The price of information: Increased inspection costs reduce the confirmation bias in visual search
Bibliographic record
Abstract
In visual search, there is a confirmation bias such that attention is biased towards stimuli that match a target template, which has been attributed to covert costs of updating the templates that guide search [Rajsic, Wilson, & Pratt, 2015. Confirmation bias in visual search. Journal of Experimental Psychology: Human Perception and Performance. Advance online publication. doi:10.1037/xhp0000090]. In order to provide direct evidence for this speculation, the present study increased the cost of inspections in search by using gaze- and mouse-contingent searches, which restrict the manner in which information in search displays can be accrued, and incur additional motor costs (in the case of mouse-contingent searches). In a fourth experiment, we rhythmically mask elements in the search display to induce temporal inspection costs. Our results indicated that confirmation bias is indeed attenuated when inspection costs are increased. We conclude that confirmation bias results from the low-cost strategy of matching information to a single, concrete visual template, and that more sophisticated guidance strategies will be used when sufficiently beneficial. This demonstrates that search guidance itself comes at a cost, and that the form of guidance adopted in a given search depends on a comparison between guidance costs and the expected benefits of their implementation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".