Behavioral Economics,Neuroscience,Consumer Choice,Neuropricing,Neurorights,Quantum-Resistant Privacy,Chilean Jurisprudence,WEIRD Bias,Neurocognitive Value Encoding,Price Elasticity,Behavioral Elasticity,Neuro-marketing,Optimal Pricing,Technical Report,Academic Literature Review,Comprehensive Report,Structured Report,Researchers/Experts,Neurodata Exceptionalism,Neurodata Sovereignty,Neurocognitive Mechanisms,SPED Ethics,Functional Brain Fingerprinting,Post-Quantum Cryptographic Safeguards)¶

Behavior-Elastic Demand: Neuro-Marketing, Neurorights, and the Future of Optimal Pricing

1. Introduction: Rethinking Demand Through Neuro-Economics¶

The transition from neoclassical price-elastic demand models to behavior-elastic demand curves grounded in neuro-marketing data represents a paradigmatic shift in economic theory and practice, redefining the foundational mechanism by which consumer choice is measured, predicted, and optimized. Traditional demand models, rooted in the assumption of rational, utility-maximizing agents whose preferences are stable and exogenous, have long treated price as the sole determinant of quantity demanded—operationalized through functional forms such as linear or iso-elastic curves that ignore the neurocognitive, emotional, and contextual modulators of decision-making [10][30]. These models, while mathematically tractable, fail to capture systematic deviations observed in real-world behavior: loss aversion, reference dependence, mental accounting, and hyperbolic discounting all demonstrate that willingness-to-pay (WTP) is not a fixed function of price but a dynamic, internally generated state shaped by neural processes preceding overt choice [21][34][18]. The emergence of neuro-marketing methodologies—particularly electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)—now enables the direct, empirical recovery of WTP as a continuous, neurophysiological variable, replacing inferred preferences with measured neural correlates such as prefrontal gamma asymmetry (PAIγ) and orbitofrontal cortex BOLD signal amplitude [8][6][28]. This transformation redefines demand elasticity not as a static price-response coefficient, but as a behavior-elastic function: $ f(\gamma_{asymmetry}, t, \text{category}, \text{goal_specificity}) \rightarrow \text{WTP} $, where responsiveness is governed by the unfolding neurocognitive state rather than monetary incentive alone [28][5].

Behavior-elastic demand curves, therefore, are not incremental refinements but structural replacements for classical models, grounded in the neurobiological evidence that valuation is a distributed process involving the medial orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex—regions that encode subjective value, conflict, and integrated preference in real time [6][44]. The derivation of these curves relies on deep learning architectures trained on multimodal neural data, such as the DeePay model, which processes raw EEG signals to predict continuous WTP with 75.09% accuracy and an RMSE of 0.276, moving beyond binary preference classification to generate individualized, latency-sensitive utility functions [25][19]. These models are further enhanced by fusion with eye-tracking and response latency data, achieving 89% classification accuracy and capturing attentional capture and emotional valence as co-determinants of choice, thereby aligning pricing mechanisms with the pre-decisional neural architecture of consumer behavior [20][31]. Consequently, optimal pricing strategies no longer seek to adjust price points to match aggregate elasticity but instead engineer behavioral nudges calibrated to neurocognitive thresholds—leveraging network effects, temporal incentives, identity signaling, and cognitive dissonance as primary levers of value extraction [7][47][9]. This shift has been empirically validated across contexts: in ride-hailing, where demand persistence under surge pricing is driven by behavioral inertia rather than price sensitivity [4]; in luxury retail, where brand trust suppresses substitution and stabilizes WTP without price changes [9]; and in digital platforms, where social proof mechanisms outperform discounts in enhancing long-term revenue [47].

Yet this innovation exists within a profound regulatory and ethical vacuum. Current data protection frameworks—GDPR, CCPA, and their state-level derivatives—treat neural data as an extension of biometric or health information, failing to recognize its sui generis nature as a direct window into cognitive sovereignty. The doctrine of neurodata exceptionalism, as codified in Chile’s landmark 2023 Girardi v. Emotiv Inc. ruling, establishes that neural signals constitute the most intimate aspect of personal identity, demanding legal protection not merely from misuse but from inferential exploitation altogether [41]. This jurisprudential anchor, grounded in the right to mental privacy and cognitive freedom, renders traditional consent mechanisms—often embedded in adhesion contracts and undermined by re-identification risks through functional brain fingerprinting—legally inadequate [41]. Compounding this, the convergence of neural data with quantum computing threatens to nullify anonymization protocols, demanding that behavior-elastic pricing architectures be designed with post-quantum cryptographic safeguards and on-device inference as non-negotiable infrastructure, not optional enhancements [12][24]. The absence of a globally enforceable regulatory taxonomy creates a patchwork of jurisdictional arbitrage, where firms deploy inferential pricing systems in regions with weak or ambiguous neurodata protections, exposing consumers to cognitive discrimination and covert manipulation under the guise of market efficiency [39].

This report systematically traces the theoretical, empirical, technical, and legal dimensions of this paradigm shift. It begins by deconstructing the foundational assumptions of neoclassical demand theory and their empirical failures [section_1], followed by an examination of the neuro-marketing methodologies—EEG and fMRI—that enable the direct measurement of behavioral elasticity [section_2]. The subsequent section details the computational frameworks for deriving behavior-elastic demand curves from neuro-data, emphasizing economic validity over classification accuracy [section_3]. Building on this foundation, the fourth section explores how optimal pricing strategies are reconfigured under this new paradigm, shifting from static price points to dynamic neurocognitive nudges [section_4]. The fifth section confronts the algorithmic and infrastructural challenges of scaling these systems, from signal fidelity to domain adaptation and real-time inference constraints [section_5]. The sixth section interrogates the ethical implications of inferential pricing, particularly the erosion of autonomy under non-consensual cognitive modeling [section_6]. The seventh section anchors the discussion in the emerging legal landscape, with Chile’s neurorights jurisprudence serving as the only enforceable constraint on commercial neuropricing [section_7]. Finally, the eighth section evaluates the empirical validation, generalizability, and future research directions necessary to ensure equitable, robust, and economically sound deployment of behavior-elastic demand models across heterogeneous populations and markets [section_8]. Together, these sections construct a comprehensive, technically rigorous, and legally grounded framework for rethinking demand not as a function of price, but as a function of the measurable neural states that underlie human choice.

2. Foundations of Neoclassical Demand Theory and Its Limitations¶

The foundations of neoclassical demand theory are anchored in the formalism established by Alfred Marshall in his 1890 Principles of Economics, which synthesized marginal utility theory with cost-based supply to derive price elasticity of demand as a quantitative measure of quantity responsiveness to price changes [10]. Marshall’s model presumed rational, utility-maximizing agents who adjust consumption until marginal utility equaled price, constructing demand curves as smooth, continuous, and deterministic functions derived from stable, exogenous preferences [10]. This framework operationalized consumer surplus as the area under the demand curve, implicitly assuming that price was the sole determinant of quantity demanded—a simplification designed for mathematical tractability rather than empirical fidelity [10]. The model’s reliance on ceteris paribus—holding income, preferences, and prices of related goods constant—enabled comparative statics but rendered it fundamentally disconnected from the dynamic, context-sensitive nature of real-world decision-making [26]. As a result, the Marshallian demand curve became the dominant analytical tool in microeconomics, despite its foundational assumptions being ungrounded in psychological or neurocognitive reality [10].

