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Designs habituation and preferential-looking paradigms with age-appropriate timing parameters and exclusion criteria
AGPL-3.0Auto-check passed
Install Infant Looking Time Designer
$ npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NeuroAIHub/BrainPilot infant-looking-time-designer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer .claude/skills/infant-looking-time-designer && rm -rf skills-srcUse ~/.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/
Install the "infant-looking-time-designer" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer into .claude/skills/infant-looking-time-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infant-looking-time-designer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NeuroAIHub/BrainPilot infant-looking-time-designer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer .agents/skills/infant-looking-time-designer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "infant-looking-time-designer" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer into .agents/skills/infant-looking-time-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infant-looking-time-designer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NeuroAIHub/BrainPilot infant-looking-time-designer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer .cursor/skills/infant-looking-time-designer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "infant-looking-time-designer" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer into .cursor/skills/infant-looking-time-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infant-looking-time-designer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NeuroAIHub/BrainPilot.git --path packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NeuroAIHub/BrainPilot infant-looking-time-designer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer .gemini/skills/infant-looking-time-designer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "infant-looking-time-designer" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer into .gemini/skills/infant-looking-time-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infant-looking-time-designer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NeuroAIHub/BrainPilot infant-looking-time-designerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer .github/skills/infant-looking-time-designer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "infant-looking-time-designer" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer into .github/skills/infant-looking-time-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infant-looking-time-designer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NeuroAIHub/BrainPilot infant-looking-time-designer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer .opencode/skills/infant-looking-time-designer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "infant-looking-time-designer" agent skill from https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer into .opencode/skills/infant-looking-time-designer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infant-looking-time-designer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
infant-looking-time-designer- GitHub stars
- 1.1k
- Token cost
- ~4.5k tokens
- SKILL.md length
- 1,928 words
- Files
- 2 (incl. references)
- Skills in repo
- 59
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
Designs habituation and preferential-looking paradigms with age-appropriate timing parameters and exclusion criteria
- Works in 5 steps: State the research question -- What… → Justify the method choice -- Why is this… → Declare expected outcomes -- What… → …
- SKILL.md covers Purpose, When to Use This Skill, Research Planning Protocol and ⚠️ Verification Notice, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Infant Looking Time Designer is an agent skill from NeuroAIHub/BrainPilot. Designs habituation and preferential-looking paradigms with age-appropriate timing parameters and exclusion criteria
Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/age-parameters.yaml`).
The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.
Example prompts
- “Use the infant-looking-time-designer skill to design habituation and preferential-looking paradigms with age-appropriate timing parameters and…”
- “/infant-looking-time-designer”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 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.
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
Infant Looking Time Designer loads about 4.5k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 36 tokens; SKILL.md has 1,928 words of instructions outside code blocks.
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). 1,928 words, ~4,509 tokens.
Download SKILL.mdSave it as
.claude/skills/infant-looking-time-designer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- infant-looking-time-designer
- description
- Designs habituation and preferential-looking paradigms with age-appropriate timing parameters and exclusion criteria
- domain
- developmental-cognition
- version
- 1.0.0
- papers
- Fantz, 1964, Colombo & Mitchell, 2009, Oakes, 2010, Hunter & Ames, 1988, Baillargeon, 1987
- dependencies.required
- research-literacy
- review_status
- ai-generated
Infant Looking Time Paradigm Designer
Purpose
This skill encodes expert methodological knowledge for designing infant looking-time studies, including habituation, preferential-looking, and violation-of-expectation paradigms. It provides age-appropriate timing parameters, habituation criteria, exclusion standards, and coding reliability benchmarks that require specialized training in developmental methodology. A general-purpose programmer would not know the appropriate trial durations by age, when to expect novelty versus familiarity preferences, or how to set habituation criteria.
When to Use This Skill
- Designing a new habituation study for infants of a specific age
- Setting up a preferential-looking paradigm (side-by-side or central fixation)
- Creating a violation-of-expectation study to test infant knowledge
- Choosing age-appropriate timing parameters (trial duration, ITI, attention-getters)
- Establishing exclusion criteria and coding reliability standards
- Deciding between online (webcam-based) and in-lab testing
- Determining sample size and expected effect sizes for infant studies
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.
