Agent skill

Cognitive Paradigm Design

by NeuroAIHub in NeuroAIHub/BrainPilot

Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions

AGPL-3.0Auto-check passedResearch & Science

Install Cognitive Paradigm Design

skills CLI
$ npx skills add NeuroAIHub/BrainPilot --skill cognitive-paradigm-design -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeuroAIHub/BrainPilot cognitive-paradigm-design --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/03_Cognitive_Psychology/cognitive-paradigm-design .claude/skills/cognitive-paradigm-design && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cognitive-paradigm-design
GitHub stars
1.1k
Token cost
~3.9k tokens
SKILL.md length
1,831 words
Files
3 (incl. references)
Skills in repo
59
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions

  • Works in 5 steps: Identify the Cognitive Construct → Select a Paradigm → Configure Parameters → …
  • Tasks that involve Hypothesis generation
  • SKILL.md covers Research Planning Protocol, ⚠️ Verification Notice, Core Workflow and Quick Reference: Paradigm…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cognitive Paradigm Design is an agent skill from NeuroAIHub/BrainPilot. Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/classic-paradigms.md` and `references/design-principles.md`).

It sits in Research & Science, covering Hypothesis generation. The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.

When your agent uses it

  • Tasks that involve Hypothesis generation

Example prompts

  • “/cognitive-paradigm-design”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Identify the Cognitive Construct
  2. Select a Paradigm
  3. Configure Parameters
  4. Design Controls
  5. Specify Dependent Variables and Analysis

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cognitive Paradigm Design loads about 3.9k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 37 tokens; SKILL.md has 1,831 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~17k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 1,831 words, ~3,937 tokens.

Download SKILL.mdSave it as .claude/skills/cognitive-paradigm-design/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
cognitive-paradigm-design
description
Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions
domain
cognitive-experimental-methods
version
1.0.0
papers
Goldstein, 2019, Cognitive Psychology (5th ed.), Morling, 2021, Research Methods in Psychology (4th ed.), Luck, 2014, An Introduction to the Event-Related…
dependencies.required
research-literacy
review_status
ai-generated

Cognitive Paradigm Design Skill

This skill helps researchers select appropriate experimental paradigms for cognitive psychology research questions, configure their parameters with cited defaults, and design proper controls. It encodes methodological knowledge from the cognitive experimental literature that a non-specialist would not know.

For detailed paradigm parameters, see references/classic-paradigms.md. For design methodology, see references/design-principles.md.


Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

  1. State the research question — What specific cognitive process or phenomenon is being investigated?
  2. Justify the method choice — Why an experimental paradigm (not survey, corpus, modeling)? What alternatives were considered?
  3. Declare expected outcomes — What pattern of results would support vs. refute the hypothesis?
  4. Note assumptions and limitations — What does this paradigm 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.

Core Workflow

When given a research question, follow this sequence:

Step 1: Identify the Cognitive Construct

Map the research question to one or more core cognitive domains:

DomainCore ConstructsExample Research Questions
AttentionSelective attention, spatial orienting, temporal attention, attentional capture"Does emotion capture attention automatically?"
MemoryEncoding, retrieval, WM capacity, false memory, recognition vs. recall"Do older adults show increased false memory?"
Decision MakingRisk, reward learning, impulsivity, perceptual decisions"Are substance users more impulsive in intertemporal choice?"
PerceptionThresholds, masking, awareness, object recognition"What is the contrast threshold for face detection?"
LanguageLexical access, sentence parsing, semantic processing"Does syntactic complexity slow reading at the verb?"
Executive FunctionInhibition, task switching, updating, cognitive flexibility"Is SSRT longer in ADHD children?"
Step 2: Select a Paradigm

Use this decision tree to narrow paradigm choices:

Attention:

  • Conflict/interference between dimensions -> Stroop task or Flanker task
  • If response-level conflict is key -> Flanker (separates search from conflict)
  • If word-reading automaticity is key -> Stroop
  • Spatial orienting -> Posner cueing
  • Exogenous (reflexive) vs. endogenous (voluntary) -> vary cue type and SOA
  • Search efficiency / feature binding -> Visual search
  • Temporal limits of attention -> Attentional blink

Memory:

  • STM scanning speed -> Sternberg task
  • False memory production -> DRM paradigm
  • Recollection vs. familiarity -> Remember-Know
  • VWM capacity -> Change detection
  • Serial position effects -> Serial position curve

Decision Making:

