(www.interscience.wiley.com) DOI: 10.1002/acp.1130 When Emotions Get the Better of Us: The Effect of Contextual Top-down Processing on Matching Fingerprints
Bibliographic record
Abstract
Twenty-seven participants made a total of 2,484 judgments whether a pair of fingerprints matched or not. A quarter of the trials acted as a control condition. The rest of the trials included top-down influences aimed at biasing the participants to find a match. These manipulations included emotional background stories of crimes and explicitly disturbing photographs from crime scenes, as well as subliminal messages. The data revealed that participants were affected by the top-down manipulations and as a result were more likely to make match judgments. However, the increased likelihood of making match judgments was limited to ambiguous fingerprints. The top-down manipulations were not able to contradict clear non-matching fingerprints. Hence, such contextual information actively biases the ways gaps are filled, but was not sufficient to override clear bottom-up information. Copyright # 2005 John Wiley & Sons, Ltd. The need to identify people accurately is widespread and is on a sharp rise. With advances in science and technology a variety of tools are now available for identifying people. Nevertheless, fingerprints continue to be the major method used for identification in forensic and other domains (Alam, Akhteruzzaman, & Cherri, 2004). Fingerprints are quite easy to find, collect, and process; and they are also relatively non-intrusive and cost
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".