Paradigm Selection Decision Tree
What is the research question?
|
+-- Does the infant have a representation of X?
| |
| +-- Test via surprise --> Violation-of-Expectation (Baillargeon, 1987)
| |
| +-- Test via discrimination --> Habituation + Test (Fantz, 1964)
|
+-- Can the infant discriminate A from B?
| |
| +-- Simultaneous comparison --> Preferential Looking (Fantz, 1958)
| |
| +-- Sequential comparison --> Habituation + Novelty Test
|
+-- Does the infant prefer/attend more to A vs B?
|
+-- Spontaneous preference --> Preferential Looking
|
+-- After familiarization --> Habituation + TestHabituation Paradigm Design
Overview
Habituation measures the decline in looking time as infants become familiar with a repeated stimulus, followed by a test phase to assess discrimination or representation (Colombo & Mitchell, 2009).
Habituation Criterion Methods
| Method | Description | Default Criterion | Source |
|---|---|---|---|
| Criterion-based (preferred) | Trials continue until looking decreases to a threshold | 50% of initial baseline | Oakes, 2010; Colombo & Mitchell, 2009 |
| Fixed-trial | Set number of habituation trials | Age-dependent (see below) | Cohen, 1976 |
| Sliding window | Criterion computed over a moving window of trials | Window of 3-4 consecutive trials | Oakes, 2010 |
Criterion-Based Habituation Parameters
| Parameter | Value | Source |
|---|---|---|
| Baseline window | First 3 trials (average looking time) | Oakes, 2010 |
| Decrement criterion | Looking drops to 50% of baseline | Oakes, 2010; Colombo & Mitchell, 2009 |
| Criterion window | 3 consecutive trials below criterion | Oakes, 2010 |
| Maximum trials before aborting | 20-25 trials (or abandon) | Colombo & Mitchell, 2009 |
| Minimum habituation trials | 4-6 trials (to ensure real exposure) | Expert consensus |
Fixed-Trial Habituation by Age
| Age Group | Recommended Trials | Source |
|---|---|---|
| Neonates (0-1 mo) | 8-12 trials | Slater, 1995 |
| 3-6 months | 6-10 trials | Cohen, 1976; Colombo & Mitchell, 2009 |
| 6-12 months | 6-8 trials | Colombo & Mitchell, 2009 |
| 12-24 months | 4-8 trials | Colombo & Mitchell, 2009 |
Maximum Trial Duration by Age
| Age Group | Max Trial Duration | Source |
|---|---|---|
| Neonates (0-1 mo) | 60 s | Slater, 1995 |
| 1-3 months | 30-60 s | Colombo & Mitchell, 2009 |
| 3-6 months | 20-30 s | Colombo & Mitchell, 2009 |
| 6-12 months | 15-20 s | Colombo & Mitchell, 2009 |
| 12-24 months | 10-20 s | Colombo & Mitchell, 2009 |
Look-Away Criterion
A trial ends when the infant looks away for a continuous duration:
| Age Group | Look-Away Duration | Source |
|---|---|---|
| Neonates | 2-3 s | Slater, 1995 |
| 3-6 months | 2 s | Oakes, 2010 |
| 6-12 months | 1-2 s | Oakes, 2010 |
| 12+ months | 1-2 s | Oakes, 2010 |
Minimum look before look-away counts: Infant must look for at least 0.5-1.0 s before a look-away can terminate the trial (Oakes, 2010).
Preferential Looking Design
Standard Configuration (Fantz, 1958)
| Parameter | Value | Source |
|---|---|---|
| Display arrangement | Side-by-side, equidistant from midline | Fantz, 1958 |
| Stimulus eccentricity | 15-20 degrees from center | Aslin, 2007 |
| Position counterbalancing | Each stimulus appears equally on left and right | Fantz, 1958; Oakes, 2010 |
| Number of test trials | 4-8 trials (minimum 2 per side assignment) | Oakes, 2010 |
| Trial duration | 10-20 s (depending on age) | Oakes, 2010 |
Interpreting Preference Direction
Is there a familiarization/habituation phase?
|
+-- NO (spontaneous preference) --> Report raw preference proportion
|
+-- YES --> What is the age and task complexity?
|
+-- Younger infants + simple stimuli --> Expect NOVELTY preference
| (Hunter & Ames, 1988)
|
+-- Younger infants + complex stimuli --> Expect FAMILIARITY preference
| (Hunter & Ames, 1988)
|
+-- Older infants + simple stimuli --> Expect NOVELTY preference
|
+-- Brief familiarization + any age --> Expect FAMILIARITY preference
(Hunter & Ames, 1988; Roder et al., 2000)Hunter & Ames (1988) model: Preference direction is determined by the interaction of:
- Age (processing speed)
- Stimulus complexity (encoding difficulty)
- Familiarization duration (encoding completeness)
General rule: Incomplete encoding produces familiarity preference; complete encoding produces novelty preference (Hunter & Ames, 1988).