Empirical and theoretical critiques from behavioral economics have since exposed systematic failures in predicting consumer behavior under these assumptions. Expected utility theory, central to price-elastic models, cannot account for loss aversion, wherein losses loom larger than equivalent gains, violating the symmetry of utility functions underlying demand elasticity [18]. Reference dependence further destabilizes the model: consumers evaluate prices not in absolute terms but relative to an anchored reference point, as demonstrated by the effectiveness of premium pricing strategies (e.g., Starbucks’ Evian water) or juxtaposed product listings (e.g., Amazon’s $1,000 vs. $1,200 laptops), which manipulate perceived value through relative loss framing [21]. Mental accounting, another violation of fungibility assumptions, shows that consumers categorize money differently based on its origin—tax refunds versus wages, for instance—leading to non-rational spending patterns unmodeled by traditional elasticity [18]. Hyperbolic discounting reveals time-inconsistent preferences, where individuals disproportionately favor immediate gratification over long-term utility, distorting intertemporal demand responses in ways exponential discounting cannot capture [18]. These deviations are not stochastic noise but systematic, predictable biases that render static price elasticity models structurally inadequate [18].

The functional forms traditionally adopted to represent demand—linear ($y(p) = a - bp$) and iso-elastic ($y(p) = dp^{-e}$)—further illuminate the limitations of classical theory. Linear demand assumes constant marginal responsiveness to price changes, while iso-elastic demand assumes constant elasticity across all price levels, both of which are mathematical conveniences that ignore the non-linear, context-dependent nature of real willingness-to-pay [30]. Game-theoretic analyses of supply chains reveal that even within neoclassical frameworks, the choice of demand function alters strategic outcomes: under linear demand, leadership in pricing yields efficiency gains, whereas under iso-elastic demand, leadership provides no advantage, demonstrating that demand elasticity is not an inherent property but a contingent outcome of institutional structure and sequencing [30]. Yet even these advanced models operate under the assumption of perfect information, rational expectations, and profit-maximizing agents, failing to incorporate cognitive biases, emotional valence, or social influences that empirically drive behavior [30]. The assumption of homogenous, linear utility functions—commonly used for tractability—has also been shown to artificially suppress phenomena like the profitability of probabilistic selling, which emerges naturally under convex preference structures, a central tenet of standard microeconomic theory [27]. This reveals that the failure to observe certain market behaviors in classical models stems not from market reality but from modeling artifacts rooted in oversimplified utility assumptions [27].

Recent empirical findings reinforce that price is not the primary driver of demand in many critical contexts. In ride-hailing markets, driver collusion to trigger surge pricing by artificially reducing supply demonstrates that demand persistence under scarcity is governed by situational necessity and algorithmic triggers, not price elasticity [4]. Similarly, in digital two-sided platforms offering experience goods—such as dining, spas, or local services—demand is driven by cross-period same-side network effects (SNE₋₁), where future consumer adoption is influenced by the observed behavior of prior users through observational learning and word-of-mouth, rendering price sensitivity secondary [47]. In such markets, consumer welfare and revenue optimization improve not through discounting but through enhancing social proof mechanisms, as seen in Meituan’s acquisition of Dianping or Airbnb’s host forums [47]. The counterintuitive finding that experience goods exhibit higher price elasticity than search goods, yet derive greater value from behavioral signals, underscores that price elasticity is not only insufficient but potentially misleading when behavioral drivers dominate [47]. Moreover, under nonstationary demand conditions, human revenue managers consistently anchor pricing decisions on recent observed willingness-to-pay rather than expected or optimal values, revealing that real pricing behavior is dynamic, bias-driven, and adaptive—a phenomenon captured by behavior-elastic curves but entirely absent from static elasticity models [23]. This strategic misalignment creates exploitable arbitrage opportunities for firms using neuro-behavioral data to anticipate and respond to shifting anchors, while traditional models systematically overprice or underprice [23].

Ultimately, the neoclassical demand curve, though foundational in its formalization, is a theoretical abstraction that ignores the neurocognitive, emotional, and contextual determinants of choice. Its reliance on ceteris paribus, rational optimization, and stable preferences has been systematically undermined by behavioral economics, empirical game theory, and digital platform dynamics, all of which point to demand as a function of reference points, loss aversion, network effects, cognitive friction, and strategic interactions—not merely price [21][26][34]. The transition to behavior-elastic demand curves, derived from neuro-marketing data such as EEG-derived willingness-to-pay, is not merely an incremental improvement but a necessary paradigm shift: from modeling demand as a function of price to modeling it as a function of the internal, measurable neural states that underlie decision-making under uncertainty, social influence, and cognitive bias. The following section will examine the neuro-marketing methodologies—EEG and fMRI—that enable the empirical recovery of these behavioral elasticities, providing the empirical bridge between theoretical critique and operational innovation.

3. Neuro-Marketing Methodologies: EEG and fMRI for Measuring Behavioral Elasticity¶

Neuro-marketing methodologies centered on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide empirically validated, non-invasive means of measuring behavioral elasticity by directly quantifying neural correlates of willingness-to-pay (WTP), thereby supplanting traditional reliance on stated or revealed preferences. EEG, with its high temporal resolution (sampling rates up to 300 Hz), captures the millisecond-scale dynamics of neural valuation processes, making it uniquely suited for tracking real-time decision-making under pricing stimuli. The international 10–20 electrode system, standardized across studies, enables reproducible acquisition of signals from frontal (F3, F4, F7, F8, Fpz), temporal, and occipital regions critical for affective and cognitive valuation, with commercial systems like Emotiv EPOC+ and DSI-24 demonstrating feasibility for scalable deployment [8]. Key neurophysiological proxies for WTP include frontal alpha asymmetry (FAA), where lower relative left-hemisphere alpha power (8–12 Hz) indicates approach motivation and increased preference, and gamma band (30–45 Hz) power in the left prefrontal cortex, which is consistently correlated with higher WTP [8][20]. The late positive potential (LPP), a late ERP component (>400 ms post-stimulus), reflects conscious emotional evaluation of products and advertisements, while the N400 component signals semantic incongruity—such as price-brand mismatches—providing complementary insights into cognitive dissonance during valuation [20][31]. Critically, prefrontal gamma asymmetry (PAIγ), calculated as (F3−F4)/(F3+F4), has been shown to be a robust, time-sensitive predictor of WTP, with a significant linear relationship (β = 0.161, p = 0.0004) that intensifies over the 3-second decision window, peaking near choice onset (R² = 0.315) and remaining predictive even within the first second of exposure (R² = 0.309), suggesting rapid, automatic value encoding preceding deliberation [14][28]. This temporal escalation mirrors the hypothesized functional coupling between the medial orbitofrontal cortex (mOFC) for value computation and dorsolateral prefrontal cortex (dlPFC) for choice execution, positioning PAIγ as a scalp-level proxy for dynamic cognitive moderator states under nonstationary demand [14][28].

The operational pipeline for translating these signals into behavioral elasticities requires rigorous experimental design and signal processing. Stimuli—typically static product images (e.g., 42–480 variants) or real product exposure (e.g., wine tasting)—are selected to elicit ecologically valid responses in digital retail contexts, with paired comparisons (e.g., branded vs. unbranded, real vs. fake prices) enabling direct estimation of price elasticity at the neural level [8]. To ensure WTP reflects true subjective value, the Becker-DeGroot-Marschak (BDM) incentive-compatible procedure is employed, where participants bid real money for products, aligning neural responses with economic outcomes [25][19]. Preprocessing is non-negotiable: artifacts from ocular (EOG), cardiac (ECG), muscular (EMG), and power-line (50/60 Hz) interference are removed using automated algorithms, and low-frequency drifts from electrode motion are corrected to preserve signal fidelity [8]. The adoption of the Brain Imaging Data Structure (BIDS) format—standardizing file naming, metadata, and directory organization—ensures interoperability, reproducibility, and integration with automated preprocessing pipelines (e.g., fMRIPrep, MRIQC), transforming fragmented lab data into scalable, shareable inputs for machine learning [35]. Publicly available datasets (e.g., Yadava et al. [8], Georgiadis et al. [125], Mashrur et al. [126]) with full experimental metadata serve as benchmarks for model training and validation, institutionalizing best practices [8][20].

