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LIT-1465: A taxonomy of technology design features that promote potentially addictive online behaviours

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StatusActive — read 2026-10-23 (NOTE-1096): in full, from the typeset article as deposited in the University of Lausanne's repository (SERVAL/IRIS). Worth reading as a compact, theory-driven catalogue of the design features of games, gambling, pornography, shopping, social-networking and streaming services that may weaken users' control. Features are sorted by whether they work on model-free learning (reinforcement schedules, personalized cues) or on model-based choice (overvaluation, features that interfere with deliberation, partial goal fulfilment, cognitive biases). It also names the selection process on the provider's side: features that raise use, time-on-device or spending are kept, with no psychology needed. The evidence reviewed is mostly cross-sectional, qualitative or indirect, and the authors warn against pathologizing common behaviour. Filed for the record's question whether potential of a proxy is valence (QUESTION-267), because it describes how a firm optimizing its own metric can install proxy-keyed potential in its users.
Tagspsychopathology-and-treatment, learning-and-conditioning, society-and-governance, motivation
Cited as “paper” byNOTE-1096 — Flayelle et al. — A taxonomy of technology design features that promote potentially addictive online behaviours

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Maèva Flayelle, Damien Brevers, Daniel L. King, Pierre Maurage, José C. Perales & Joël Billieux (2023), Nature Reviews Psychology 2(3):136–150 — DOI-10.1038/s44159-023-00153-4

Key takeaways

From the reading, NOTE-1096. Pages are the deposit’s PDF pages.

  • The question. How “technology design features might influence users’ capacity to exert control over how they engage with and use websites and applications, thereby amplifying uncontrolled, and perhaps addictive, use” (p. 1).
  • Two families. Model-free features work through cached values: random-ratio rewards (“where non-monetary rewards such as ‘likes’, ‘retweets’ or notifications are unpredictable”, p. 6) and personalized cues. Model-based features work through the user’s computed comparison: overvaluation, interference with deliberation, partial goal fulfilment and cognitive biases (Table 2, p. 8). The two “are not mutually exclusive, but can operate in parallel” (p. 6).
  • Unpredictability as the model-free engine. “the unpredictability of rewards, and the impossibility of reducing such unpredictability with experience, promote perseverative and cue-driven addiction-like behaviour” (p. 6). Incentive sensitization can be produced by drugs “as well as other reinforcers when their delivery is highly unpredictable” (p. 6).
  • Selection without psychology on the provider’s side. “design features that increase the frequency of online behaviour, time-on-device or spending (and therefore revenue) will be maintained, whereas those that do not will be abandoned” (p. 6).
  • Informational asymmetry. Providers know their users and users do not know how that knowledge is used. “If user motives constitute vulnerabilities, these automated processes could become exploitative” (p. 8).
  • Weak evidence, stated. Gaming research is “limited by its reliance on cross-sectional data” (p. 4). For cybersex, “empirical research is scarce and supporting evidence is mainly indirect” (p. 5). The authors call substance-use models applied to online behaviour “highly reductionistic” (p. 10).

Standing in the record

Filed on 2026-10-23 at the owner’s request, for QUESTION-267 and the organizational side of the record’s account of valence as ordering-potential. It describes a firm whose own metric (use, time-on-device, revenue) selects features that make a cue-keyed ranking potential in its users. That links the record’s reading of Doctorow (LIT-033, NOTE-041) to the corporate claim CLAIM-432. It is a review and a taxonomy, not a test, so no THEORY is drawn from it. A case documenting a particular firm’s design choices would be the place to ground that link; see NOTE-1096.

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