From Computational Cognition to Neuroarchitecture: Tracing the Past and Future Potential of Brain-Informed Design

Abstract

This paper traces the intellectual foundations of neuroarchitecture, the design of environments informed by how the brain processes spatial information, from its origins in 1970s environmental psychology and later connectionist philosophy to its contemporary state. While early computational approaches prioritized speed and efficiency for engineering tasks like pattern recognition, a prescient group of pioneers pursued a different path. They developed biologically plausible neural network models that prioritized neural realism over computational performance. These networks embraced the complex realities of biological brains, incorporating excitatory and inhibitory dynamics, local learning rules, and hierarchical knowledge representation. We examine how the philosophical frameworks developed during this formative period established the theoretical foundation for meaningful interdisciplinary collaboration between neuroscience and design. The field has since expanded significantly through our contemporary understanding of neurodiversity. This broader perspective has the potential to transform neuroarchitecture from a niche research area into a comprehensive approach for creating environments that support cognitive performance and brain health for everyone.

1. Introduction

While Donald Hebb’s 1949 theory proposed that “neurons that fire together, wire together” [1], it took nearly four decades for theory and models to emerge that could transform his insights into testable computational models. The intellectual foundations of what would later be termed neuroarchitecture emerged in the mid-1980s from a convergence of several disciplines. Advances in neuroscience, philosophy of mind, environmental psychology, and computational methods collectively reshaped our understanding of the relationships among brains, buildings, and behavior. The term ‘neuroarchitecture’ itself was introduced in 2003 when neuroscientist Fred Gage addressed the Academy of Neuroscience for Architecture at the AIA National Convention [2].

By the late 1980s, most researchers were racing to build faster, more efficient convolutional neural networks (CNNs) for practical applications such as image recognition [3]. But a small group of pioneers took a different path, inspired by emerging understanding of how the brain processes spatial information. They began developing biologically plausible Spiking Neural Networks (SNNs) that prioritized accurate modeling of neural function over computational efficiency. The fundamental distinction between CNNs and SNNs lies in their computational paradigms. SNNs incorporate excitatory and inhibitory dynamics, local learning rules, and hierarchical knowledge representation [4,5]. CNNs process information through synchronous, rate-coded activations across hierarchical layers optimized for spatial feature extraction. SNNs, by contrast, employ discrete spike-based communication incorporating temporal dynamics. Information is encoded not merely in activation magnitude but in the precise timing and patterns of spikes across neural populations. This more closely approximates biological neural computation. The models developed through early SNN research enabled the development of contemporary neuromorphic platforms such as Nengo [6], EBRAINS [7], and Lava [8], along with the specialized hardware architectures making large-scale neuromorphic computing feasible.

After its peak in the late 1980s, research in this area experienced a significant decline. This early 1990s “AI winter” [9] reflected practical concerns about the limited applicability of biologically inspired models to solving real-world problems. Computers lacked the power to run realistic neural simulations, and training algorithms for complex spiking networks had not yet been developed [10].

Decades later, the computational advances of the 2000s and 2010s provided the processing power and training methodologies necessary to render SNN models practical. Today, the field has reached a critical juncture: contemporary spiking neural networks have the potential to model the impacts of environmental and design features on physiological brain health and cognitive function. It is also possible to model human individual differences in brain function such as age, autism spectrum disorder, post-traumatic stress, and other variations that shape how individuals experience their environments.

These capabilities form the foundation for the continuing shift toward person-centered computational design with significant implications for design practice. For instance, the concept of cognitive “digital twins,” computational models or agents that represent cognitive profiles, offers the possibility of evaluating the performance of virtual environments as part of the design development process. Such an approach could be used to reveal how buildings affect allostatic overload due to stress-inducing design features [11], cognition outcomes such as wayfinding decision-making behavior [12,13], and modeling the impact of environmental features and behaviors they support (such as physical activity) on the presence of proteins that support the neuronal health [14,15].