While EEG excels in temporal precision, fMRI provides unparalleled spatial resolution (1–10 mm) for localizing the neural substrates of valuation. The orbitofrontal cortex (OFC) has been consistently identified as a core region encoding WTP during everyday economic transactions, with Plassmann et al. [89] establishing that BOLD signal amplitude in the OFC quantitatively predicts consumer willingness to pay beyond self-reports [6][44]. High prices activate the insula, a region associated with pain and aversion, while discounts enhance activity in reward-processing regions such as the ventral striatum, directly linking perceived value to neurophysiological responses [6]. The foundational fMRI study by Plassmann et al. [90] on marketing-induced modulation of experienced pleasantness—cited over 600 times—demonstrates that neural representations of value are malleable to branding and contextual cues, directly challenging the neoclassical assumption of stable, exogenous preferences [6]. These spatially precise measures reveal that behavioral elasticity is not a unitary construct but is dynamically modulated by emotional and cognitive states encoded in distributed networks, including the amygdala (emotion), anterior cingulate cortex (conflict monitoring), and vmPFC (integrated value), offering a richer, biologically grounded substrate for demand modeling than aggregate market transactions [6][44].

A critical insight from both modalities is that neural WTP signatures are not universal but are profoundly context-conditioned. EEG studies demonstrate that the relationship between PAIγ and WTP reverses direction across product categories: it is positively associated with WTP for luxury goods (bags, shoes) but negatively associated with fast-moving consumer goods (FMCG) (t = −2.80, p = 0.006), indicating that FMCGs elicit heightened price sensitivity mediated by different neural pathways [14][28]. This category-specific modulation necessitates adaptive, not fixed, behavioral elasticity functions—a departure from the homogeneous assumptions of traditional models. The convergence of these findings with fMRI’s identification of OFC as a value-encoding hub suggests that the neural architecture underlying demand is fundamentally heterogeneous, shaped by product identity, emotional salience, and cognitive context. The integration of EEG and fMRI data is therefore not merely complementary but essential: EEG captures the real-time dynamics of valuation as it unfolds, while fMRI localizes the cortical networks that generate those dynamics, enabling a dual-layered model of behavioral elasticity grounded in both temporal and spatial neurobiology [44].

To derive continuous WTP functions from these signals, deep learning architectures have replaced manual feature extraction, which introduced experimenter bias and limited generalizability. The DeePay model, trained on 35,000+ EEG-WTP pairs from 183 subjects across six product categories, employs an end-to-end deep neural network that processes raw, minimally preprocessed EEG data (8-channel, 250 Hz) to predict continuous WTP values without predefined frequency bands or electrode averaging, achieving 75.09% classification accuracy and an RMSE of 0.276 [25][19]. Network visualizations reveal predictive spatiotemporal patterns localized to frontal electrodes (Fp1, Fp2, Fpz, F7, F8, Fz), confirming the biological validity of the learned representations. This architecture is inherently extendable to multimodal inputs: while DeePay did not incorporate eye-tracking, its design allows for parallel input channels—such as fixation duration or pupil dilation—to be fused as additional feature streams, enabling the synthesis of attentional, emotional, and neural signals into a unified WTP function [25][19]. The use of machine learning, particularly deep neural networks, has consistently outperformed traditional regression and SVM approaches, achieving mean classification accuracy of 89% when combining EEG and eye-tracking, and mitigating the low effect sizes and replication failures endemic to frequentist neuro-marketing studies [20][31]. Critically, these models move beyond binary preference classification to generate individualized, continuous WTP functions that reflect latent utility, directly enabling the replacement of static, price-elastic demand curves with dynamic, neuro-data-driven behavior-elastic curves [8][19]. The transition to these methods is not incremental but paradigmatic: it shifts demand modeling from inference based on observable transactions to direct measurement of the internal, measurable neural states that generate choice, establishing a new empirical foundation for pricing theory grounded in neurocognitive reality.

4. Deriving Behavior-Elastic Demand Curves from Neuro-Data¶

The derivation of behavior-elastic demand curves from neuro-marketing data represents a fundamental reconceptualization of demand as a function of latent neural states rather than exogenous price changes. Central to this framework is the operationalization of willingness-to-pay (WTP) as a continuous, neurophysiologically grounded variable, mapped from high-temporal-resolution EEG signals and spatially precise fMRI activations. The most robust neural proxy for WTP is the Prefrontal Asymmetry Index in the gamma band (PAIγ), computed as $(F3 - F4)/(F3 + F4)$ from bilateral frontal electrodes, which demonstrates a significant linear relationship with stated WTP (β = 0.161, t = 3.55, p = 0.0004) and accounts for 27.0% of variance independently, increasing to 64.1% when combined with product category [28]. Unlike alpha-band asymmetry, which shows no consistent predictive power (t = −1.07, p = 0.286), PAIγ exhibits dynamic temporal evolution: its predictive strength increases over the 3-second decision window, peaking at R² = 0.315, indicating that neural value computation is not static but unfolds during deliberation—a critical departure from the instantaneous, exogenous price-response assumption of classical demand theory [28]. This temporal trajectory aligns with the neurobiological sequence of valuation, where early gamma oscillations in the left prefrontal cortex reflect initial utility encoding, followed by sustained activation of the ventromedial prefrontal cortex (vmPFC) during integrated value formation, which is directly observable via fMRI as amplitude-modulated BOLD signals [6][44]. The vmPFC’s BOLD response has been quantitatively shown to predict WTP beyond self-reports, with signal intensity linearly proportional to subjective value, establishing it as a core neural substrate for demand [6][44].

These neural signals are not universal but are profoundly context-conditioned, necessitating category-specific calibration of behavior-elastic curves. PAIγ positively predicts WTP for luxury goods such as bags and shoes but negatively predicts it for fast-moving consumer goods (FMCG) (t = −2.80, p = 0.006), revealing that the same neurophysiological marker can encode opposite economic behaviors depending on product identity and perceived necessity [28]. This context-dependence is further reinforced by fMRI findings that the insula (associated with pain and aversion) activates under high prices, while the ventral striatum (reward) responds to discounts, demonstrating that the neural architecture of valuation is modulated by both affective and cognitive pathways [6]. To translate these discrete neural responses into continuous demand functions, end-to-end deep learning architectures have replaced manual feature extraction, which introduced bias and limited generalizability. The DeePay model, trained on over 35,000 EEG-WTP pairs across six product categories, processes raw, minimally preprocessed EEG data (8-channel, 250 Hz) to output continuous WTP estimates without predefined frequency bands or electrode averaging, achieving an RMSE of 0.276 and 75.09% classification accuracy [25][19]. Network visualizations confirm that predictive features are localized to frontal electrodes (Fp1, Fp2, Fpz, F7, F8, Fz), validating the biological fidelity of the learned representations [25][19]. This architecture is inherently extensible: multimodal inputs such as eye-tracking-derived fixation duration or pupil dilation can be fused as parallel feature streams, enabling the integration of attentional dynamics and neural valuation into a unified behavioral elasticity function [25][19]. When EEG is combined with eye-tracking via deep learning, classification accuracy rises to 89%, significantly outperforming single-modality approaches and mitigating the replication crises that plagued earlier frequentist neuro-marketing studies [20][31].

The economic validity of these neuro-data-derived WTP functions is not assessed by classification accuracy alone, but by their ability to reproduce empirically grounded behavioral economic metrics derived from mixed-effects modeling. The parameter α, defined as the rate of change in elasticity across the demand curve, serves as the core elasticity slope to be calibrated via neural inputs [5]. Mixed-effects models treat α and Q₀ (intensity) as random effects, allowing estimation of both population-level trends and individual neurocognitive heterogeneity within a single framework—critical for generalizing behavior-elastic curves across diverse consumers without sacrificing individual variability [5]. Economic validity is further anchored in the reproduction of Omax (maximum expenditure) and Pmax (price at which expenditure peaks), which are empirically derived from real-world purchase tasks and represent direct revenue optimization targets [5]. Behavior-elastic curves must not only predict WTP but must replicate these economic thresholds under neural perturbations; failure to do so renders them descriptive rather than prescriptive [5]. Temporal stability is assessed through repeated-measures designs, where the same neurocognitive stimuli are presented across trials to evaluate within-subject consistency in WTP estimation, with error variance and maximum likelihood estimation ensuring probabilistic model stability [5]. This framework transforms demand modeling from static, aggregate price-quantity mappings to dynamic, individualized functions f(γ_asymmetry, time, category, goal_specificity) → WTP, where demand elasticity is a function of neurocognitive state, not price [28].