Looking Time Preference Threshold
| Measure | Threshold | Source |
|---|---|---|
| Proportion looking to target | > 55% of total looking time | Oakes, 2010 |
| Statistical test | One-sample t-test against 50% (chance) | Standard practice |
| Effect size benchmark (infant studies) | Cohen's d ~ 0.4 -- 0.6 (medium) | Oakes, 2010 |
Violation-of-Expectation (VoE) Design
Overview (Baillargeon, 1987)
Infants view an expected and an unexpected event. Longer looking at the unexpected event is interpreted as detection of the violation.
Standard VoE Structure
- Familiarization phase: Infants see the basic event (e.g., screen rotating)
- Test phase: Two events presented (expected vs. unexpected), counterbalanced for order
- Measure: Looking time difference between expected and unexpected events
VoE Parameters
| Parameter | Value | Source |
|---|---|---|
| Familiarization trials | 4-6 trials | Baillargeon, 1987; Spelke et al., 1992 |
| Test trials per event type | 2-3 trials each | Baillargeon, 1987 |
| Maximum test trial duration | 30-60 s (age-dependent; see habituation table) | Colombo & Mitchell, 2009 |
| Event presentation order | Counterbalanced (expected-first vs. unexpected-first) | Standard practice |
| Expected effect direction | Longer looking at unexpected event | Baillargeon, 1987 |
Important Methodological Caveats
- Low-level perceptual confounds: Ensure expected and unexpected events are matched on visual features (motion, color, surface area). The unexpected event should differ only in the conceptual violation (Baillargeon, 2004).
- Familiarity preference interpretation: Longer looking at the "expected" event does not necessarily mean failure to detect the violation; it may reflect familiarity preference (Hunter & Ames, 1988).
- Replication concerns: Some classic VoE findings have proven difficult to replicate (Baillargeon et al., 2016).
General Timing Parameters
Attention-Getters
| Parameter | Value | Source |
|---|---|---|
| Type | Central animated stimulus with sound | Oakes, 2010 |
| Duration | 3-5 s (or until infant fixates center) | Expert consensus |
| Presentation | Before every trial | Oakes, 2010 |
| Purpose | Recenter gaze to midline before trial onset | Oakes, 2010 |
Inter-Trial Interval
| Age Group | ITI Duration | Source |
|---|---|---|
| All ages | 1-3 s (blank screen or neutral gray) | Oakes, 2010 |
See references/age-parameters.yaml for a comprehensive age-by-parameter table.
Exclusion Criteria
Trial-Level Exclusion
| Criterion | Threshold | Source |
|---|---|---|
| Minimum looking on test trial | > 0.5 s looking required | Expert consensus |
| Fussiness (infant turns away from screen) | Trial excluded | Oakes, 2010 |
| Parental interference | Trial excluded | Standard practice |
| Equipment failure (eye-tracker loss) | Trial excluded | Standard practice |
Participant-Level Exclusion
| Criterion | Threshold | Source |
|---|---|---|
| Completed test trials | Must complete > 50% of test trials | Oakes, 2010 |
| Failure to habituate | Exclude if not habituated after maximum trials | Colombo & Mitchell, 2009 |
| Side bias | > 90% looking to one side across all trials | Oakes, 2010 |
| Fussiness | General fussiness preventing data collection | Standard practice |
| Parent report of atypical state | Sleepy, ill, recent feeding issues | Standard practice |
Expected Exclusion Rates
| Setting | Expected Exclusion Rate | Source |
|---|---|---|
| In-lab (3-6 months) | 20-40% | Oakes, 2010 |
| In-lab (6-12 months) | 15-30% | Oakes, 2010 |
| In-lab (12-24 months) | 10-25% | Oakes, 2010 |
| Online (webcam-based) | 30-50% (higher due to environment) | Smith-Flores et al., 2022 |
Sample size implication: Recruit 1.5-2x the target N to account for exclusions (Oakes, 2010).
