neuroaihub Visual Search Array Generator
Purpose
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.
When to Use
Use this skill when:
- Designing a visual search experiment (feature search, conjunction search, spatial configuration search)
- Generating stimulus arrays with specific set sizes, spacings, and feature dimensions
- Selecting target-distractor similarity levels to manipulate search efficiency
- Choosing set sizes and trial structure for measuring search slopes
- Configuring display timing, inter-trial intervals, and response windows
Do not use this skill when:
- The task is not visual search (e.g., change detection, visual working memory, attentional capture without search)
- You are analyzing existing visual search data rather than designing new experiments
- The display involves naturalistic scenes rather than controlled arrays (use scene perception methods)
Research Planning Protocol
Before executing the domain-specific steps below, you MUST:
- State the research question -- What specific question is this analysis/paradigm addressing?
- Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
- Declare expected outcomes -- What results would support vs. refute the hypothesis?
- Note assumptions and limitations -- What does this method assume? Where could it mislead?
- Present the plan to the user and WAIT for confirmation before proceeding.
For detailed methodology guidance, see the research-literacy skill.
⚠️ Verification Notice
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.
Search Type Classification
Feature Search (Parallel / Pop-out)
Target defined by a single unique feature (Treisman & Gelade, 1980).
- Search slope: < 10 ms/item for target-present trials (Wolfe, 2021)
- RT x set size function: Flat or near-flat
- Example: Red target among green distractors; vertical target among horizontal distractors
- Theoretical basis: Pre-attentive feature maps can detect unique singletons without serial scanning (Treisman & Gelade, 1980)
Conjunction Search (Inefficient / Serial)
Target defined by a combination of features shared individually with distractors (Treisman & Gelade, 1980).
- Search slope: 20-30 ms/item for target-present trials (Wolfe, 2021)
- Absent:present slope ratio: Approximately 2:1 if search is self-terminating (Treisman & Gelade, 1980)
- Example: Red vertical target among red horizontal and green vertical distractors
- Note: Many conjunction searches are more efficient than predicted by strict serial models; guided search theory accounts for this (Wolfe, 1994)
Spatial Configuration Search
Target differs from distractors in spatial arrangement of parts rather than simple features.
- Search slope: 30-50+ ms/item (Wolfe, 2021)
- Example: T among Ls; 2 among 5s
- These are among the most inefficient search tasks and should be used when studying attentional limits
Search Slope Classification Benchmarks
| 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 |
Display Parameters
Spatial Layout
| 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) |
Preventing Crowding
Crowding impairs identification when flanking items are too close to the target, especially in the periphery (Pelli & Tillman, 2008).
- Critical spacing: Approximately 0.5 x eccentricity (Bouma, 1970)
- At 5 degrees eccentricity, items must be > 2.5 degrees apart to avoid crowding
- At 10 degrees eccentricity, items must be > 5 degrees apart
- For items near fixation (< 2 degrees), minimum spacing of 1 degree is sufficient
Set Sizes
| 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).
Trial Structure
| 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).