This paper is organized as follows. Section 2 describes the review methodology. Section 3 traces the convergent discoveries in neuroscience, environmental psychology, and computational science that established the intellectual foundations of neuroarchitecture. Section 4 examines how the philosophical debates of the 1980s were resolved through connectionism, enabling integration across these disciplines and the development of early computational models. Section 5 presents applications of biologically based neural network models for spatial cognition. Section 6 discusses contemporary extensions toward inclusive neuroarchitecture, incorporating neurodiversity perspectives. Section 7 reviews computational design tools currently available for evidence-based practice. Section 8 concludes with an analysis of persistent barriers and proposed pathways for bridging the research–practice divide.

2. Review Methodology

This review synthesizes the literature across three domains: computational neuroscience, environmental psychology, and architectural design research. Literature was identified through searches of PubMed, PsycINFO, Web of Science, and Google Scholar using terms including “spiking neural network,” “spatial cognition,” “cognitive mapping,” “neuroarchitecture,” “environmental psychology,” “wayfinding,” and “connectionism.” Given the historical scope of this review, no date restrictions were applied, though emphasis was placed on foundational works from 1970 to 1995 and contemporary applications from 2015 to the present. Inclusion criteria prioritized: (a) empirical studies with behavioral or neural outcome measures, (b) computational models with documented validation against human data, and (c) theoretical frameworks that established cross-disciplinary integration. Design tools were included if they demonstrated explicit connection to neuroscientific or psychological evidence.

Table 1. Classification of Cited Studies by Evidence Type and Outcome Measures.

Evidence Type/Study	Outcome Measures	Key Findings
Neurophysiological [16]	Place cell firing patterns	Hippocampal spatial mapping system identified
Behavioral/Self-report [17]	Attention restoration, cognitive fatigue	ART framework for restorative environments
Computational + Behavioral validation [5]	Wayfinding accuracy, route selection	NAPS-PC predicted human performance (r = 0.87, p < 0.001, N = 47)
Computational [18]	Activation dynamics, attention decay	TRACE temporal dynamics model validated
Computational + VR behavioral [13]	Wayfinding time, legibility ratings	DesignMind toolkit validated with architects
Physiological (pilot) [19]	Neuroinflammatory markers	Biophilic design reduces neuroinflammation
Empirical (cross-sectional) [15]	BDNF levels, METs	BDNF–METs relationship quantified for built environments
Computational + Empirical [20]	Visual discomfort ratings, façade analysis	High-contrast patterns trigger cognitive overload

classifies the primary studies cited in this review by evidence type and outcome measures.

3. Historical Foundations: Convergent Discoveries

The intellectual foundations of neuroarchitecture emerged from three independent research programs that converged in the 1970s and 1980s. In neuroscience, John O’Keefe and Lynn Nadel’s The Hippocampus as a Cognitive Map (1978) proposed that the hippocampus functions as a dedicated spatial processing system [16]. In environmental psychology, Stephen Kaplan at the University of Michigan had been developing theory on cognitive mapping since the early 1970s, arguing that cognitive maps were functional organizations of spatial information rather than literal copies of environments [21,22]. His research suggested that specific environmental characteristics influence cognitive function—work that would crystallize into Attention Restoration Theory, demonstrating that natural environments help cognitive systems recover from fatigue [17]. In computational science, David Rumelhart and James McClelland were demonstrating through Parallel Distributed Processing research that networks of simple, neuron-like units could exhibit complex cognitive behaviors [23].

These parallel developments posed a fundamental question: Could insights from neuroscience, psychology, and computation be integrated into a coherent framework for understanding how environments shape cognition? The answer required resolving a deeper philosophical problem.

Table 2. Historical Timeline: From Hebb to Contemporary Neuromorphic Platforms.

