Resource scarcity impairs visual online detection and prospective memory
Bibliographic record
Abstract
Operating under limited resources (e.g., money, time) poses significant demands on the cognitive system. Scarcity induces attentional trade-offs of information in the environment, which can impact memory encoding. In three experiments (N=227) we demonstrate that people under time scarcity failed to detect time-saving cues as they occur in the environment, suggesting that scarcity impairs the ability to detect online cues. These time-saving cues, if noticed, would have saved more time for the time poor participants, alleviating the condition of scarcity. A follow-up experiment showed that the visuospatial proximity of the time-saving cues to the focal task determined successful detection of the time-saving cues, suggesting that the online detection errors can be explained by spatial attention on the task at hand. Thus, time scarcity may cause attentional trade-offs whereby attention is focused on the task at hand, while ironically, other beneficial information is neglected as it occurs in the environment. We also demonstrate that people under time scarcity were more likely to forget previous instructions to execute future actions, suggesting that scarcity causes prospective memory errors. Ironically, the time poor participants failed to remember previous instructions which, if followed, would have saved them time. These experiments show that scarcity impairs the online detection of beneficial information in the environment, as well as the execution of prospective memory cues. Failures of prospective memory and online detection are particularly problematic because they cause forgetting and neglect of beneficial information, perpetuating the condition of scarcity. The current studies provide a new cognitive account for the counterproductive behaviors in individuals under resource scarcity, and have implications for interventions to reduce neglect and forgetting in the poor. Meeting abstract presented at VSS 2017
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".