Coding Reliability
Live vs. Offline Coding
| Method | Description | When to Use |
|---|---|---|
| Live coding | Experimenter presses key during session | Habituation criterion in real-time |
| Offline coding | Frame-by-frame from video recording | All published looking time data |
| Automated (eye-tracking) | Tobii, EyeLink, or webcam-based | High precision needed; older infants |
Show full SKILL.md (775 more words)Show less
Reliability Standards
| Metric | Minimum Standard | Source |
|---|---|---|
| Proportion of sessions double-coded | > 25% (at least) | Oakes, 2010 |
| Inter-coder agreement (proportion) | > 90% | Oakes, 2010 |
| Cohen's kappa (looking/not-looking) | > 0.85 | Oakes, 2010; Colombo & Mitchell, 2009 |
| Pearson r (total looking times) | > 0.90 | Oakes, 2010 |
Coding Resolution
| Method | Temporal Resolution | Source |
|---|---|---|
| Frame-by-frame video coding | 33 ms (30 fps) or 17 ms (60 fps) | Standard practice |
| Live key-press coding | ~200-300 ms (human reaction time) | Expert consensus |
| Eye-tracker | 4-17 ms (60-250 Hz) | Equipment-dependent |
Online vs. In-Lab Testing
Considerations for Online Infant Testing
| Factor | In-Lab | Online | Source |
|---|---|---|---|
| Environmental control | High | Low (home distractions) | Smith-Flores et al., 2022 |
| Stimulus calibration | Precise (visual angle, distance) | Variable (screen size, distance) | Zaadnoordijk et al., 2022 |
| Looking time coding | Offline video or eye-tracker | Webcam-based or parent-coded | Smith-Flores et al., 2022 |
| Exclusion rate | 20-30% | 30-50% | Smith-Flores et al., 2022 |
| Sample diversity | Limited to local population | Broader demographic reach | Zaadnoordijk et al., 2022 |
| Recommended platform | N/A | Lookit, Labvanced, Gorilla | Smith-Flores et al., 2022 |
Critical: Online studies require explicit instructions to parents about distance from screen (typically 60 cm) and minimizing distractions. Validate online paradigms against in-lab data before drawing novel conclusions (Smith-Flores et al., 2022).
Common Pitfalls
- Ignoring novelty vs. familiarity preference: Assuming longer looking always means preference for the novel stimulus. Depending on age, complexity, and encoding time, infants may show familiarity preference instead (Hunter & Ames, 1988).
- Fixed vs. criterion habituation: Using fixed-trial habituation when criterion-based is more appropriate. Criterion-based habituation ensures infants have actually encoded the stimulus before testing (Oakes, 2010).
- Perceptual confounds in VoE: Unexpected events that differ from expected events on low-level perceptual features (motion path length, surface area visible) confound interpretation (Baillargeon, 2004).
- Insufficient counterbalancing: Failing to counterbalance stimulus position (left/right), trial order (expected/unexpected first), and stimulus assignment across infants.
- Not reporting exclusion rates: Journals increasingly require transparent reporting of how many infants were excluded and why. High exclusion rates may bias the sample (Oakes, 2010).
- Coding reliability not reported: All published looking-time data should include inter-coder reliability from offline coding, even if live coding was used during the session.
- Age-inappropriate timing: Using adult-like trial durations with young infants, or overly short trials with neonates, leading to floor/ceiling effects.
Minimum Reporting Checklist
Based on Oakes (2010) and Colombo & Mitchell (2009):
- Paradigm type (habituation, preferential looking, VoE)
- Age of participants (mean, range, in days or weeks for infants < 12 months)
- Habituation criterion and method (if applicable)
- Number of habituation trials to criterion (mean, SD)
- Trial duration parameters (maximum duration, look-away criterion, minimum look)
- Number of test trials and counterbalancing scheme
- Attention-getter description and duration
- Exclusion criteria and number excluded (with reasons)
- Coding method (live, offline, automated) and temporal resolution
- Inter-coder reliability (kappa, r, proportion agreement)
- Looking time data: means and SDs per condition
- Statistical tests, effect sizes, and confidence intervals
References
- Aslin, R. N. (2007). What's in a look? Developmental Science, 10(1), 48-53.
- Baillargeon, R. (1987). Object permanence in 3.5- and 4.5-month-old infants. Developmental Psychology, 23(5), 655-664.
- Baillargeon, R. (2004). Infants' reasoning about hidden objects: Evidence for event-general and event-specific expectations. Developmental Science, 7(4), 391-424.
- Baillargeon, R., Stavans, M., Wu, D., Gertner, Y., Setoh, P., Kittredge, A. K., & Bernard, A. (2016). Object individuation and physical reasoning in infancy: An integrative account. Language Learning and Development, 8(1), 4-46.
