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Specifies display parameters, set sizes, target-distractor similarity, and randomization constraints for visual search experiments
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| name | visual-search-array-generator |
| description | Specifies display parameters, set sizes, target-distractor similarity, and randomization constraints for visual search experiments |
| domain | cognitive-psychology |
| version | 1.0.0 |
| authors | ["Claude (AI-assisted)"] |
| papers | ["Treisman & Gelade, 1980","Wolfe, 1994","Wolfe, 2021","Duncan & Humphreys, 1989","Chun & Wolfe, 1996"] |
| dependencies | {"required":["research-literacy"]} |
| review_status | ai-generated |
This skill encodes expert methodological knowledge for designing and generating visual search arrays. A competent programmer could easily generate random stimulus displays, but without domain training they would likely violate critical constraints: items too closely spaced (causing crowding), eccentricities beyond useful vision, inappropriate set sizes that cannot distinguish search types, target-distractor similarity levels that produce ceiling or floor effects, or trial ratios that distort search behavior. This skill provides the validated parameters needed to create psychophysically sound visual search experiments.
Use this skill when:
Do not use this skill when:
Before executing the domain-specific steps below, you MUST:
For detailed methodology guidance, see the research-literacy skill.
This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.
Target defined by a single unique feature (Treisman & Gelade, 1980).
Target defined by a combination of features shared individually with distractors (Treisman & Gelade, 1980).
Target differs from distractors in spatial arrangement of parts rather than simple features.
| Slope (ms/item) | Classification | Citation |
|---|---|---|
| < 5 | Highly efficient / pop-out | Wolfe, 2021 |
| 5-10 | Efficient (feature-like) | Wolfe, 2021 |
| 10-20 | Moderately efficient (guided) | Wolfe, 1994; Wolfe, 2021 |
| 20-30 | Inefficient (conjunction-like) | Treisman & Gelade, 1980; Wolfe, 2021 |
| > 30 | Very inefficient (serial) | Wolfe, 2021 |
| Parameter | Recommended Value | Citation / Rationale |
|---|---|---|
| Maximum eccentricity | 15 degrees of visual angle from fixation | Beyond ~15 deg, acuity drops substantially; standard upper bound (Wolfe et al., 1998) |
| Minimum inter-item spacing | > 1 degree center-to-center | Prevents crowding effects (Bouma, 1970: crowding zone ~ 0.5 x eccentricity) |
| Item size | 0.5-2 degrees of visual angle | Standard range for search items (Wolfe, 2021) |
| Display area | Circular or rectangular region within eccentricity limit | Avoid items near monitor edges where distortion may occur |
| Fixation cross | Present for 500-1000 ms before array onset | Standard in visual search (Wolfe et al., 1998) |
Crowding impairs identification when flanking items are too close to the target, especially in the periphery (Pelli & Tillman, 2008).
| Design Goal | Recommended Set Sizes | Rationale |
|---|---|---|
| Classify search type | 4, 8, 12, 16 (minimum 3 set sizes) | Need multiple points to estimate slope reliably (Wolfe, 2021) |
| Test for pop-out | 8, 16, 32 (wide range) | Pop-out confirmed if slope ~ 0 even at large set sizes (Treisman & Gelade, 1980) |
| Standard conjunction search | 4, 8, 12, 16, 20 | Finer-grained slope estimation (Wolfe, 1994) |
| Quick screening | 6, 12, 18 | Three evenly spaced set sizes for slope estimation |
Minimum set sizes: At least 3 different set sizes are required to reliably estimate a search slope. Two set sizes cannot distinguish linear from nonlinear search functions.
Maximum set size: Constrained by display density. With 1 degree minimum spacing and 15 degree eccentricity limit, the practical maximum is approximately 40-50 items for typical item sizes (Wolfe et al., 1998).
| Parameter | Recommended Value | Citation |
|---|---|---|
| Target-present : target-absent ratio | 1:1 (50% present) | Chun & Wolfe, 1996; standard in most search tasks |
| Low prevalence condition | 10% target-present | Wolfe et al., 2005 (miss rate increases dramatically) |
| Trials per cell | Minimum 20-30 trials per set size x presence combination | Wolfe, 2021; more for stable RT distributions |
| Practice trials | 10-20 trials before data collection | Standard practice |
| Total trial count | Typically 400-800 for a standard search task | Depends on number of conditions and set sizes |
Critical warning about target prevalence: When target prevalence drops below ~25%, miss rates increase dramatically -- the "prevalence effect" (Wolfe et al., 2005). This is a critical design consideration for applied search tasks (e.g., medical image screening).