Year/Period	Development
1949	Hebb: “Neurons that fire together, wire together” [1]
1978	O’Keefe & Nadel: The Hippocampus as a Cognitive Map [16]
1986	Rumelhart & McClelland: PDP connectionist framework established [23,24,25]
1985–1991	Levenick, O’Neill, Kaplan: Developed SNN spatial cognition models (NAPS-PC, TRACE) [18,26,27]
1992	O’Neill AutoNet: First Agentic AI-based Computational Design Tool within AutoCAD v.11 release 10 [28,29]
1990s	“AI Winter:” Limited computational power constrains SNN development [9]
1997–1998	GENESIS and NEURON platforms: computational neuroscience infrastructure [30,31]
2014–2022	Contemporary platforms: Nengo [6], EBRAINS [7], Lava [8]
2022–present	Gath-Morad DesignMind, person-centered computational design tools [32]

presents a timeline of the key developments from Hebb’s foundational work through contemporary neuromorphic platforms.

4. Philosophical Foundations for Brain-Informed Design

The central question dividing cognitive science in the 1980s was “What is the relationship between mind and brain?” The answer determined whether interdisciplinary work connecting neuroscience to environmental cognition was even coherent as a research program.

The debate was crystallized around three positions. Churchland [24] advanced ‘eliminative materialism,’ arguing that psychological concepts, beliefs, desires, intentions would ultimately be replaced by neurobiological descriptions as neuroscience matured. If correct, environmental psychology’s vocabulary of preferences, attention, and cognitive maps would dissolve into purely neural terms. The opposing position, defended in Fodor’s The Language of Thought [33] and Dennett’s Brainstorms [34], held that mental states are defined by their causal roles rather than their physical implementation which preserves psychological explanation but had the potential of making neuroscience irrelevant to understanding cognition. Davidson’s [35] work charted a middle course through “anomalous monism,” acknowledging the physical basis of mind while insisting that psychological explanation retained irreducible explanatory power.

Each stance carried implications for modeling the brain and cognition. Eliminative materialism required abandoning cognitive concepts for neural mechanisms. Functionalism suggested brain science might be irrelevant to environmental experience. Anomalous monism allowed neuroscience and psychology to coexist but never truly integrate. None offered a clear path toward synthesis.

The Parallel Distributed Processing research group started the work that ultimately resolved this impasse. Their landmark 1986 volumes [23] demonstrated that cognition could emerge from interactions among simple, neuron-like processing units organized in networks. As McClelland, Rumelhart, and Hinton argued [25], connectionist models simultaneously honored both neural constraint and cognitive phenomenon—patterns of activation grounded in brain-like architecture yet exhibiting emergent properties that cognitive science sought to explain.

Kaplan and his colleagues built upon the PDP approach with his ‘cognitive connectionism’ theory which addressed criticisms that standard connectionist models lacked the structured knowledge representations necessary for higher cognition. He proposed that active symbols, dynamic patterns of network activation rather than static tokens, could serve as the basis for knowledge representation [4].

For researchers interested in how environments shape cognition, cognitive connectionism offered a path forward. Cognitive representations were distributed across networks rather than stored as discrete symbols. Learning occurred gradually through experience rather than by following explicit rules. The same network could produce different behaviors in different contexts. These principles suggest that environmental information is encoded in patterns of neural activity. And repeated environmental exposure could reshape cognitive organization, and that individual differences in environmental response might reflect variations in network architecture. Kaplan’s work reconciled the distributed processing advantages of connectionism with the structured representations that cognitive tasks require, setting the stage for initial development of biologically based neural network models [36].

5. Computational Wayfinding Models: From Symbolic Rules-Based to Connectionist Paradigms

By the late 1970s, researchers had begun developing computational models to simulate human wayfinding behavior in built environments. Kuipers [37] developed the influential TOUR model, which represented cognitive maps using symbolic data structures with explicit representations of paths, places, and regions, employing condition–action rules to simulate navigation decisions. Elliott and Lesk [38] created algorithms for route-finding in street maps, while Stahl [39] developed simulation tools for design decision-making. Leiser and Zilbershatz [40] subsequently developed the TRAVELLER model, which used rules linking specific places or nodes within a network, constructing knowledge through condition–action pairs broken down into intermediate steps.

