands/ overviewand https://www.media.mit.edu/projects/alterego/overview/ .

¹⁷ See, for example, https://www.nbcnews.com/id/wbna27162401 .

¹⁸ See https://www.paulsimon.com/track/the- sound- of- silence/ .

4.3 | Sociocultural Research Agenda

The sociocultural shift concerns how the growing presence of neurotechnologies may reshape social institutions, norms, and collective imaginaries. As brain–technology interaction becomes embedded in domains such as workplaces, education, consumer markets, and digital governance, new questions arise regarding regulation of neurotechnological systems, inequalities, and social relations themselves.

Priority 7. Norms, institutional design, and governance : As neu- rotechnologies expand beyond clinical contexts and become embedded in everyday environments, they raise new challenges for their governance across multiple social domains. Principles and rights such as cognitive liberty [ 3, 15, 16, 29, 30 ], autonomy [ 30 ], mental privacy and integrity [ 3, 15, 16, 18 ], and nondis- crimination [ 16 ] require not only normative articulation but also practical governance mechanisms. A growing body of governance instruments already illustrates the range of mechanisms avail- able, and these can be usefully distinguished by type. At the inter- national level, soft-law standards have begun to emerge, such as the OECD Recommendation on Responsible Innovation in Neu- rotechnology and, more recently, the UNESCO Recommendation on the Ethics of Neurotechnology.²⁰ At the national level, binding and proposed legislation has started to address neural data and neurorights, including the 2021 constitutional reform in Chile [ 31 ], the proposed federal MIND Act in the United States,²¹ and the General Law on Neurorights and Neurotechnologies under discussion in Mexico [ 32 ]. Beyond substantive rights, a third type of instrument concerns procedural guarantees designed to make these rights effective: a notable example is the proposed habeas cogitationem writ, introduced as a bill in the Argentine Chamber of Deputies in 2025,²² which would complement the existing habeas corpus and habeas data remedies [ 33 ]. Finally, judicial decisions are beginning to test these protections in practice, as in the 2023 Chilean Supreme Court ruling on consumer

neurodata [ 34 ] discussed in Section 5 . A key research task is to assess how these heterogeneous instruments—international, national, procedural, and jurisprudential—interact, and whether they can be coordinated into coherent and interoperable gov- ernance mechanisms. One concrete line of inquiry concerns which existing bodies (e.g., data protection authorities, medical- device regulators, consumer-protection agencies) are best placed to govern nonclinical neurotechnologies, and where current mandates leave gaps that no existing institution covers.

Priority 8. Inequality and the neurotechnology divide : The expan- sion of neurotechnologies beyond clinical contexts poses impor- tant dilemmas about (in)equality and access. Just as earlier digital technologies generated new forms of digital divide [ 35, 36 ], the emergence of neurotechnological natives may produce what could be described as a neurotechnology divide. Differences in access to neuroadaptive tools, neural monitoring systems, and other neurotechnologies could create new asymmetries in edu- cation, employment, and everyday life. At the same time, some scholars have questioned the normative justification for actively promoting a right to cognitive or mental enhancement through neurotechnologies, noting that such claims may overlook unre- solved ethical-legal concerns related to inequality, autonomy, and broader societal impacts [ 37 ]. Research should examine how socioeconomic, institutional, and geopolitical factors influence who benefits from neurotechnological innovation and whether (un)equal access to brain–technology interaction could amplify existing social inequalities.

Priority 9. Collective imaginaries : The sociocultural implications of the neurotechnology shift also depend on how these technolo- gies are imagined, narrated, and represented in public discourse and cultural production. Collective imaginaries surrounding neurotechnology—shaped by media narratives, popular culture, and scientific communication [ 38, 39 ]—play an important role in shaping public trust, social expectations, institutional responses, and regulatory priorities. A core question is whether specific narrative frames (e.g., cognitive and moral enhancement, mind- reading, brain control) measurably shape public acceptance and regulatory appetite, and whether these framings differ across cultural contexts, including both Western and non-Western perspectives (see [ 40 ]), in ways that affect governance.

¹⁹ See https://www.simplypsychology.org/vygotsky.html .

²⁰ See https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0457 and https://www.unesco.org/en/legal-affairs/recommendation-ethics- neurotechnology, respectively.

²¹ See https://www.congress.gov/bill/119th-congress/senate-bill/2925/text/is .

²² See https://www.hcdn.gob.ar/proyectos/detalle_tp_adjunto/index.html?id= 285165 .

5 | Toward Neurotechnology Governance in the Brain-as-Interface Era

Brain and technological systems have largely belonged to speculative fiction. In his futuristic novel *3001: The Final Odyssey*, Arthur C. Clarke envisioned brain-to-text “thinkwriters” as well as “braincaps” capable of projecting immersive scenarios directly into the user’s mind.²³ Yet, recent advances in neurotechnologies suggest that certain forms of brain–technology interaction once confined to fiction may emerge far sooner than previously imagined. It is quite obvious that, throughout history, the human brain has mediated the relationship between biological cognition and the technologies created and used by human societies. Tools, writing systems, machines, and digital devices have extended human cognitive capacities through external interfaces. However, contemporary neurotechnologies introduce a qualitatively differ- ent possibility: the emergence of devices capable of interacting directly—even merging—with neural processes.

