(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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.717 | 0.653 |
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".