These approaches shared a common foundation in symbolic artificial intelligence. As O’Neill [28] characterized them, such models operated through “a rule-based mechanism” acting on “an inert data structure” (p. 364). Environmental information was represented “in a propositional, or word-like format,” with wayfinding decisions determined “by Boolean (if-then) rules of logic” (p. 364). This approach enabled researchers to model observable way-finding behaviors by explicitly programming decision rules derived from empirical observations of human navigation.

The parallel distributed processing (PDP) framework developed by Rumelhart and McClelland [23] provided an alternative theoretical foundation grounded in neurophysiological principles. Rather than encoding knowledge as explicit rules, PDP models represented information through patterns of activation distributed across interconnected processing units. This connectionist approach, built upon Hebb’s [1] earlier proposal regarding synaptic strengthening through correlated activity, suggested that cognitive processes could emerge from the collective behavior of simple, neuron-like elements.

Researchers including Levenick [26] and O’Neill [27] pursued biologically plausible approaches to modeling spatial cognition using spiking neural networks. Ten years later, broadly similar computational neuroscience platforms like GENESIS [30] and NEURON [31] were developed to model biological neural networks with high fidelity. However, where GENESIS and NEURON focused on simulating neuronal function, O’Neill’s work addressed whether biologically based neural network simulations could be used to predict human cognition and decision-making within architectural settings [27]. A summary of the different approaches to modeling brain function and cognition can be found in

Table 3. Comparative Analysis: SNNs, CNNs, and Agent-Based Models.

Dimension	CNNs	SNNs	Agent-Based Models
Processing paradigm	Synchronous, rate-coded	Spike-based, temporal	Rule-based or neural-driven
Biological plausibility	Low	High	Variable
Information encoding	Activation magnitude	Spike timing + patterns	State + behavior rules
Learning mechanism	Backpropagation	STDP, Hebbian	Experience accumulation
Temporal dynamics	Limited	Intrinsic	Simulation steps
Application to design	Image/pattern analysis	Cognitive modeling	Population simulation
Neurodiversity modeling	Difficult	Parameter adjustment	Agent profiles

.

5.1. The NAPS-PC Model: Architecture and Parameters

Collaborating with Levenick and Kaplan, O’Neill developed the NAPS-PC model (Network Activity Processing Simulator), which represented a departure from rules-based approaches. Rather than requiring manual encoding of environmental structure into discrete symbolic representations, NAPS-PC worked directly with patterns of neural activation learned from exposure to an environmental representation; a data set that represented “nodes” (decision-points) and “paths” (the topological connections between nodes). In this model, spatial knowledge emerged from the strengthening of connections between neurons representing places (nodes), as Hebb had proposed in 1949.

The NAPS-PC architecture incorporated four types of processing elements (nodes), each with specific neurobiological functions:

• Base-level nodes: The lowest level nodes closely associated with environmental features, representing individual choice points within a building. Parameters: max activation = 95–100, min = 0, starting activation = 10, threshold = 50.
• Hierarchical nodes: Represent segments of a floorplan composed of groups of lower-level nodes, enabling chunked spatial representations. Parameters: max = 65, min = 0, start = 10, threshold = 50.
• Hierarchical inhibitory nodes: Control the level of activation of hierarchical nodes by providing top-down inhibition. Parameters: max = 100, min = 0, start = 20, threshold = 101.
• Regional inhibitory nodes: Connected to every base-level node in the network, inhibiting the activity of groups of closely associated base-level nodes to implement lateral inhibition. Parameters: max = 100, min = 0, start = 20, threshold = 101.

