Visual working memory for negative events is weakened by alternation between event types during judgments of trends.
Bibliographic record
Abstract
In order to effectively assess trends in the severity of a problem, we must often integrate working memory representations of several types of event. Alternating between different event types may cause them to seem less salient in memory, leading to an overly optimistic view of a given trend. We tested this possibility in a visual task portraying a series of events related to the success of orchard crops over time. Each scenario in our task portrayed one decade worth of crops. Each “year”, an icon was presented depicting either a healthy crop, a mild mold or insect infestation, a moderate infestation, or a severe infestation. In one condition, we structured the presentation order such that crop events of the same type occurred in sequence (e.g. mild mold, mild mold, moderate mold, healthy, mild insects, mild insects, moderate insects). Each “decade” in which crop events were grouped by type (the grouped condition) involved a different sequence – thus there was no statistical pattern to learn. In the ungrouped condition, the order of crop events within a decade was randomized (e.g. mild insects, mild mold, moderate insects, mild insects, healthy, moderate mold, mild mold). Participants were trained on four example scenarios depicting ‘normal’ frequencies for each type and severity of crop infestation within a decade. Next, following each decade in the main task, they rated the extent to which the crops had been better or worse than ‘normal’. Participants were generally biased towards judging a decade worth of crops as better than it actually was. They also made a more optimistic rating of the crops in the ungrouped condition than the grouped one. This demonstrates how switching attention between different types of symptoms or signs of a problem can cause us to be blind to the severity of that problem. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".