| Parameter | Recommended Value | Rationale |
|---|---|---|
| Fixation duration | 500-1000 ms | Allow fixation stabilization |
| Display duration | Until response (standard) or fixed (brief search) | Self-paced search is default (Wolfe, 2021) |
| Brief display search | 100-200 ms (then mask) | Tests pre-attentive processing (Treisman & Gelade, 1980) |
| Response deadline | 3000-5000 ms | Exclude abnormally slow RTs |
| Inter-trial interval | 500-1000 ms | Prevent carryover effects |
| Feedback duration | 500 ms (if used) | Brief error/correct feedback |
| Parameter | Guideline | Citation |
|---|---|---|
| Feature search JND | Target-distractor color difference > 30 degrees in CIE Lab* or CIELUV hue angle for pop-out | Derived from Nagy & Sanchez, 1990 |
| Conjunction control | Equate target-distractor color distance across conditions | Essential for isolating conjunction cost |
| Number of colors | Typically 2-4 distinct colors for conjunction search | Wolfe, 1994 |
| Luminance | Equate luminance across colors to avoid luminance pop-out | Use isoluminant colors or verify with photometer |
| Color space | Specify in CIE Lab* or Munsell; avoid RGB for scientific reporting | RGB is device-dependent |
| Parameter | Guideline | Citation |
|---|---|---|
| Feature search JND | Target-distractor difference > 15-20 degrees for efficient search | Foster & Ward, 1991 |
| Pop-out threshold | Orientation difference > 30 degrees produces reliable pop-out | Wolfe et al., 1992 |
| Cardinal advantage | Vertical and horizontal orientations are detected faster than obliques | Appelle, 1972 |
| Recommended: Use oblique orientations (e.g., 45 deg, 135 deg) to avoid cardinal effects unless cardinals are of interest |
| Parameter | Guideline | Citation |
|---|---|---|
| Feature search JND | Target at least 1.5-2x distractor size for pop-out | Treisman & Gelade, 1980 |
| Weber fraction | Size discrimination Weber fraction ~ 0.04-0.06 (JND/standard) | Nachmias, 2011 |
| For search: Size ratio of > 1.5:1 (target:distractor) typically needed for efficient search | Wolfe, 2021 |
Search efficiency depends on two factors:
| T-D Similarity | D-D Similarity | Expected Search | Example |
|---|---|---|---|
| Low | High | Very efficient (pop-out) | Red among identical greens |
| Low | Low | Efficient | Red among varied colors (not red) |
| High | High | Inefficient | Pink among reds |
| High | Low | Very inefficient | Pink among varied warm colors |
Not controlling for eccentricity confounds: Larger set sizes place items at greater eccentricities on average, confounding set size with acuity. Solution: Use a fixed display area and add items by filling in gaps, not by expanding the area (Wolfe et al., 1998).
Interpreting null set-size effects as "pop-out" without verification: A flat slope does not guarantee parallel processing. Verify with brief presentations (100-200 ms + mask) and check that accuracy remains high (Treisman & Gelade, 1980).
Ignoring the prevalence effect: With low target prevalence (<25%), observers adopt a more liberal quitting threshold, increasing miss rates from ~5% to >25% (Wolfe et al., 2005). Design accordingly for applied contexts.
Using too few set sizes: Two set sizes define only a line; you cannot assess linearity or detect nonlinear search functions. Use at least 3 set sizes, preferably 4-5 (Wolfe, 2021).
Not equating luminance across color conditions: Luminance differences create an unintended pop-out cue. Always measure and equate luminance (use a photometer or validated software settings; Nagy & Sanchez, 1990).
Placing items too close together: Violating minimum spacing creates crowding, where items become unidentifiable not because of search difficulty but because of peripheral vision limits (Bouma, 1970; Pelli & Tillman, 2008).
Confounding distractor heterogeneity with target discriminability: Adding distractor variability reduces search efficiency independently of T-D similarity. Manipulate one while controlling the other (Duncan & Humphreys, 1989).
Failing to counterbalance target position: If the target systematically appears at certain locations, observers develop spatial biases. Counterbalance across quadrants and eccentricities.
Based on current best practices in visual search research:
See references/array-generation-parameters.yaml for a machine-readable parameter specification.