Connection strengths between nodes ranged from 39 to 75, representing learned associations between places based on traversal frequency. The system incorporated mechanisms grounded in neurobiology: neural fatigue (activity decay over time), lateral inhibition (competitive suppression of nearby nodes), hierarchical organization (chunked spatial representations), and Hebbian learning (connection strengthening through co-activation). When searching for a path between locations (nodes), activity spreads from start and goal nodes until intersecting at intermediate waypoints.

O’Neill developed NAPS-PC using human wayfinding data from controlled experiments in building-scale settings, calibrating the biological parameters of the model to optimize prediction of actual decision-making patterns. As O’Neill observed, “with additional experience, NAPS-PC ‘learns’ more about the links between places in the environment... With experience, new connections (paths) between start and goal points are possible” [28] (p. 364). Validation against independent data demonstrated that predictions correlated strongly with human performance (r = 0.87, p < 0.001, N = 47 participants) across three building settings with different floor plan complexities [5,27]. The NAPS-PC model is likely one of the earliest, if not the first SNN model trained and validated from human behavioral data.

5.2. The TRACE Model

The TRACE model [18], developed by Kaplan, Chown, and colleagues, addressed a complementary problem: how cognitive processes unfold over time, particularly in relation to attention and environmental restoration. Where NAPS-PC demonstrated spatial knowledge representation in neural terms and cognition measured by decision-making performance, TRACE was focused on explaining the internal temporal dynamics of neural network behavior. The model incorporated activity levels, fatigue, short-term and long-term connection strengths, and external input to simulate learning, short-term memory, and activity cascades where initial input triggered sustained neural activity persisting after the stimulus ceased. This temporal perspective proved particularly relevant for understanding how natural environments promote attention restoration.

5.3. Early Applications: AutoNet

By the early 1990s, these theoretical developments yielded a practical application. O’Neill developed an SNN-based computer-aided design tool, AutoNet, that evaluated building legibility by simulating human wayfinding performance [28]. It was based on the NAPS-PC model that was developed as an application running within AutoCad release 10 [41]. It was intended to be used in real time by the designer to optimize office workspace design by predicting wayfinding performance within the floorplan [29]. This application was likely the first agentic AI-based computational design tool ever developed. It established an early precedent: architectural design could be informed by computational models of brain function [42]. AutoNet represented an initial step toward environmental design grounded in scientific understanding of human cognition.

6. Contemporary Extensions: Toward Inclusive Neuroarchitecture

The early 2000s marked a turning point for biologically based neural networks. Increasing computational power and better training algorithms were making previously impractical approaches feasible again. But when researchers finally had the tools to implement the original vision, they discovered something the pioneers hadn’t anticipated: human brains were far more diverse than anyone had imagined.

The explosion of research into autism spectrum conditions, ADHD, sensory processing differences, and other forms of neurodiversity revealed that what scientists had long treated as “standard” human cognitive processing was just one point on a vast spectrum of neurological variation [43,44]. Equally significant was the growing recognition that aging itself represents a fundamental dimension of neurodiversity, with older adults experiencing systematic changes in neural processing, sensory function, and cognitive performance that have profound implications for environmental design [45]. Age-related changes in neural efficiency, processing speed, and sensory acuity create distinct environmental needs and preferences [46,47]. People across the spectrum of neurodiversity exhibit profound differences in sensory processing, attention regulation, and social space preferences [48].

Even more striking are emerging findings in architectural neuroimmunology. This research suggests that visual exposure to specific architectural elements can affect neurophysiological responses, including neuroinflammation and stress-related biomarkers [19]. Studies identified that certain high-contrast patterns and architectural façade designs could trigger physiological discomfort and cognitive overload in susceptible individuals [49].

7. Computational Design Tools for Inclusive Design

The classical design approach that has dominated architectural practice for centuries can now be augmented by computational models that position human experience at the center of the design process, with explicit attention to outcomes such as cognitive performance and neurogenesis and brain health [14,15,50]. Computational approaches grounded in biologically based neural network models are emerging as particularly well-suited to address the challenge of designing for neurodiversity and for modeling environmental impacts on stress and neuroplasticity [51,52].

