Financial scarcity and caloric intake: It is not always about motivation
Bibliographic record
Abstract
Abstract Although prior research has established a cognitive association between perceived financial resources and increased caloric intake, the underlying process is still largely unknown. To date, the psychological influence of financial cues on eating behavior has primarily been explained in terms of goal activation. Perceived scarcity of financial resources is thought to result in a motivational drive to acquire food. In this research, we provide empirical support for this account while exploring how other types of cognitive associations involving semantic constructs (like traits) can also impact eating behavior. Traits are distinguishing qualities or characteristics that are associated with an individual or a group. They are commonly activated spontaneously during social interactions, leading to an associative behavior. Since semantic constructs are not motivational, they influence behavior in the absence of motivation. In this paper, we contribute to the existing literature in several ways. First, we conceptually replicate prior research, providing further support for the behavioral effects of a cognitive association between financial resources and caloric intake. Second, we provide empirical evidence that financial cues influence eating behavior both motivationally and non‐motivationally. Lastly, we find that trait constructs and goal constructs can be activated independently and have differential effects on eating behavior.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".