Real-world cases already illustrate how the brain-as-interface paradigm (see definition in Table 2A) and the different dimensions of the neurotechnology shift are present in practice. For example, reports in 2019 described the use of wearable EEG headbands in some Chinese schools to monitor students’ levels of attention during classroom activities.²⁴ From a technoscientific angle, these systems rely on relatively simple, minimally invasive EEG devices equipped with a small number of electrodes. Their deployment, therefore, raises questions about signal reliability, calibration, and the technical validity of interpreting cognitive states using simplified wearable systems. From a cognitive perspective, continuous monitoring of attention may influence how students experience concentration, self-regulation, and awareness of being observed. From a sociocultural point of view, such practices raise broader questions about educational norms, institutional responsibilities, acceptable forms of monitoring, and the protection of fundamental rights. This example clearly illustrates how governance challenges emerge from the interaction between technoscientific capabilities, cognitive processes, and social infrastructures.

Importantly, the relationship between the three dimensions of the neurotechnology shift is not unidirectional. For instance, while technoscientific innovations may influence cognitive pro- cesses and social practices, cognitive responses and sociocultural expectations may in turn reshape technological development. Behavioral adaptations to neural monitoring may influence how neurotechnological systems are designed, while institutional norms, regulatory frameworks, and governance structures may constrain how these technologies are deployed and used. The neurotechnology shift should, therefore, be understood as a dynamic system in which technoscientific innovation, cognitive transformation, and sociocultural change continuously shape and reshape one another (see Figure 1 ).

lenges. Addressing them requires approaches capable of integrat- ing insights from neuroscience, psychology, ethics, law, and social sciences, as well as interoperability and coordination among institutions responsible for the regulation and oversight of emerg- ing technologies. In this context, principles such as cognitive liberty—def ined as “every person’s fundamental right to think independently, to use the full spectrum of his or her mind, and to have autonomy over his or her own brain chemistry”[ 29 ]—may serve as transversal reference points linking the technoscientific, cognitive, and sociocultural dimensions of the neurotechnology shift, guiding both normative debates and the development of operational governance frameworks for neurotechnologies. Recent legal developments also illustrate how these governance challenges are already emerging in practice. In a landmark decision, the Supreme Court of Chile ruled in 2023 in favor of a user of a consumer neurotechnology company based in the USA, establishing the first judicial precedent in the world recognizing the protection of consumer neurodata and requiring stronger safeguards for this typology of data [ 34 ]. This Chilean decision, which concerned a consumer EEG device, is significant precisely because it shows the fourth type of governance instrument intro- duced in Section 4.3 (i.e., judicial enforcement) beginning to oper- ate even before comprehensive legislation is in place, and doing so by drawing on prior national privacy and mental-integrity protections rather than on bespoke neurorights provisions. It thereby exemplifies how the international, national, procedural, and jurisprudential instruments discussed earlier do not operate in isolation but increasingly interact: soft-law standards inform and influence national legislation, national frameworks shape the remedies available to courts, and judicial decisions in turn test and clarify the scope of substantive protections. For the prospect of neurotechnological natives, the central governance question is whether this still-fragmented and largely reactive landscape can mature into anticipatory frameworks capable of addressing the technoscientific, cognitive, and sociocultural implications of sustained brain–technology interaction before, rather than after, harms materialize.

The neurotechnology shift also raises important challenges for educational and institutional systems. Recent OECD assessments indicate that foundational adult skills such as literacy and numeracy have stagnated or declined in many advanced economies over the last decade.²⁵ If societies are already struggling to maintain basic cognitive capacities, the emergence of technologies capable of interacting directly with neural and cognitive processes may pose additional challenges for educational systems and governance frameworks. Developing forms of neurotechnological literacy will, therefore, be essential to enable citizens—especially vulnerable populations—to make informed decisions about the use of neurotechnologies⁴¹.

Ultimately, the research agenda proposed in this article—and the concept of neurotechnological natives that motivates it—should be understood not as predictions of inevitable technological trajectories, but as heuristic tools to guide empirical and interdisciplinary investigation about how emerging neurotechnological environments may reshape human cognition, social practices, and systems of governance—and how cognitive responses, institutional arrangements, and sociocultural expectations may in turn influence the design, deployment, and regulation of neurotechnologies. Understanding these threefold, bidirectional dynamics will be essential not only for anticipating the implications of the neurotechnology shift, but also for deciding how societies choose to navigate the emerging brain-as-interface era.

Acknowledgments

The author thanks the anonymous reviewers of this article for their thoughtful and constructive comments on previous versions of the manuscript. Their feedback significantly helped clarify the structure and conceptual framework of the article.

²³ See https://archive.org/details/3001finalodyssey0000clar/mode/2up.

²⁴ See https://www.wsj.com/articles/chinas-efforts-to-lead-the-way-in-ai-start-in-its-classrooms-11571958181 and, again, https://www.theguardian.com/world/2019/nov/01/chinese-primary-school-halts-trial-of-device-that-monitors-pupils-brainwaves.

²⁵ See [OECD – Adult skills in literacy and numeracy declining or stagnating in most OECD countries](https://www.oecd.org/en/about/news/press-releases/2024/12/adult-skills-in-literacy-and-numeracy-declining-or-stagnating-in-most-oecd-countries.html).

Open access publication of this article was funded by Universidad CEU Cardenal Herrera.

Conflicts of Interest

The author declares no conflicts of interest.

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