- Cohen, L. B. (1976). Habituation of infant visual attention. In T. J. Tighe & R. N. Leaton (Eds.), Habituation: Perspectives from Child Development, Animal Behavior, and Neurophysiology. Erlbaum.
- Colombo, J., & Mitchell, D. W. (2009). Infant visual habituation. Neurobiology of Learning and Memory, 92(2), 225-234.
- Fantz, R. L. (1958). Pattern vision in young infants. The Psychological Record, 8, 43-47.
- Fantz, R. L. (1964). Visual experience in infants: Decreased attention to familiar patterns relative to novel ones. Science, 146(3644), 668-670.
- Hunter, M. A., & Ames, E. W. (1988). A multifactor model of infant preferences for novel and familiar stimuli. Advances in Infancy Research, 5, 69-95.
- Oakes, L. M. (2010). Using habituation of looking time to assess mental processes in infancy. Journal of Cognition and Development, 11(3), 255-268.
- Roder, B. J., Bushnell, E. W., & Sasseville, A. M. (2000). Infants' preferences for familiarity and novelty during the course of visual processing. Infancy, 1(4), 491-507.
- Slater, A. (1995). Visual perception and memory at birth. Advances in Infancy Research, 9, 107-162.
- Smith-Flores, A. S., Perez, J., Zhang, M. H., & Feigenson, L. (2022). Online measures of looking and learning in infancy. Infancy, 27(1), 4-24.
- Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99(4), 605-632.
- Zaadnoordijk, L., Buckler, H., & Cusack, R. (2022). Online testing in developmental science: A guide to design and implementation. Behavior Research Methods, 54, 1202-1221.
See references/ for detailed age-by-parameter tables and paradigm checklists.
© 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 1 other file (references) in packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer of NeuroAIHub/BrainPilot.
- SKILL.md
- references/age-parameters.yaml
Open the folder on GitHubat commit 93f6855
Compare with similar skills
Infant Looking Time Designer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
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| Infant Looking Time Designer this skillNeuroAIHub/BrainPilot | 1.1k | — | ~4.5k | Automated safety check: Pass | AGPL-3.0 | |
| Frontend Slideszarazhangrui/frontend-slides | 30k | 16 repos | ~7k | Automated safety check: Pass | MIT | |
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| Canvas Designanthropics/skills | 180k | 52 repos | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Impeccablebestofjs/bestofjs | 3.1k | 26 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Brand and Design Toolkitnextlevelbuilder/ui-ux-pro-max-skill | 135k | 1 repos | ~3.5k | Automated safety check: Pass | MIT |
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Questions about Infant Looking Time Designer
What does Infant Looking Time Designer do?
Designs habituation and preferential-looking paradigms with age-appropriate timing parameters and exclusion criteria. Infant Looking Time Designer is an agent skill from NeuroAIHub/BrainPilot.
How do I install Infant Looking Time Designer in Claude Code?
Run `npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a claude-code`. Or copy the skill folder (packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer in NeuroAIHub/BrainPilot) into .claude/skills/infant-looking-time-designer in your project. Claude Code loads it when a task matches its description.
How do I install Infant Looking Time Designer in Codex?
Run `npx skills add NeuroAIHub/BrainPilot --skill infant-looking-time-designer -a codex`. Or copy the skill folder (packages/skills/skills/11_Developmental_Cognition/infant-looking-time-designer in NeuroAIHub/BrainPilot) into .agents/skills/infant-looking-time-designer in your project. Codex loads it when a task matches its description.
Can I use Infant Looking Time Designer 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 infant-looking-time-designer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/infant-looking-time-designer, .gemini/skills/infant-looking-time-designer, .github/skills/infant-looking-time-designer and .opencode/skills/infant-looking-time-designer in your project.
What does Infant Looking Time Designer need to run?
SKILL.md names no scripts, command-line tools or credentials: Infant Looking Time Designer is instructions for the agent only.
Does Infant Looking Time Designer 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 Infant Looking Time Designer 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 Infant Looking Time Designer use?
Infant Looking Time Designer 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 Infant Looking Time Designer use?
About 4.5k tokens (SKILL.md is roughly 18k 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 1.5k tokens, read only when the agent opens those files.
What are the alternatives to Infant Looking Time Designer?
Skills that share tags, products or a category with Infant Looking Time Designer: Frontend Slides (zarazhangrui/frontend-slides, 30k stars), Algorithmic Art with p5.js (anthropics/skills, 180k stars), Canvas Design (anthropics/skills, 180k stars) and Impeccable (bestofjs/bestofjs, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Infant Looking Time Designer?
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.