visual-search-array-generator

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

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:

1. State the research question -- What specific question is this analysis/paradigm addressing?
2. Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
3. Declare expected outcomes -- What results would support vs. refute the hypothesis?
4. Note assumptions and limitations -- What does this method assume? Where could it mislead?
5. 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:

1. Target-distractor (T-D) similarity: Higher similarity = less efficient search
2. 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)

1. Define the display region (circular with radius = max eccentricity)
2. 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)

3. Enforce minimum inter-item spacing (> 1 degree center-to-center)
4. Enforce minimum distance from fixation (> 1 degree; avoids masking by fixation cross)
5. Balance target position across eccentricity bins and quadrants over the experiment
6. 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

1. 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).

2. 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).

3. 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.

4. 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).

5. 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).

6. 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).

7. 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).

8. 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.