Visual Search Array Generator

Specify validated display, set size, and randomization parameters for visual search arrays.

34|5|Updated Feb 28, 2026
One-click install
npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill visual-search-array-generator-neuroaihub
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Visual Search Array Generator
Source: https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills/tree/main/skills/visual-search-array-generator
Command: npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill visual-search-array-generator-neuroaihub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Expert visual search experiments require precise display, timing, and randomization constraints to avoid crowding, prevalence biases, and unreliable search slope estimates; this skill lays out validated defaults so researchers can design arrays that respect literature-backed limits without guesswork.

Core Features & Use Cases

  • Display and timing guardrails: Specifies eccentricity, spacing, set size selection, timing windows, and target prevalence tips to keep arrays psychophysically valid.
  • Feature similarity control: Guides color, orientation, and size thresholds while balancing target-distractor heterogeneity and referencing Duncan & Humphreys, Nagy & Sanchez, and Wolfe benchmarks.
  • Trial structuring and randomization: Recommends practice trials, trial counts, counterbalancing, and placement algorithms with crowding-safe spacing so you can implement predictable search slope assays.
  • Use Case: Configure a conjunction search experiment with set sizes {4,8,12,16,20}, ensure target-absent runs match target-present runs, and verify response deadlines and ITIs align with the published guidelines.

Quick Start

Request a validated visual search array configuration by specifying set sizes, spacing, target-distractor similarity, and randomization constraints.

Frequently Asked Questions about Visual Search Array Generator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a visual search array with valid set sizes and spacing?

Valid visual search array design requires specifying eccentricity, crowding-safe spacing, and set size selections to maintain psychophysical validity. This skill provides validated display defaults and randomization constraints referencing foundational literature benchmarks.

What target-distractor similarity thresholds should I use for conjunction search experiments?

Conjunction search experiments require controlling color, orientation, and size thresholds to balance target-distractor heterogeneity. This skill guides feature similarity limits using benchmarks from Duncan & Humphreys, Nagy & Sanchez, and Wolfe.

How do I prevent prevalence bias and crowding in psychophysics trial randomization?

Preventing prevalence bias and crowding requires prevalence-balanced trial structures and placement algorithms with crowding-aware spacing. This skill recommends practice trials, counterbalancing, and timing windows aligned with published guidelines.

Can I configure spatial configuration search efficiency assays using validated display guardrails?

Configuring spatial configuration search efficiency assays requires precise display and timing guardrails to avoid unreliable search slope estimates. This skill specifies validated eccentricity, spacing, and response deadlines for controlled trial structures.

Why are my visual search experiment results unreliable despite controlled stimulus design?

Unreliable visual search results often stem from violating display timing windows or unbalanced target-present and target-absent trial counts. This skill provides literature-backed constraints to match prevalence and verify response deadlines.

Visual search array generation for cognitive psychology research: what parameters are needed?

Visual search array generation for cognitive psychology requires specifying set sizes, spacing, target-distractor similarity, and randomization constraints. This skill outputs validated display parameters satisfying crowding-aware spacing and prevalence-balanced trial requirements.