Timing Parameters
| 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 |
Feature Dimensions and Similarity
Color
| 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 |
Orientation
| 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 |
Size
| 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 |
Target-Distractor Similarity and Distractor Heterogeneity
Duncan & Humphreys (1989) Framework
Search efficiency depends on two factors:
- Target-distractor (T-D) similarity: Higher similarity = less efficient search
- Distractor-distractor (D-D) similarity: Lower D-D similarity (heterogeneous distractors) = less efficient search
| 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 |
Practical Implementation
- Homogeneous distractors: All distractors identical; cleanest test of T-D similarity
- Heterogeneous distractors: Distractors vary in the search-relevant feature; tests the D-D similarity effect
- Controlling heterogeneity: Sample distractor features from a uniform distribution within a defined range (e.g., orientation distractors drawn from 0 +/- 10 degrees; Duncan & Humphreys, 1989)
Array Generation Algorithm
Placement Algorithm (Recommended)
- Define the display region (circular with radius = max eccentricity)
- Generate candidate positions using one of:
- Grid + jitter: Place items on a regular grid, then add random jitter (uniform, +/- 0.3 deg) to break regularity (Wolfe et al., 1998)
- Random placement with rejection: Sample random positions; reject any that violate minimum spacing
- Concentric rings: Place items on concentric rings at fixed eccentricities (controls eccentricity distribution)
- Enforce minimum inter-item spacing (> 1 degree center-to-center)
- Enforce minimum distance from fixation (> 1 degree; avoids masking by fixation cross)
- Balance target position across eccentricity bins and quadrants over the experiment
- For each trial, randomly assign target to one position (present trials) or assign no target (absent trials)
Randomization Constraints
- Target position: Counterbalance across quadrants and eccentricity bins within each set size
- Set size order: Randomize or pseudorandomize within blocks
- Target presence: Pseudorandomize to avoid long runs of present or absent trials (max run length: 4 consecutive same-type trials; standard practice)
- Feature assignment: For conjunction search, ensure equal numbers of each distractor type (e.g., 50% share color with target, 50% share orientation; Treisman & Gelade, 1980)
- Block structure: If multiple set sizes are used, either mix within blocks or block by set size (within-block mixing is standard; Wolfe, 2021)
Common Pitfalls
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.
Minimum Reporting Checklist
Based on current best practices in visual search research:
- Search type (feature, conjunction, spatial configuration) and theoretical motivation
- Set sizes used and number of trials per set size per target-presence condition
- Target-present to target-absent ratio
- Display parameters: eccentricity range, item size (in degrees of visual angle), minimum spacing
- Item features: colors (in device-independent space), orientations (in degrees), sizes (in degrees)
- Target-distractor similarity metric and value
- Distractor composition (homogeneous vs. heterogeneous; how features were assigned)
- Viewing distance and display specifications (size, resolution, refresh rate)
- Timing: fixation duration, display duration, response deadline, ITI
- Randomization scheme: how set size, target presence, and target position were randomized
- Search slope values (ms/item) with confidence intervals for target-present and target-absent
- Slope ratio (absent:present) to assess self-termination
- Error rates by condition (especially miss rates)
- RT trimming criteria and percentage of data excluded
- Software used for stimulus generation and presentation (with version)
References
- Appelle, S. (1972). Perception and discrimination as a function of stimulus orientation: The "oblique effect" in man and animals. Psychological Bulletin, 78, 266-278.
- Bouma, H. (1970). Interaction effects in parafoveal letter recognition. Nature, 226, 177-178.
- Chun, M. M., & Wolfe, J. M. (1996). Just say no: How are visual searches terminated when there is no target present? Cognitive Psychology, 30, 39-78.
- Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96, 433-458.
- Foster, D. H., & Ward, P. A. (1991). Asymmetries in oriented-line detection indicate two orthogonal filters in early vision. Proceedings of the Royal Society B, 243, 75-81.
- Nachmias, J. (2011). Shape and size discrimination compared. Vision Research, 51, 400-407.
- Nagy, A. L., & Sanchez, R. R. (1990). Critical color differences determined with a visual search task. Journal of the Optical Society of America A, 7, 1209-1217.
- Pelli, D. G., & Tillman, K. A. (2008). The uncrowded window of object recognition. Nature Neuroscience, 11, 1129-1135.
- Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97-136.
- Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1, 202-238.
- Wolfe, J. M. (2021). Guided Search 6.0: An updated model of visual search. Psychonomic Bulletin & Review, 28, 1060-1092.
- Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided search: An alternative to the feature integration model for visual search. Journal of Experimental Psychology: Human Perception and Performance, 15, 419-433.
- Wolfe, J. M., Friedman-Hill, S. R., Stewart, M. I., & O'Connell, K. M. (1992). The role of categorization in visual search for orientation. Journal of Experimental Psychology: Human Perception and Performance, 18, 34-49.
- Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435, 439-440.
- Wolfe, J. M., O'Neill, P., & Bennett, S. C. (1998). Why are there eccentricity effects in visual search? Perception & Psychophysics, 60, 140-156.
See references/array-generation-parameters.yaml for a machine-readable parameter specification.