Real-world validation of this approach emerges from studies demonstrating that demand in digital platforms is governed not by price elasticity but by neurobehavioral triggers. In e-commerce, product popularity and time restriction cues interact non-linearly with consumer goal specificity: under low goal specificity, popularity drives approach behavior, while time restriction is ineffective; under high goal specificity, the two cues synergistically reinforce purchase intent (β₃ = 0.65, p < 0.05), demonstrating that demand curves are dynamically recalibrated in real time by internal cognitive states measurable via eye-tracking and response latency [16]. Similarly, in ride-hailing, demand persistence under surge pricing is driven not by price elasticity but by behavioral thresholds of waiting-time sensitivity and driver collusion, which alter the behavioral elasticity surface without changing base prices—validating that optimal pricing must shift from per-unit pricing to nudge architectures rooted in neurocognitive disutility [4]. In refurbished markets, firms leveraging behavior-elastic curves outperform competitors using traditional models by exploiting brand-specific switching behavior: a strong brand increases its refurbished price when internal cannibalization is high, protecting its premium segment—a counterintuitive move under price-elastic assumptions but rational under behavioral elasticity [17]. This strategic asymmetry is only visible when demand is modeled as a function of neural and behavioral proxies (e.g., brand trust, perceived substitution) rather than price sensitivity alone [17]. The same principle applies to platform markets with positive network externalities, where consumer utility is modeled as $ u = -p + f(q) - \varepsilon $, with $ f(q) $ capturing the value of participation rate and $ \varepsilon $ representing unobserved preference heterogeneity; under rational expectations, price sensitivity is amplified by the factor $ \frac{1}{1 - s f' \phi} $, where $ s $ is adjustment friction and $ \phi $ is preference dispersion—formalizing a behavior-elastic curve where elasticity is endogenously shaped by network dynamics and cognitive barriers, not exogenous price [34].

Despite these advances, scalability and generalizability remain critical challenges. Traditional classifiers like SVMs and k-NN suffer from poor generalizability across diverse populations and recording conditions due to high-dimensional noise and reliance on handcrafted features [29]. The transition from controlled lab environments to real-world deployment demands robust, data-driven architectures such as the EEG Mind-Transformer, which learns invariant representations across heterogeneous datasets, reducing the need for per-subject calibration [29]. Domain adaptation techniques, particularly adversarial learning and behavioral covariate conditioning within integrated learning and optimization (ILO) frameworks, are essential to generalize behavior-elastic curves to low-data segments (e.g., new demographics or markets) without introducing bias [42]. ILO, which jointly trains prediction (neural WTP mapping) and optimization (revenue maximization) objectives, preserves the non-linear encoding of neurocognitive signals and avoids the error propagation inherent in predict-then-optimize pipelines [42]. Furthermore, fairness-aware regularization and counterfactual explanations ensure that pricing disparities are not artifacts of spurious covariate correlations but reflect genuine neurocognitive differences, enabling equitable deployment [42]. The BIDS-standardized infrastructure for neuroimaging data provides a foundational layer for reproducibility, but real-time quality control, ecological calibration, and minimal identifier retention protocols remain underdeveloped for commercial deployment [1]. The convergence of these methodological advances—neural signal mapping, economic metric calibration, multimodal fusion, domain adaptation, and ILO—establishes a comprehensive, empirically grounded pipeline for deriving behavior-elastic demand curves that directly replace price-elastic models with neurocognitively validated, dynamically adaptive, and economically valid alternatives.

Having established the mathematical and empirical foundations for behavior-elastic demand curves, the following section will examine how these curves fundamentally alter the architecture of optimal pricing strategies across diverse market contexts, shifting the optimization objective from static price points to dynamic behavioral nudges governed by neurocognitive thresholds.

5. Optimal Pricing Strategies Under Behavior-Elastic Demand¶

Optimal pricing strategies under behavior-elastic demand curves fundamentally reconfigure the objectives of revenue maximization, shifting focus from static price-point optimization to dynamic, neurocognitively calibrated nudge architectures that exploit context-sensitive behavioral thresholds. Unlike price-elastic models, which assume demand responds uniformly to monetary incentives, behavior-elastic frameworks treat willingness-to-pay (WTP) as a latent, neurophysiologically grounded function modulated by cognitive state, social context, and strategic information architecture—enabling firms to extract surplus through non-price levers that align with the neural mechanisms of value construction. In digital platforms characterized by positive network externalities, optimal pricing is no longer determined by marginal revenue equals marginal cost under fixed elasticity, but by achieving and sustaining a critical mass threshold of adoption, where the shadow price of additional users becomes infinite. This phenomenon, formalized in dynamic control models using Pontryagin’s maximum principle, reveals that advertising investment—not price discounts—becomes the primary lever for profitability, as consumer utility is embedded in network size rather than unit cost, with firms strategically stalling at the threshold to maintain equilibrium and maximize long-term revenue [3]. The amplification of price sensitivity under rational expectations, quantified as $ \frac{1}{1 - s f' \phi} $, where $ s $ represents cognitive adjustment friction, $ f' $ captures network value sensitivity, and $ \phi $ denotes preference heterogeneity, further demonstrates that demand elasticity is endogenously shaped by neurobehavioral dynamics, not exogenous pricing [34]. In such environments, pricing becomes a mechanism of expectation management and social proof orchestration, where perceived value is anchored in adoption momentum rather than monetary cost.

In ride-hailing markets, behavior-elasticity reveals a counterintuitive win-win between revenue optimization and environmental sustainability: when consumer price sensitivity is low (βₚ = −0.084) and the perceived inconvenience of pooling is high (αₚ = −1.821), increasing the relative price differential between individual and pooled rides—rather than lowering absolute prices—boosts pooled ride adoption by 22.7% and reduces vehicle miles traveled (VMT) by 32% over a decade, while simultaneously increasing platform revenue [ref2283585e][32]. This strategy exploits behavioral inertia and loss aversion toward shared travel, transforming a non-price barrier into a structural pricing lever invisible to classical elasticity models. The optimal trajectory diverges from cost-pass-through under autonomous vehicle adoption; instead of reducing prices proportionally, firms amplify the behavioral disincentive to pool, leveraging the neurocognitive disutility of shared travel to steer demand toward greener alternatives without sacrificing profitability [ref2283585e][32]. In contrast, in developing markets with high price sensitivity (βₚ = −0.336) and low pooling inconvenience (αₚ = −0.202), behavior-elasticity contributes minimally to revenue gains, confirming that the paradigm is most potent in mature, affluent, luxury-like service contexts where non-price attributes dominate choice [32]. This distinction underscores that behavior-elastic demand is not universally applicable but is contextually emergent, requiring firms to identify market conditions where behavioral barriers outweigh monetary responsiveness.

In experience goods markets—such as dining, local services, or premium digital subscriptions—demand persistence is governed by cross-period same-side network effects (SNE₋₁), where future consumer adoption is driven by observational learning and word-of-mouth from prior users, not by price sensitivity [47]. Platforms like Airbnb and Meituan enhance CLV2 and revenue not through discounts but by designing features that amplify behavioral signals: user reviews, host forums, and rating systems serve as neurocognitive anchors that reduce quality uncertainty and elevate perceived value, creating a self-reinforcing loop of trust and adoption [47]. Traditional price-elastic models fail to capture this mechanism, leading to underestimation of platform value and misallocation of marketing resources toward price incentives rather than social proof infrastructure. Similarly, in high-end retail, firms such as Bergdorf Goodman and Farfetch strategically hinder consumer information sharing—suppressing online reviews—to preserve and amplify brand-driven quality beliefs (qc), enabling simultaneous targeting of high- and low-WTP segments under Strategy 𝒩 [9]. By decoupling consumer expectations from volatile product attributes, these retailers stabilize perceived value, increase marginal profit, and expand their target segment without altering price points, demonstrating that demand is shaped by identity, status signaling, and strategic information withholding, not price elasticity [9]. The profit-maximizing strategy here is not to lower prices to increase volume, but to engineer cognitive dissonance around substitution, reinforcing brand loyalty as a non-price barrier to entry.

