ADDICTION, NEUROECONOMICS, AND THE COLONIZATION OF VALUE IN DIGITAL ECONOMIES

THE MENTAL FORECAST

Recent neuroeconomic findings on addiction become more intelligible — and more troubling — when they are situated within the broader ecology of digital consumption and what we conceptualize as unconscious economic codes. Neuroeconomics shows that decision-making is not neutral but continuously shaped by valuation processes: the brain assigns subjective value to stimuli, updates this value through reinforcement learning, and selects actions accordingly. What recent studies demonstrate is that craving itself modulates these valuation and learning parameters (Kulkarni et al., 2026), meaning that the “market of choices” is internally distorted before any conscious deliberation occurs. When transposed into digital environments — where platforms systematically amplify salient cues, intermittent rewards, and emotionally charged content — this distortion is no longer episodic but chronic. The user is not simply choosing; their valuation system is being continuously recalibrated by an external architecture designed to capture attention.

This is where the notion of unconscious economic codes becomes operational rather than metaphorical. Digital platforms embed specific value hierarchies — visibility, immediacy, performance, social validation — that function as implicit norms guiding behavior. From a neuroeconomic perspective, these codes act as environmental priors: they shape the expected value of actions before learning even begins. For instance, the framing effect described in recent work (Sellitto & Griffiths, 2026) shows that the way options are presented can shift risk preferences and reward evaluation. Social media interfaces industrialize this mechanism: metrics such as likes, views, and followers are not neutral feedback but calibrated signals that redefine what counts as “reward.” Over time, this produces a systematic bias toward short-term, high-salience outcomes, reinforcing delay discounting and impulsive decision-making — core mechanisms in addiction.

A critical inversion emerges when we consider that addiction is not only located in substances or behaviors but in the structure of the environment itself. Studies on cue-induced conflict and impaired cognitive control (Xu et al., 2026) demonstrate that exposure to salient cues can hijack response selection processes before reflective control is engaged. In digital ecosystems, cues are omnipresent, personalized, and algorithmically optimized. This creates a condition of permanent cue-reactivity, where attentional capture becomes the default state. The individual is thus placed in a continuous loop of stimulus–response activation, where the capacity for inhibition is systematically eroded. In this sense, what appears clinically as “addiction” may partly reflect an adaptation to an environment that continuously exploits neurocognitive vulnerabilities.

The epidemiological implication is that addiction mechanisms are being generalized across the population, not confined to clinical subgroups. Neuroeconomic disruptions — altered learning rates, biased valuation, impaired feedback processing (Dampuré et al., 2026) — are no longer limited to substance use disorders but are increasingly observable in everyday digital behavior. This aligns with a social epidemiology perspective: exposure to high-intensity digital environments functions as a distributed risk factor, similar to chronic stress or environmental toxins. The concept of technostress becomes relevant here, not only as a subjective experience but as a biological and computational load that interferes with decision-making systems. The result is a diffuse form of behavioral addiction, where individuals oscillate between engagement and exhaustion, without clear thresholds of pathology.

THE MENTAL FORECAST, in this context, points toward a progressive convergence between economic systems, digital infrastructures, and neurocognitive functioning. The key trajectory is the externalization of decision-making architectures: platforms increasingly anticipate, shape, and even pre-empt choices through predictive algorithms. This creates a feedback loop in which unconscious economic codes are not only internalized but dynamically updated in real time. The risk is not merely increased addiction rates, but a transformation of subjectivity itself — where value, desire, and attention are continuously aligned with external economic logics. In such a configuration, the distinction between individual pathology and systemic influence becomes increasingly untenable, requiring a shift from clinical models of addiction toward integrated frameworks that include neuroeconomics, computational environments, and political economy as co-determinants of mental health.

Liviu Poenaru, PhD

March 2026

References

Dampuré, J. D. J., Agudelo-Orjuela, P. A., Van der Meij, M., Barber, H. A., & Belin, D. B. (2026). Behavioural and electrophysiological determinants of decision-making in individuals with a smoking habit. Frontiers in Psychology, 17, 1743972. https://doi.org/10.3389/fpsyg.2026.1743972

Kulkarni, K. R., Berner, L. A., Rhoads, S. A., Fiore, V. G., Schiller, D., & Gu, X. (2026). A computational mechanism linking momentary craving and decision-making in alcohol drinkers and cannabis users. Nature Mental Health. https://doi.org/10.1038/s44220-026-00593-w

Sellitto, M., & Griffiths, K. R. (2026). Exploring the framing effect on maladaptive behaviors: Neural mechanisms and applications. Frontiers in Behavioral Neuroscience, 20, 1816738. https://doi.org/10.3389/fnbeh.2026.1816738

Xu, Y., Lu, Y., & Wang, L. (2026). Drug cue enhances response conflict and reduces conflict adaptation in methamphetamine addiction. Psychological Research, 90, Article 57. https://doi.org/10.1007/s00426-026-02277-7