  • Decision making under ambiguity with learning -> Iowa Gambling Task
  • Impulsivity / temporal discounting -> Delay discounting
  • Sensitivity vs. bias decomposition -> Signal Detection Theory (Yes/No or 2AFC)
  • Perceptual/cognitive discrimination -> 2AFC

Perception:

  • Threshold estimation -> Psychophysical staircase (1-up/2-down or QUEST)
  • Few trials available -> QUEST (converges in ~30-50 trials; Watson & Pelli, 1983)
  • Simple implementation needed -> 1-up/2-down (converges in ~50-80 trials; Levitt, 1971)
  • Subliminal processing / visibility control -> Masking paradigms
  • Vary visibility continuously -> backward masking (SOA manipulation)
  • Prevent conscious identification -> sandwich masking (forward + backward)

Language:

  • Single-word recognition / lexical access -> Lexical decision
  • Spreading activation / semantic networks -> Priming (with lexical decision or naming)
  • Incremental sentence comprehension -> Self-paced reading or eye-tracking
  • Budget-friendly, no specialized equipment -> Self-paced reading
  • Maximum ecological validity and rich temporal data -> Eye-tracking

Executive Function:

  • Simple response inhibition (withholding) -> Go/No-Go
  • Action cancellation (stopping initiated response) -> Stop-signal task
  • Need a latent measure of inhibition speed -> Stop-signal (yields SSRT)
  • Cognitive flexibility / set shifting -> Task switching
  • Working memory updating under continuous load -> N-back
Step 3: Configure Parameters

For each selected paradigm, consult references/classic-paradigms.md for the full parameter reference. Apply these general rules:

Timing Parameters
ParameterDefaultAdjustment Rule
Stimulus durationUntil response (RT tasks) or 100-500 ms (brief presentation)Shorten for masking or iconic memory studies; lengthen for patient populations
ISI / ITI1000-2000 msIncrease to 2000-3000 ms for EEG (to separate ERPs); increase for fMRI (jittered 2-8 s for HRF deconvolution)
SOAParadigm-specific (see reference)Short SOA (<300 ms): automatic processes; Long SOA (>500 ms): strategic/controlled processes (Neely, 1977)
Response deadline1500-2000 ms for RT tasksTighten for speed-emphasis; loosen for accuracy-emphasis or elderly/clinical samples
Trial Counts
ScenarioMinimum Trials per ConditionRationale
Large effect (d > 0.8)40-60Stroop, Flanker, AB (Hedge et al., 2018)
Medium effect (d ~ 0.5)60-100Priming, switching, search slopes (McNamara, 2005; Monsell, 2003)
Small effect (d ~ 0.3)100-200Subtle manipulations, individual differences (Baker et al., 2021)
SDT measures (d', c)100+ total (50+ signal, 50+ noise)Macmillan & Creelman (2005)
Reliability-critical (SSRT, K)160-200 totalVerbruggen et al. (2019); Rouder et al. (2011)
Proportion Manipulations
  • Congruency proportion (Stroop, Flanker): 50/50 is the unbiased standard. Deviating introduces list-wide proportion congruency effects that modulate conflict (Logan & Zbrodoff, 1979; Bugg & Crump, 2012). Only deviate if proportion effects are the research question.
  • Cue validity (Posner): 80% valid for endogenous orienting (Posner, 1980); 50% (uninformative) for pure exogenous effects.
  • Stop-signal proportion: 25% is standard. Higher rates induce proactive slowing (Verbruggen et al., 2019).
  • Target prevalence (search, detection): 50% unless studying prevalence effects (Wolfe et al., 2005).
  • Relatedness proportion (priming): Keep at ~25-50% to minimize strategic expectancy; lower RP isolates automatic priming (Neely et al., 1989).
Step 4: Design Controls

Apply these control procedures:

4.1 Condition Assignment
  • Within-subjects preferred for most cognitive paradigms (maximizes power by eliminating between-subject variance; Maxwell & Delaney, 2004)
  • Between-subjects required when conditions produce carry-over (e.g., training studies, deception manipulations, proportion manipulations)
  • See references/design-principles.md, Section 1 for the full decision framework
4.2 Counterbalancing
  • 2-3 conditions: Full counterbalancing (all k! orders)
  • 4+ conditions: Balanced Latin Square (Williams, 1949); ensures each condition precedes every other condition equally often
  • Always counterbalance: Stimulus-response mappings, response hand assignments
  • Pseudo-randomize within blocks: No more than 3-4 consecutive same-condition trials; equal condition transitions (see references/design-principles.md, Section 5.4)
4.3 Practice Trials
  • Simple RT tasks: 10-20 practice trials (Luce, 1986)
  • Complex tasks (task switching, N-back): 20-40 practice trials with feedback
  • Adaptive tasks (staircase): 50-100 familiarization trials before data collection (Watson & Pelli, 1983)
  • Require >80% accuracy in practice before advancing
  • Use different stimuli from experimental trials
4.4 Catch Trials and Comprehension Checks
  • Detection tasks: Include 10-20% no-target catch trials (Posner, 1980)
  • Reading tasks: Comprehension probes after 30-50% of sentences (Just et al., 1982)
  • Masked priming: Post-experiment visibility check or 5-10% awareness probes (Forster & Davis, 1984)
Step 5: Specify Dependent Variables and Analysis
Primary DVs by Paradigm Type
Paradigm TypePrimary DVAnalysis Notes
Speeded RT tasksRT (ms) + accuracy (%)Always report both. Apply RT trimming: remove anticipatory (<200 ms) and slow (>2.5 SD or >2000 ms) responses. Analyze only correct trials for RT.
Accuracy-focused tasksProportion correct or d'Use SDT when signal/noise distinction applies (Macmillan & Creelman, 2005)
Memory tasksHit rate, false alarm rate, d', KCowan's K for change detection; d' for recognition
Adaptive thresholdThreshold estimateAverage last 6-8 reversals (staircase); maximum-likelihood estimate (QUEST)
Learning/decision tasksBlock-by-block performanceIGT: (C+D)-(A+B) per block of 20; Delay discounting: indifference points per delay
Show full SKILL.md (729 more words)Show less
  • Repeated-measures ANOVA: Standard for factorial designs; check sphericity (Girden, 1992)
  • Linear mixed-effects models: Preferred for unbalanced designs, missing data, item-level analysis; include random intercepts for subjects and items ("by-subject and by-item" approach; Baayen et al., 2008)
  • Bayesian analysis: Report Bayes factors for key comparisons when sample size is limited or null effects are informative (Rouder et al., 2009)
  • Drift-diffusion modeling: For decomposing RT and accuracy into drift rate, boundary separation, and non-decision time (Ratcliff & McKoon, 2008)

Quick Reference: Paradigm Selection Matrix

Research Question TypeFirst-Choice ParadigmAlternative
Does X capture attention?Posner cueing / Visual searchDot-probe task
Does X interfere with processing?Stroop / FlankerSimon task
What is VWM capacity for X?Change detectionContinuous report
Does X cause false memories?DRM paradigmMisinformation paradigm
Is recognition based on recollection or familiarity?Remember-KnowROC analysis
Does X affect inhibitory control?Stop-signal (SSRT)Go/No-Go
Does X modulate cognitive flexibility?Task switchingWisconsin Card Sorting
Is X processed without awareness?Backward masking + primingContinuous flash suppression
What is the perceptual threshold for X?QUEST / Staircase + 2AFCMethod of constant stimuli
Does X affect reading?Self-paced reading / Eye-trackingERP (N400, P600)
Does X prime Y?Semantic priming + LDTCross-modal priming
Is X related to impulsivity?Delay discountingStop-signal
Does X affect decision making under risk?Iowa Gambling TaskBalloon Analogue Risk Task
Does X affect WM updating?N-backOperation span

Domain-Specific Warnings

These are non-obvious pitfalls that require domain expertise:

  1. Stroop: Using fewer than 4 color-response mappings introduces item-specific contingency learning that mimics Stroop effects but is not conflict-based (Schmidt & Besner, 2008). Always use >= 4 colors.

  2. Stop-signal: Never estimate SSRT from mean Go RT alone. The integration method accounts for the Go RT distribution shape. Failed-stop RTs must be faster than Go RTs (independence assumption check; Logan & Cowan, 1984). Use the consensus guide (Verbruggen et al., 2019).

  3. Attentional blink: T1 must be masked (by a trailing distractor). Removing the T1+1 item eliminates the AB entirely (Raymond et al., 1992). Always include T1+1 distractor.

  4. Change detection (VWM): Retention intervals shorter than ~900 ms may allow iconic memory to contribute, inflating K estimates. Use >=900 ms retention interval, and consider articulatory suppression to prevent verbal recoding (Luck & Vogel, 1997; Vogel et al., 2001).