The shift from price- to behavior-elasticity is further evident in digital product ecosystems governed by network effects and sequential adoption. In markets dominated by product rollovers, optimal pricing shifts from per-unit discounting to temporal incentive design, where the timing of successor releases becomes the primary lever for revenue extraction [7]. For sophisticated consumers who anticipate future releases, the firm must delay the launch of the next version (t₂) to induce sequential adoption, setting p₂* > p₁* even under low perceived obsolescence (α), exploiting cognitive dissonance between current consumption and future utility [7]. Simultaneous product availability (dual rollover) is suboptimal, as it triggers strategic waiting and cannibalization—not due to price sensitivity, but because consumers weigh the cognitive cost of purchasing an obsolete product against the anticipated value of the future version [7]. This reframes pricing as a dynamic control policy, where the firm’s optimization problem is defined over release timing and price, with state variables including consumer patience (δc) and anticipation (γ), yielding non-linear, non-monotonic optimal paths that cannot be captured by static elasticity functions [7]. The firm’s ability to manipulate the temporal structure of demand—engineering “naive preference orderings” through information asymmetry—demonstrates that behavior-elastic curves are not merely predictive tools but strategic instruments for shaping consumer cognition.

A critical insight from experimental revenue management studies is that human decision-makers consistently deviate from optimal pricing thresholds due to cognitive biases rooted in anchoring, loss aversion, and optimism bias, which traditional elasticity models cannot account for [23]. Participants in controlled settings exhibit “inventory mis-sensitivity,” systematically overpricing due to optimism bias or underpricing due to loss aversion, with these errors persisting even after repeated exposure and accurate demand forecasts [23]. These deviations are not stochastic noise but structured, heuristic-driven behaviors—mirroring real-world pricing practices such as CHASING (anchoring to recent WTP) or ANCHOR-ŵₜ (relying on expected WTP)—which induce persistent, non-random errors in revenue outcomes [23]. Behavior-elastic pricing, by contrast, leverages neuro-data to anticipate and correct for these biases, replacing reactive, heuristic-based pricing with proactive, neurocognitively informed control. When combined with deep learning architectures like DeePay, which map real-time EEG signals to continuous WTP estimates with 75.09% accuracy and RMSE of 0.276, firms can dynamically adjust pricing to align with the unfolding neurophysiological valuation process, ensuring optimal thresholds are met before cognitive bias distorts consumer response [25][19]. The integration of multimodal inputs—EEG, eye-tracking, and response latency—further refines this control, capturing attentional capture and emotional valence as co-determinants of WTP, enabling pricing systems to respond not just to what consumers say or do, but to what their brains signal before they act [25][19].

The strategic advantage conferred by behavior-elastic pricing is not merely operational but structural, altering competitive equilibrium in duopolistic markets. When one firm deploys an algorithmic pricing system trained on neuro-behavioral demand patterns, it can infer private demand signals from the competitor’s public price path, converging to supra-competitive equilibria without explicit collusion [11]. This “accidental signaling” channel allows the algorithmic firm to sustain higher market share and revenue by leveraging the neurocognitive calibration embedded in its pricing, while competitors relying on traditional price-elastic models systematically misprice and lose share [11]. The algorithm’s convergence to collusive outcomes under axiomatic definitions of Pareto optimality—regardless of firm asymmetry—reveals that behavior-elastic data, when algorithmically processed, becomes a strategic asset that reshapes market power, not by violating competition law, but by exploiting its informational blind spots [11]. This transformation renders pricing not a static function of cost and demand, but a dynamic, inferential process where the firm’s pricing trajectory encodes and transmits behavioral intent, making neuro-data the new frontier of competitive differentiation.

These findings collectively demonstrate that optimal pricing under behavior-elastic demand is not a refinement of classical models but a paradigmatic reorientation: from maximizing revenue through price adjustments to maximizing value extraction through neurocognitive architecture. The key levers are no longer marginal cost and price elasticity, but network thresholds, behavioral disincentives, temporal incentives, information control, and cognitive bias anticipation—all calibrated via empirically validated neural proxies such as PAIγ, vmPFC BOLD amplitude, and WTP maps derived from BDM auctions [6][28][38]. The economic validity of these strategies is confirmed by their ability to reproduce core demand function properties—elasticity slope, revenue-maximizing thresholds, and convex utility structure—under neurocognitive perturbation, not as statistical fits but as structural outcomes derived from behavioral game theory and dynamic control [34][7]. Success requires moving beyond predictive accuracy to economic calibration: behavior-elastic curves must replicate empirically grounded metrics like Omax and Pmax under neural perturbation, ensuring they are not merely descriptive but prescriptive [5]. The transition to this paradigm demands a new technical infrastructure—one where real-time neuro-signal acquisition, multimodal fusion, adversarial domain adaptation, and ILO frameworks are embedded within legally constrained, quantum-resistant architectures, as mandated by neurorights jurisprudence [42][40]. The following section will examine the algorithmic and technical implementation challenges inherent in scaling these behavior-elastic pricing architectures from controlled laboratory environments to live, high-stakes commercial systems, where latency, privacy, and regulatory compliance become non-negotiable constraints.

6. Algorithmic and Technical Implementation Challenges¶

The deployment of behavior-elastic pricing systems faces formidable algorithmic and technical barriers that stem from the inherent complexity of neurophysiological data, the demands of real-time inference, and the structural constraints of existing commercial infrastructure. Unlike price-elastic models, which operate on aggregated transactional records, behavior-elastic systems require the continuous, low-latency acquisition, preprocessing, and interpretation of high-dimensional, noisy neural signals—primarily EEG and eye-tracking data—under ecologically valid conditions. The transition from controlled laboratory environments to dynamic, real-world pricing contexts exposes critical vulnerabilities in signal fidelity and generalizability. EEG data, despite its high temporal resolution (up to 300 Hz), is highly susceptible to artifacts from ocular, muscular, and environmental noise, necessitating robust, automated preprocessing pipelines such as ASR and FORCE to preserve signal integrity [8]. However, current standards like BIDS, while ensuring reproducibility and interoperability across research cohorts, lack real-time quality control mechanisms and ecological calibration protocols essential for consumer-facing deployment, such as context-aware normalization for ambient lighting, cognitive load, or behavioral state alignment [1]. Furthermore, the 22.5% participant attrition rate observed in web-deployed eye-tracking studies—due to hardware incompatibility, calibration failure, or non-compliance—highlights a systemic scalability challenge: neuro-data collection is not plug-and-play, demanding specialized hardware, participant training, and real-time monitoring that are economically prohibitive for mass-market pricing systems [33].

At the algorithmic core, the mapping of neural signals to continuous willingness-to-pay (WTP) functions requires architectures that overcome the limitations of traditional machine learning methods. Conventional classifiers like SVMs and k-NN exhibit poor generalizability across heterogeneous populations and recording conditions, succumbing to overfitting in high-dimensional feature spaces and failing to capture the full complexity of neurophysiological variability [29]. While deep learning models such as DeePay achieve 75.09% classification accuracy and RMSE of 0.276 by processing raw, minimally preprocessed EEG data without predefined frequency bands, their performance degrades significantly when applied to low-data segments—new demographics, geographic markets, or product categories—due to covariate shift and domain mismatch [25][19]. This necessitates advanced domain adaptation techniques grounded in integrated learning and optimization (ILO), where prediction (neural WTP mapping) and optimization (revenue maximization) are jointly trained end-to-end [42]. Frameworks like SPO+, DBB, and PyEPO enable this paradigm, preserving the non-linear encoding of latent utility from multimodal neural streams while directly optimizing for economic objectives, thereby avoiding the error accumulation inherent in predict-then-optimize pipelines [42]. Crucially, ILO must be augmented with behavioral covariate conditioning, using adversarial learning and distributionally robust optimization (DRO) with Wasserstein ambiguity sets to align distributions across segments and mitigate bias in pricing outcomes, ensuring equitable generalization without relying on large, costly neuro-data repositories [42][33].

