Visual Search Array Generator

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.