  5. DRM: False recall varies dramatically across lists (10-60%). Always report which word lists were used and their BAS values (Stadler et al., 1999). Roediger et al. (2001) normed 55 lists.

  6. Iowa Gambling Task: Apparent "learning" may reflect frequency-of-loss avoidance rather than long-term value sensitivity. Consider deck-by-deck analysis, not just (C+D)-(A+B) (Steingroever et al., 2013).

  7. Priming: High relatedness proportions (>50%) inflate priming through strategic expectancy, not automatic spreading activation. Use RP <= 25% to isolate automatic priming (Neely et al., 1989).

  8. Task switching: In alternating-runs designs (AABB), the response-stimulus interval (RSI) is confounded with cue-stimulus interval (CSI). Use cued-switching designs to separate preparation time from passive decay (Monsell, 2003; Meiran, 1996).

  9. Psychophysical staircases: Step sizes of <5% lead to staircases that fail to generate enough reversals. Use initial step sizes of at least 10-20% of the expected threshold range, then halve after the first 2-4 reversals (Garcia-Perez, 1998).

  10. N-back: Omission errors are more informative than commission errors (unlike Go/No-Go). Always report d' rather than raw accuracy, as d' separates sensitivity from bias (Haatveit et al., 2010). Include lure trials (n-1 or n+1 matches) to assess interference susceptibility (Gray et al., 2003).


References

  • Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.
  • Baker, D. H., Vilidaite, G., Lygo, F. A., et al. (2021). Power contours: Optimising sample size and precision in experimental psychology and human neuroscience. Psychological Methods, 26, 295-314.
  • Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models. Journal of Cognition, 1(1), 9.
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Erlbaum.
  • Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50, 1166-1186.
  • Macmillan, N. A., & Creelman, C. D. (2005). Detection Theory: A User's Guide (2nd ed.). Erlbaum.
  • Verbruggen, F., Aron, A. R., Band, G. P., et al. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife, 8, e46323.

© NeuroAIHub, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in packages/skills/skills/03_Cognitive_Psychology/cognitive-paradigm-design of NeuroAIHub/BrainPilot.

  • SKILL.md
  • references/classic-paradigms.md
  • references/design-principles.md

Open the folder on GitHubat commit 93f6855

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Questions about Cognitive Paradigm Design

What does Cognitive Paradigm Design do?

Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions. Cognitive Paradigm Design is an agent skill from NeuroAIHub/BrainPilot.

When should I use Cognitive Paradigm Design?

Cognitive Paradigm Design fits situations like: tasks that involve Hypothesis generation.

How do I install Cognitive Paradigm Design in Claude Code?

Run `npx skills add NeuroAIHub/BrainPilot --skill cognitive-paradigm-design -a claude-code`. Or copy the skill folder (packages/skills/skills/03_Cognitive_Psychology/cognitive-paradigm-design in NeuroAIHub/BrainPilot) into .claude/skills/cognitive-paradigm-design in your project. Claude Code loads it when a task matches its description.

How do I install Cognitive Paradigm Design in Codex?

Run `npx skills add NeuroAIHub/BrainPilot --skill cognitive-paradigm-design -a codex`. Or copy the skill folder (packages/skills/skills/03_Cognitive_Psychology/cognitive-paradigm-design in NeuroAIHub/BrainPilot) into .agents/skills/cognitive-paradigm-design in your project. Codex loads it when a task matches its description.

Can I use Cognitive Paradigm Design in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeuroAIHub/BrainPilot --skill cognitive-paradigm-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cognitive-paradigm-design, .gemini/skills/cognitive-paradigm-design, .github/skills/cognitive-paradigm-design and .opencode/skills/cognitive-paradigm-design in your project.

What does Cognitive Paradigm Design need to run?

SKILL.md names no scripts, command-line tools or credentials: Cognitive Paradigm Design is instructions for the agent only.

Does Cognitive Paradigm Design access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Cognitive Paradigm Design safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cognitive Paradigm Design use?

Cognitive Paradigm Design is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cognitive Paradigm Design use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Cognitive Paradigm Design?

Skills that share tags, products or a category with Cognitive Paradigm Design: Hypothesis Generation (spacering-net/codeg, 3.9k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars), Hypothesis Generation (K-Dense-AI/claude-scientific-writer, 2.4k stars) and Good Question (Rimagination/good-question, 305 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cognitive Paradigm Design?

NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,062 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.

Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.