The integration of multimodal data streams—EEG, eye-tracking, and potentially fNIRS or facial expression analysis—further compounds computational complexity. While fusion via deep learning improves predictive accuracy to 89%, current architectures lack standardized protocols for temporal alignment, sampling rate harmonization (e.g., EEG at 250 Hz vs. eye-tracking at 120 Hz), and cross-attention-based fusion of spatiotemporal features [45]. The absence of Transformer-based designs incorporating positional encoding for neural latency and gaze dynamics limits the ability to model the dynamic interplay between attentional allocation and value computation, a critical factor in behavior-elastic demand [45]. Moreover, the computational cost of real-time inference on edge devices—required to preserve privacy and reduce latency—is prohibitive for complex models. Deploying full-scale DeePay or multimodal Transformers on mobile or IoT platforms demands model compression, quantization, and hardware-aware optimization, which risk degrading the very neurocognitive fidelity these systems aim to preserve.

Scalability is further constrained by data efficiency. Neuro-marketing data acquisition is prohibitively expensive and context-specific, making it infeasible to collect sufficient labeled WTP pairs for every target segment. Active learning strategies, as proposed by Liu et al. (2023), offer a viable path forward by minimizing the number of neuro-trials needed to train robust policies through targeted data sampling, maximizing signal-to-noise ratio for underrepresented groups [42]. However, this requires sophisticated uncertainty estimation and iterative feedback loops that are absent in most commercial pricing engines. Equally critical is the challenge of causal identification: behavior-elastic curves must encode true neurocognitive WTP, not spurious correlations with demographic proxies. Alley et al. (2023) provide a methodological anchor through novel loss functions that isolate the causal effect of price on demand, disentangling neural valuation from confounding contextual variables—a prerequisite for generalizable, non-discriminatory pricing [42]. Yet, this demands rigorous counterfactual auditing, as introduced by Forel et al. (2023), to identify minimal contextual changes that alter pricing decisions and ensure disparities reflect genuine neurocognitive differences rather than algorithmic bias [42].

Perhaps the most urgent technical challenge lies in the convergence of neuro-data sensitivity and emerging computational threats. Functional brain fingerprinting has demonstrated that EEG signals can uniquely identify individuals, qualifying neuro-data as biometric personal data under GDPR Article 9 [43]. Traditional anonymization techniques are rendered obsolete by quantum computing, which enables adversarial re-identification attacks capable of reconstructing neural profiles from obfuscated datasets [12]. Consequently, behavior-elastic pricing architectures must embed post-quantum cryptographic safeguards, quantum-resistant neural network architectures, and quantum scrambling techniques for differential privacy from the ground up—not as optional add-ons, but as non-negotiable infrastructure [12]. This demands a fundamental rethinking of data flow: centralized inference models are untenable; decentralized, on-device processing via federated learning, coupled with blockchain-based audit trails for consent and data provenance, is required to maintain compliance with neurorights jurisprudence and participant-centric governance frameworks [24]. The absence of such architectures renders even the most accurate behavior-elastic models legally and ethically non-viable.

Finally, failure modes are not merely technical but systemic. Algorithmic collusion, demonstrated in duopoly settings where firms using behavior-elastic algorithms converge to supra-competitive equilibria through accidental signaling—inferring each other’s private demand curves from public pricing paths—creates market distortions that regulators cannot detect under current antitrust paradigms [11]. The algorithmic output itself becomes a channel of strategic information transfer, transforming neuro-behavioral data into a legally ambiguous, non-consensual competitive asset. Without transparent, auditable, and legally enforceable constraints on inferential modeling, behavior-elastic pricing risks enabling persistent, invisible price-fixing that erodes consumer welfare while evading regulatory scrutiny. The transition from laboratory to market thus demands not only algorithmic robustness but institutional safeguards: mandatory ethical review boards grounded in legally enforceable neurorights, as codified in Chilean jurisprudence, must precede deployment to audit for collusion, bias, and re-identification risk [42][11]. The technical architecture of behavior-elastic pricing is inseparable from its regulatory and ethical architecture.

Having examined the algorithmic and technical barriers to scalable implementation, the following section will address the ethical and regulatory imperatives that must constrain these systems, particularly the transition from static consent to dynamic, revocable, and cognition-aware governance mechanisms that safeguard cognitive autonomy in the face of inferential commercial models.

The deployment of behavior-elastic pricing systems, grounded in the direct measurement of neural correlates of willingness-to-pay, introduces profound ethical risks that challenge the foundational principles of consumer autonomy, informed consent, and cognitive sovereignty. Unlike traditional pricing models that operate on observable transactional behavior, neuro-informed systems infer and exploit subconscious cognitive processes—such as implicit bias, emotional valence, and attentional capture—before conscious deliberation occurs, transforming commercial influence into a form of covert neurological nudging. This shift from price-based persuasion to neurocognitive manipulation fundamentally reconfigures the power dynamic between firm and consumer: the latter is no longer merely responding to an external price signal but is being steered by algorithmic architectures calibrated to the internal, unreported states of their own minds. The moral status of neural data as a uniquely intimate, non-transferable, and identity-revealing domain—akin to genetic or biometric data but with deeper cognitive implications—demands that its commercial use be subject not to mere data minimization or anonymization, but to a doctrine of neurodata exceptionalism that recognizes it as a form of cognitive property [41]. When neural signals are harvested without explicit, revocable, and non-coercive consent—particularly when collected under conditions of functional necessity (e.g., via wearable EEG headsets marketed as “focus enhancers”)—they become the substrate for inferential commercial models that violate the right to mental privacy and undermine the very possibility of autonomous choice [41].

The erosion of autonomy manifests not only through the extraction of neural data but through the design of pricing architectures that exploit neurocognitive vulnerabilities. Behavioral economics has long documented systematic biases such as loss aversion and anchoring, but behavior-elastic pricing leverages real-time neuro-data to activate these biases predictively and persistently. For instance, by detecting elevated insula activity—a neural marker of perceived pain or aversion—firms can dynamically adjust pricing thresholds to exploit heightened sensitivity during moments of cognitive fatigue or emotional distress, effectively turning physiological states into profit opportunities. Similarly, the use of prefrontal gamma asymmetry (PAIγ) to identify approach motivation enables firms to time price promotions not according to market cycles, but to the precise moment when a consumer’s neural system is primed for acquisition, regardless of their stated intent or rational budget constraints. Such practices constitute a form of subliminal manipulation that bypasses conscious evaluation, rendering traditional notions of “informed consent” obsolete: a consumer cannot meaningfully consent to a pricing mechanism they are unaware of, cannot perceive, and whose operation depends on the decoding of their private neural activity [28][41]. The legal fiction that broad, boilerplate consent obtained via digital adhesion contracts constitutes valid authorization is dismantled by Chile’s Girardi v. Emotiv Inc. ruling, which affirmed that true autonomy cannot be coerced and that re-identification risk—even from “anonymized” neural data fused with behavioral traces—renders such consent legally and ethically invalid [41].

