Bibliographic record
Abstract
In this study we tested whether or not confirmation bias, a well-known decision-making bias consisting of a tendency to selectively search for and evaluate information expected to confirm a focal hypothesis, can occur in visual search. Participants completed visual searches for a target letter, and were asked to make one response when the target letter appeared in a specified color and another response when the target letter appeared in a different color. The set size was held constant at eight, and the critical manipulation was the proportion of the stimuli that were in the specified color, referred to as the proportion of "hypothesis confirming" (HC) stimuli, compared to the unspecified color, referred to as the "hypothesis disconfirming" (HD) stimuli in a given search display. In Experiment 1, we found a confirmation bias, as participants searched through HC stimuli first, even when that required searching more items than searching through the smaller HD set. In Experiment 2, we attempted to attenuate the confirmation bias by incorporating a color preview display prior to the visual search display, so that participants could plan their search in advance of the stimuli appearing, therefore allowing them to implement an unbiased strategy. The results showed that the bias was attenuated, although participants were not able to detect the presence of a target letter in the HD set as efficiently as determining the absence of a target letter in that same set. Overall, these findings suggest that visual search is susceptible to confirmation bias and that this bias can be diminished by cognitive control mechanisms. Furthermore, this work shows that visual search can be used as a model for determining the role that attentional mechanisms may have in generating and maintaining confirmation biases. Meeting abstract presented at VSS 2014
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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 | 0.005 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".