The dark side of product attachment: Reactivity of non-users and users to addictive product advertising
Bibliographic record
Abstract
Advertising is a ubiquitous and pervasive environmental cue. The average consumer, for example, is exposed on average to three thousand ads per day (Schwartz 2004). Under normal circumstances, consumers choose which advertising messages to attend to both consciously and non-consciously (Bargh 2002; Grunert 1996). However for consumers, environmental stimuli may elicit a unique type of response affecting decision making and driving behavior (Bernheim and Rangel 2004). The aim of this dissertation is to explore how environmental stimuli affect addictive product users and non-users and includes two studies, one using a brain imaging technique developed in neuroscience called functional magnetic resonance imaging (fMRI) and the other using laboratory experiments with ad-exposed non-users and users. Brain imaging data for study 1 showed that non-users who viewed addictive product ads had higher activation in brain regions associated with craving and cognitive resource depletion. Users, on the other hand, had a more complicated reaction. While they exhibited higher activation in some of the regions associated with craving and cognitive resource depletion, they also displayed a significant deactivation pattern in specific brain regions associated with craving and cognitive resource depletion, distinct from those regions that showed an activation pattern. These results indicated that while non-users may be unwittingly vulnerable to the effects of addictive product ads, users employed strategies that mitigated their craving and depletion responses.Study 2 looked at how craving and cognitive resource depletion in non-users and users were affected by exposure to addictive (vs. non-addictive) product ads. It also examined whether ad-induced craving in non-users and users elicited cognitive resource depletion for products not directly related to the addictive product ads. The study recruited over 1600 young adult non-users and users to view ads embedded in a mock magazine format and to complete a questionnaire. Non-users reported higher craving levels and demonstrated higher cognitive resource depletion effects when exposed to addictive (vs. non-addictive) product ads. Conversely users exposed to addictive product ads (vs. non-addictive) reported lower craving and no significant effects for cognitive resource depletion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".