Agent-based modeling offers complementary methods for implementing neurobiologically informed design principles in practice. By integrating spiking neural networks with agent-based simulation frameworks, researchers can model how individuals with varying neural processing characteristics navigate and respond to architectural environments across time [53]. Recent advances in generative agent-based modeling have further demonstrated the potential for creating behavioral simulations that incorporate established principles of spatial cognition and environmental perception [54]. The integration of these models with architectural design software represents a significant methodological advance: computational systems capable of predicting how environments will be experienced across the full spectrum of human neurodiversity, enabling the design of inherently inclusive spaces from the outset rather than through post hoc accommodations [32,50].

Recent developments illustrate this potential through concrete implementations. Gath-Morad and colleagues developed the DesignMind toolkit, which integrates four complementary modules: evidence-based design flashcards synthesizing research findings, topological and geometric spatial analysis, agent-based behavioral simulation, and cognitive walkthrough assessment in virtual reality environments [32]. These tools enable practitioners to evaluate designs against human-centered criteria including wayfinding performance, social interaction potential, and cognitive accessibility [12,13,20,51,52].

Such contemporary implementations represent a substantial evolution from the foundational work initiated in the late 1980s with O’Neill’s NAPS-PC model [5,27]. Where NAPS-PC demonstrated the capacity to predict wayfinding performance for neurotypical users [5,28], emerging systems can simulate populations of cognitively diverse agents—each instantiated with distinct neural processing parameters representing typical cognitive profiles alongside characteristics associated with ADHD, autism spectrum conditions, or age-related cognitive changes [43,44,45,46,47]. These simulations reveal differential environmental experiences: how the same architectural configuration might be processed by an individual with heightened visual sensitivity, by someone whose working memory capacity reflects age-related changes, or by a person whose preferences for social proximity diverge from population norms [48]. This capability to model individual differences in environmental response represents a critical extension of the original computational vision toward genuinely inclusive design practice.

8. Conclusions: Bridging the Research–Practice Divide

This paper has traced the arc of development of the field of neuroarchitecture. Decades of compelling research, including recent studies, link how built environments affect human cognition, brain health and cardiovascular wellbeing [55,56,57]. Yet the architecture profession has struggled to see the practical application and relevance of this research to design practice. Organizations like EDRA and ANFA have worked since the 1970s to bridge environmental design research and professional practice, yet the gap persists [58]. This stems from the difficulty of translating research insights into actionable design strategies. If the research community can influence the architectural profession, then their insights and innovations can provide benefits to the users of the spaces.

Table 4. Mapping Conclusions to Supporting Evidence.

Conclusion	Supporting Evidence	Evidence Type & Strength
Biologically grounded models can predict human wayfinding	O’Neill [5]: NAPS-PC validation r = 0.87, p < 0.001, N = 47 across 3 building types	Empirical validation; large effect size
Temporal dynamics of attention can be computationally modeled	Kaplan et al. [18]: TRACE model simulations of activity cascades	Computational demonstration
Agent-based tools enable person-centered design	AutoNet agentic AI within AutoCad DesignMind 4-module toolkit with VR validation [13]	Tool development + pilot validation
Environmental features affect neuroplasticity markers	Khalil [51]: BDNF–METs relationship quantified	Empirical (cross-sectional)
Architectural elements can trigger neuroinflammatory responses	Valentine & Mitcheltree [19]: Biophilic design pilot study	Empirical (pilot, requires validation)

summarizes the key conclusions of this review and their supporting empirical foundations.

8.1. Persistent Barriers

The fundamental challenge lies in the differences in culture and training between the research and design communities. Architecture education emphasizes intuitive design thinking and judgment through studio-based learning [59,60]. Practitioners skilled at synthesizing complex requirements into built form often lack training in research methodology or evidence-based decision making. Researchers often lack understanding of how to translate their research findings or tools into a form that is relevant to architects.