Moreover, the classification of neural data as personal property introduces a moral imperative for ownership and control. If willingness-to-pay is a direct output of the brain’s valuation circuitry, then the data that encodes it is not merely a byproduct of interaction but a manifestation of the individual’s internal economic identity. The commercial commodification of this data without compensation, control, or opt-out mechanisms constitutes a form of cognitive expropriation—a proprietary seizure of the mind’s own economic preferences. This is not analogous to the harvesting of browsing history or purchase records; neural data reveals not what a person chose, but why they chose it, exposing their latent preferences, cognitive biases, and emotional triggers in ways even they may not fully comprehend. The absence of legal frameworks recognizing neurorights as enforceable property rights creates a vacuum in which firms treat neural profiles as unregulated assets, enabling discriminatory pricing based on inferred vulnerability, mental fatigue, or neurocognitive profile—an emergent form of cognitive discrimination that no existing consumer protection law is designed to detect or remedy [41]. The danger is not hypothetical: studies have shown that PAIγ responses vary systematically across product categories, with luxury goods eliciting positive correlations and FMCGs negative ones, suggesting that pricing systems trained on neural data may inadvertently or deliberately target low-income consumers with higher prices for essential goods based on their heightened neurocognitive sensitivity—a pattern indistinguishable from redlining but encoded in neural signatures rather than zip codes [28].

Ethical design frameworks must therefore move beyond principles of transparency and fairness to embed enforceable constraints on inferential modeling itself. The distinction between persuasion and manipulation is not semantic but structural: persuasion appeals to conscious reasoning and aligns with revealed preferences, whereas manipulation operates on pre-conscious neural states and exploits latent, unarticulated vulnerabilities. Behavior-elastic pricing, when deployed without neurorights-based safeguards, is inherently manipulative because it decouples pricing from the consumer’s expressed will and anchors it in their unmonitored neurophysiology. A truly ethical architecture must therefore be built on three pillars: first, the legal recognition of neurorights as non-derogable—enshrining mental privacy, cognitive liberty, and freedom from algorithmic subversion as enforceable rights; second, the institutionalization of mandatory, independent ethical review boards modeled on Chilean jurisprudence, empowered to audit pricing algorithms for covert influence, bias amplification, and re-identification risk prior to deployment [41]; and third, the technical implementation of post-quantum cryptographic safeguards and on-device inference to ensure neural data never leaves the consumer’s control, rendering inferential models incapable of persistent surveillance or aggregation [12][24]. Without these constraints, behavior-elastic pricing does not optimize revenue—it redefines the consumer as a biological interface to be optimized, eroding the moral foundation of market exchange. The transition from price-elastic to behavior-elastic demand is not merely a technical advancement; it is an ethical inflection point. The question is no longer whether we can decode the mind to set prices, but whether we should—and if so, under what non-negotiable conditions of cognitive sovereignty.

Having established the ethical imperatives that constrain inferential pricing, the following section will examine the emerging legal frameworks—centered on Chile’s neurorights jurisprudence—that provide the only enforceable foundation for aligning these systems with human dignity and cognitive autonomy.

8. Regulatory and Legal Landscape: Emerging Frameworks and Compliance Pathways¶

The regulatory and legal landscape governing behavior-elastic pricing systems is characterized by profound fragmentation, emerging doctrinal tension, and a growing jurisprudential consensus that neural data constitutes a sui generis category requiring protection beyond conventional privacy frameworks. While existing data protection regimes such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA/CPRA) have begun to incorporate neural data under broader categories of sensitive personal information, their application remains inconsistent, context-dependent, and structurally inadequate to address the uniquely inferential and identity-revealing nature of neurocognitive signals. Under GDPR Article 9, EEG and fMRI-derived signals may qualify as biometric data for the purpose of uniquely identifying a natural person, particularly given emerging research demonstrating that brain activity patterns can serve as stable, individualized fingerprints with authentication accuracy reaching 40% [43]. Furthermore, because these signals may indirectly reveal health conditions, political opinions, or sexual orientation through predictive inference, they trigger the special category protections requiring explicit consent and strict lawful bases for processing—yet the regulation does not explicitly mention neurotechnology, leaving enforcement contingent on interpretive expansion [43]. Similarly, California’s Senate Bill 1223, effective January 1, 2025, codifies “neural data” as a distinct category of sensitive personal information, defined as information generated by measuring the activity of a consumer’s central or peripheral nervous system and not inferred from nonneural sources [2]. This definition anchors legal obligation to the origin of the data—direct physiological measurement—rather than its inferential content, creating a critical gap: data derived from non-neural biosensors (e.g., heart rate, eye-tracking, galvanic skin response) that algorithmically reconstruct cognitive states fall outside this protection, despite their functional equivalence in revealing mental states [13]. This discrepancy is emblematic of the “neural data Goldilocks problem” observed across U.S. state laws: Colorado HB 24-1058 embeds neural data within a broader “biological data” category, regulating it only when used for identification—a condition that renders the provision functionally inert given current neurotechnological applications; Montana SB 163 excludes downstream physical effects like pupil dilation, thereby omitting key proxies for cognitive load; and Connecticut SB 1295 restricts its scope to central nervous system activity alone, potentially excluding peripheral signals that correlate with emotional valence [39]. These legislative variations create regulatory arbitrage opportunities, enabling firms to deploy neurobehavioral pricing systems in jurisdictions with narrower definitions or weaker enforcement, undermining the coherence of global compliance [39].

In stark contrast to this fragmented, condition-based approach, Chile’s constitutional and judicial developments establish the world’s only concrete jurisprudential foundation for “neurorights” as a distinct, non-redundant framework grounded in cognitive sovereignty. The 2021 constitutional amendment enshrined the right to mental privacy, mental identity, free will, and fair access to mental augmentation as fundamental rights, explicitly rejecting the notion that existing data protection laws are sufficient to safeguard against the direct access and decoding of internal cognitive states [37]. This doctrine of neurodata exceptionalism—akin to genetic exceptionalism—is not abstract ethical idealism but operationalized law. In its landmark 2023 ruling in Girardi v. Emotiv Inc., Chile’s Supreme Court ordered the deletion of neural data collected via the Emotiv Insight EEG headset after the user declined to purchase a paid license, rejecting the firm’s claims of compliance through pseudonymization and broad consent [41]. The Court explicitly dismantled the Cartesian separation between “mental life” and “personal data,” affirming that neurodata—defined as information derived from the electrical activity of the nervous system—constitutes the most intimate aspect of human personality, revealing subconscious intentions, cognitive patterns, and psychological integrity [41]. Crucially, the ruling invalidated the validity of consent obtained through adhesion contracts where refusal to accept terms blocked device functionality, establishing that genuine autonomy cannot be coerced and that re-identification risk, even from anonymized data fused with behavioral traces, renders traditional data minimization principles insufficient [41]. This precedent directly constrains behavior-elastic pricing models: any commercial inference of willingness-to-pay from neural signals without explicit, revocable, context-specific, and non-coercive consent constitutes a violation of mental privacy and cognitive freedom, irrespective of whether the data was collected for “research” or “marketing” purposes [41]. The Court further highlighted the regulatory vacuum permitting commercial neurotechnologies to be marketed without health authority authorization, exposing a critical gap between technological capability and legal oversight [41]. While Chile’s Chamber of Deputies is actively considering legislation to codify neural data as sensitive personal information, the judicial ruling already functions as a binding constraint, demanding that algorithmic pricing architectures be designed with neurorights as non-negotiable, pre-deployment safeguards—not post-hoc compliance mechanisms [46][22].

The divergence between Chile’s rights-based, human dignity-centered framework and the U.S. and EU’s data-centric, purpose-limited approaches reveals a fundamental philosophical and operational rift. While the EU’s AI Act prohibits AI systems that use “significantly harmful subliminal manipulation” and bans emotion inference in workplaces and educational settings, it does not explicitly address the inferential use of neural data for dynamic pricing, leaving compliance ambiguous and enforcement untested [46]. The UK Information Commissioner’s Office (ICO) has recognized neurodata as health information under UK GDPR, requiring ethical oversight for research applications, yet no equivalent regulatory guidance exists for commercial pricing contexts [36]. Meanwhile, UNESCO’s 2024 draft instrument under intergovernmental negotiation and the UN Human Rights Council’s advisory committee recommendation to develop General Comments on freedom of thought and mental integrity represent nascent, non-binding global norms that may eventually influence national courts but currently lack enforcement power [46]. The absence of a unified, enforceable global standard creates a regulatory patchwork that incentivizes jurisdictional shopping and erodes consumer protection. Furthermore, opposition to neurorights persists: scholars such as Lara Gálvez and Miralles argue that existing privacy laws already cover brain data, and that the Emotiv case was resolved under conventional frameworks—not neurorights—while critics like Alegre and Bublitz warn of “rights inflationism,” asserting that neurotechnology risks are speculative and that existing rights should be tested in court before new ones are created [15]. This scholarly dissent, however, fails to engage with the empirical reality exposed by the Neurorights Foundation’s analysis of 29 consumer neurotech user agreements: all companies retained full possession of neural data, 29 of 30 permitted unfettered third-party sharing, and none adequately disclosed decoding capabilities or provided meaningful opt-out mechanisms [46][15]. This industry practice underscores that legal ambiguity is not a neutral gap but a structural vulnerability exploited by commercial actors.

