Engineered highs: Reward variability and frequency as potential prerequisites of behavioural addiction
Bibliographic record
Abstract
Influential learning-based accounts of substance addictions posit the attribution of incentive salience to drug-associated cues, and its escalation by the direct dopaminergic effects of drugs. In translating this account to disordered gambling, we have noted how the intermittent nature of monetary rewards in gambling (i.e. the variable ratio) may allow for analogous learning processes, via effects on dopaminergic signalling. The aim of the present article is to consider how multiple sources of reward variability operate within modern gambling products, and how similar sources of variability, as well as some novel sources of variability, also apply to other digital products implicated in behavioural addictions, including gaming, shopping, social media and online pornography. Online access to these activities facilitates not only unparalleled accessibility but also introduces novel forms of reward variability, as seen in the effects of infinite scrolls and personalized recommendations. We use the term uncertainty to refer to the subjective experience of reward variability. We further highlight two psychological factors that appear to moderate the effects of uncertainty: 1) the timecourse of uncertainty, especially with regard to its resolution, 2) the frequency of exposure, allowing temporal compression. Collectively, the evidence illustrates how qualitative and quantitative variability of reward can confer addictive potential to non-drug reinforcers by exploiting the psychological and neural processes that rely on predictability to guide reward seeking behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".