Moreover, insights from neuroarchitecture research require specialized context for translation to application. However, design fees leave little budget for tools or explorations outside what is required for Schematic Design (SD) and the Design Development (DD) phases. Clients want solutions delivered quickly, and liability concerns discourage experimentation with research-based approaches. Clients are aware that architects use AI applications (such as generative design) and they expect a reduction in fees due to greater efficiencies [61].

Communication barriers further widen the divide. Research published in neuroscience journals uses language assuming familiarity with the content and analyses that architects rarely possess. Who then makes the case to the client about the value of these insights?

8.2. The Path Forward

Bridging this divide requires coordinated change across multiple dimensions. Architectural education should integrate basic research training without abandoning the studio system [62]. The research community must continue to develop accessible tools like the DesignMind toolkit [32] that provide clear, actionable guidance while requiring minimal specialized knowledge.

New collaboration models are essential. Rather than expecting architects to become neuroscience experts, the field needs embedded research specialists who can translate between communities. A promising approach lies in integration with computational design platforms. In this way research tools can be embedded directly into design software. Future technologies could automatically evaluate designs for experiential outcomes such as well-being, neuroplasticity, ease of wayfinding, visual comfort, stress or neurodiversity accommodation, making research-informed design as natural as checking building code compliance.

The vision of architecture informed by scientific understanding of human cognition remains unrealized but not impossible. As our understanding of cognitive well-being expands and our ability to model human–environment interactions becomes more sophisticated, the field of neuroarchitecture will be able to offer tools for creating environments that support human flourishing across the full spectrum of cognitive diversity. Research insights and tools have the potential to transform architectural practice if they offer a clearly articulated value proposition to the architecture community, and their clients.

8.3. Opportunities for Neuroarchitecture: Next Generation Computational Models

Given the emphasis on neurodiversity and aging throughout this review, age-stratified analyses are needed to determine whether model parameters require systematic adjustment across the lifespan. Cross-validation of agent parameters across cognitive profiles—neurotypical, autism spectrum, ADHD, and age-related cognitive decline, would establish the generalizability of computational frameworks as they are developed. Without such validation, claims about modeling neurodiversity remain theoretical rather than empirically grounded.

Expanding public health and other public datasets offer opportunities to train and improve the next generation of AI models. The building and training person-centered design computational models with longitudinal outcome data, tracking actual health, cognitive, and wellbeing outcomes in built environments, would result in the most robust models.

Recent advances in healthcare AI demonstrate both the feasibility and necessity of such integration. Stanford’s Human-Centered AI initiative has emphasized that longitudinal datasets are essential for training multimodal models to understand complex, long-term health patterns, noting that current medical datasets fail to reflect the full scope of past and future health information needed for robust AI development [63]. A scoping review of machine learning applications on longitudinal electronic health records found that deep learning models can detect diseases earlier than current clinical diagnoses and that incorporating personally responsible factors enables targeted prevention interventions [64].

For neuroarchitecture specifically, allostatic load, the cumulative physiological wear resulting from chronic environmental stress, offers a promising framework for quantifying the health impacts of built environments. Data analyzed from the NIH All of Us Research Program’s multimodal longitudinal data demonstrated how perceived stress predicts allostatic load and biological aging [65]. Their earlier review identified that environmental stressors such as lack of green spaces and neighborhood socioeconomic disadvantage are associated with higher allostatic load levels [66]. Neuroarchitecture research could add to this work by examining the design features of the spaces people use within communities. Notably, streamlined allostatic load measurement using as few as five biomarkers (including heart-rate variability obtainable from wearable devices) could reduce data collection burden while maintaining predictive validity.

These developments suggest a path forward for neuroarchitecture research: drawing from large-scale, longitudinal studies that pair environmental exposure data from built settings with standardized health outcome measures could generate the training datasets necessary for next-generation computational models. Such models could move to forecasting how specific design interventions affect stress biomarkers, cognitive trajectories, and long-term brain health across diverse populations.