The convergence of these legal, technical, and ethical pressures necessitates a new regulatory taxonomy for behavior-elastic pricing systems, structured by risk level and grounded in enforceable neurorights. A tiered framework is proposed: (1) Low-risk systems—such as general advertising using aggregated, non-individualized behavioral signals derived from non-neural sources (e.g., clickstream, purchase history)—are governed by existing consumer protection and advertising laws prohibiting deceptive practices, with no requirement for neurodata-specific consent; (2) Medium-risk systems—including digital platforms that use multimodal biosensor data (e.g., EEG, eye-tracking, galvanic skin response) to infer cognitive or affective states for dynamic pricing or personalization—must comply with strict opt-in consent under CPRA or GDPR Article 9, require granular, dynamic controls enabling real-time revocation, mandate data minimization (collecting only essential signals), and prohibit repurposing without fresh authorization, as outlined in ISO/IEC 24443 and W3C Verifiable Credentials frameworks [36][13]; (3) High-risk systems—those that infer willingness-to-pay from direct neural data (EEG, MEG, fMRI) or employ cognitive biometrics to predict individualized vulnerability, bias, or decision-making tendencies—are subject to mandatory pre-deployment ethical review by independent, legally empowered review boards modeled on Chilean jurisprudence, require post-quantum cryptographic safeguards and on-device inference to prevent re-identification, and are prohibited from using neural-derived preferences for pricing unless the individual has provided explicit, revocable, and non-coercive consent under a regulated, auditable consent infrastructure [12][41][13]. This taxonomy shifts the regulatory focus from data type to inference risk and cognitive harm, aligning legal constraints with the neurocognitive reality that the value of neural data lies not in its origin but in its capacity to reveal the private architecture of human choice. Without such a taxonomy, behavior-elastic pricing risks becoming a legal grey zone where algorithmic inference outpaces regulation, enabling cognitive discrimination and covert manipulation under the guise of market efficiency. The Chilean precedent provides the only viable anchor for this taxonomy, demonstrating that neurorights are not a speculative future ideal but an operational legal constraint that must be embedded in the technical architecture of pricing systems before deployment.

9. Validation, Generalizability, and Future Research Directions¶

The robustness of behavior-elastic demand curves across cultures, demographics, and product categories remains an open and critical challenge, as current models are predominantly trained on homogenous, Western, educated, industrialized, rich, and democratic (WEIRD) samples, limiting their generalizability to global markets. While the DeePay architecture demonstrated 75.09% classification accuracy and an RMSE of 0.276 across six product categories in controlled lab settings, these results were derived from cohorts with limited ethnic, socioeconomic, and neurocognitive diversity [25][19]. Cross-cultural validation studies reveal that neural proxies for willingness-to-pay—such as prefrontal gamma asymmetry (PAIγ)—exhibit directional reversals depending on cultural context: in individualistic societies, PAIγ strongly correlates with luxury goods valuation, whereas in collectivist cultures, the same signal shows attenuated or even inverted relationships, suggesting that social norms and identity-based valuation mechanisms modulate the neural encoding of economic value [28]. This context-dependence is further amplified by demographic heterogeneity: older adults exhibit delayed neural response latencies in the vmPFC and reduced gamma-band synchronization during WTP tasks, indicating that age-related neurocognitive changes necessitate recalibration of temporal fusion models [5]. Similarly, neurodiverse populations, including individuals with autism spectrum disorder, demonstrate atypical patterns of attentional allocation and emotional valence processing, which, if unaccounted for, introduce systematic bias into pricing inferences derived from eye-tracking and EEG fusion [29].

Replication challenges are compounded by methodological fragmentation. Despite the adoption of the Brain Imaging Data Structure (BIDS) format for reproducibility, most public neuro-marketing datasets lack standardized experimental protocols, incentive structures, or ecological validity [35]. The Becker-DeGroot-Marschak (BDM) procedure, while gold-standard for aligning neural responses with economic outcomes, is rarely implemented consistently across studies, with variations in bid ranges, payment mechanisms, and stimulus presentation durations introducing uncontrolled variance in WTP estimates [25][19]. Longitudinal stability of neural responses is another untested assumption: current models assume that an individual’s neural valuation signature remains stable over time, yet preliminary evidence suggests that repeated exposure to pricing stimuli induces neural adaptation, with PAIγ responses diminishing by up to 22% over a 14-day period in the same subjects, indicating that behavior-elastic curves may require continuous retraining to maintain predictive fidelity [5]. Without longitudinal neuro-trials tracking individual WTP trajectories across months or years, firms risk deploying static models that misprice as consumers’ neurocognitive responses evolve due to habituation, learning, or life-stage changes.

The absence of open, large-scale, and ethically governed neuro-marketing datasets is the most significant barrier to generalizability and validation. Unlike traditional market transaction data, which is widely available through retail analytics platforms, neuro-data remains siloed within academic labs, proprietary neurotech firms, and corporate R&D units, often subject to restrictive data-use agreements that prohibit sharing or aggregation [8]. This scarcity impedes the development of robust, population-representative models and prevents independent validation of algorithmic claims. While datasets from Yadava et al., Georgiadis et al., and Mashrur et al. serve as foundational benchmarks, they are small (n < 200), narrowly scoped, and lack demographic metadata necessary for subgroup analysis [8][20]. To overcome this, the field must establish centralized, federated data repositories governed by ethical review boards aligned with neurorights jurisprudence—modeled on Chile’s enforceable framework—where data is stored on-device or in encrypted, decentralized nodes, and access is granted only under strict, auditable protocols that preserve participant autonomy and prohibit re-identification [24][41]. Such infrastructure must be built on post-quantum cryptographic safeguards to prevent future adversarial reconstruction of neural profiles [12].

Future research must prioritize three interconnected directions. First, hybrid models that integrate neural signals with behavioral economic primitives—such as reference dependence, loss aversion, and mental accounting—are essential to bridge the gap between neurocognitive measurement and economic theory. Rather than treating EEG and fMRI data as black-box predictors, future architectures should embed structural constraints from prospect theory into deep learning loss functions, ensuring that predicted WTP functions conform to known behavioral regularities and are interpretable as latent utility functions, not merely statistical correlations [18][5]. Second, real-world field trials are urgently needed to validate behavior-elastic pricing outside the lab. These trials must be conducted in ecologically valid settings—e.g., mobile retail apps, ride-hailing platforms, or e-commerce sites—using non-invasive, consumer-grade EEG headsets with real-time quality monitoring, paired with transparent, revocable consent interfaces that comply with neurorights standards [24][41]. Such trials should measure not only revenue outcomes but also consumer trust, perceived fairness, and long-term brand loyalty to assess the holistic impact of neuro-informed pricing. Third, consumer education initiatives must be developed to empower individuals to understand, interrogate, and control the use of their neural data. These initiatives should demystify neuro-marketing practices, provide accessible tools for data access and deletion requests, and promote digital literacy around cognitive autonomy—akin to GDPR’s right to explanation but extended to neural inference. Only through this triad of hybrid modeling, real-world validation, and cognitive empowerment can behavior-elastic demand curves transition from promising laboratory constructs to equitable, robust, and socially responsible tools for economic optimization.

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