菜单
Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| 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 Potential Technique (2nd ed.)","Macmillan & Creelman, 2005, Detection Theory (2nd ed.)"] |
| dependencies | {"required":["research-literacy"]} |
| review_status | ai-generated |
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.
Before executing the domain-specific steps below, you MUST:
For detailed methodology guidance, see the research-literacy skill.
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.
When given a research question, follow this sequence:
Map the research question to one or more core cognitive domains:
| Domain | Core Constructs | Example Research Questions |
|---|---|---|
| Attention | Selective attention, spatial orienting, temporal attention, attentional capture | "Does emotion capture attention automatically?" |
| Memory | Encoding, retrieval, WM capacity, false memory, recognition vs. recall | "Do older adults show increased false memory?" |
| Decision Making | Risk, reward learning, impulsivity, perceptual decisions | "Are substance users more impulsive in intertemporal choice?" |
| Perception | Thresholds, masking, awareness, object recognition | "What is the contrast threshold for face detection?" |
| Language | Lexical access, sentence parsing, semantic processing | "Does syntactic complexity slow reading at the verb?" |
| Executive Function | Inhibition, task switching, updating, cognitive flexibility | "Is SSRT longer in ADHD children?" |
Use this decision tree to narrow paradigm choices:
Attention:
Memory:
Decision Making:
Perception:
Language:
Executive Function:
For each selected paradigm, consult references/classic-paradigms.md for the full parameter reference. Apply these general rules:
| Parameter | Default | Adjustment Rule |
|---|---|---|
| Stimulus duration | Until response (RT tasks) or 100-500 ms (brief presentation) | Shorten for masking or iconic memory studies; lengthen for patient populations |
| ISI / ITI | 1000-2000 ms | Increase to 2000-3000 ms for EEG (to separate ERPs); increase for fMRI (jittered 2-8 s for HRF deconvolution) |
| SOA | Paradigm-specific (see reference) | Short SOA (<300 ms): automatic processes; Long SOA (>500 ms): strategic/controlled processes (Neely, 1977) |
| Response deadline | 1500-2000 ms for RT tasks | Tighten for speed-emphasis; loosen for accuracy-emphasis or elderly/clinical samples |
| Scenario | Minimum Trials per Condition | Rationale |
|---|---|---|
| Large effect (d > 0.8) | 40-60 | Stroop, Flanker, AB (Hedge et al., 2018) |
| Medium effect (d ~ 0.5) | 60-100 | Priming, switching, search slopes (McNamara, 2005; Monsell, 2003) |
| Small effect (d ~ 0.3) | 100-200 | Subtle 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 total | Verbruggen et al. (2019); Rouder et al. (2011) |
Apply these control procedures:
references/design-principles.md, Section 1 for the full decision frameworkreferences/design-principles.md, Section 5.4)| Paradigm Type | Primary DV | Analysis Notes |
|---|---|---|
| Speeded RT tasks | RT (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 tasks | Proportion correct or d' | Use SDT when signal/noise distinction applies (Macmillan & Creelman, 2005) |
| Memory tasks | Hit rate, false alarm rate, d', K | Cowan's K for change detection; d' for recognition |
| Adaptive threshold | Threshold estimate | Average last 6-8 reversals (staircase); maximum-likelihood estimate (QUEST) |
| Learning/decision tasks | Block-by-block performance | IGT: (C+D)-(A+B) per block of 20; Delay discounting: indifference points per delay |
| Research Question Type | First-Choice Paradigm | Alternative |
|---|---|---|
| Does X capture attention? | Posner cueing / Visual search | Dot-probe task |
| Does X interfere with processing? | Stroop / Flanker | Simon task |
| What is VWM capacity for X? | Change detection | Continuous report |
| Does X cause false memories? | DRM paradigm | Misinformation paradigm |
| Is recognition based on recollection or familiarity? | Remember-Know | ROC analysis |
| Does X affect inhibitory control? | Stop-signal (SSRT) | Go/No-Go |
| Does X modulate cognitive flexibility? | Task switching | Wisconsin Card Sorting |
| Is X processed without awareness? | Backward masking + priming | Continuous flash suppression |
| What is the perceptual threshold for X? | QUEST / Staircase + 2AFC | Method of constant stimuli |
| Does X affect reading? | Self-paced reading / Eye-tracking | ERP (N400, P600) |
| Does X prime Y? | Semantic priming + LDT | Cross-modal priming |
| Is X related to impulsivity? | Delay discounting | Stop-signal |
| Does X affect decision making under risk? | Iowa Gambling Task | Balloon Analogue Risk Task |
| Does X affect WM updating? | N-back | Operation span |
These are non-obvious pitfalls that require domain expertise:
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.
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).
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.
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).
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.
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).
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).
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).
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).
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).
Domain knowledge for building extracellular electrophysiology pipelines with SpikeInterface: loading data with extractors, preprocessing, running spike sorters, post-processing via SortingAnalyzer, quality metrics, curation, comparison, visualization, and export.
Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn. Use this skill whenever the user asks to preprocess fMRI/BOLD data, run fMRIPrep on a BIDS dataset, set up Docker/Singularity/Apptainer containers for fMRIPrep, choose output spaces (MNI152NLin2009cAsym, fsaverage, fsLR/CIFTI), configure susceptibility distortion correction (SDC), extract or interpret the confounds table, resample to surface/grayordinates, cite fMRIPrep and its dependencies, or debug fMRIPrep crashes and hangs.
Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical surface visualization. Use this skill whenever the user works with brain connectivity matrices, connectomes, graph theory on brain networks, parcellated brain data (Schaefer, Cammoun, Desikan-Killiany), cortical surface templates (fsaverage, fsLR, CIVET, Conte69), network communication metrics, null model generation, spatial autocorrelation on brain maps, community detection in connectomes, or surface-based visualization with PyVista/PySurfer. Also trigger when the user mentions netneurotools, netneurolab, structure-function coupling, network neuroscience, brain graph analysis, or needs to fetch neuroimaging atlases and templates.
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.
Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
Toolbox for markerless animal pose estimation with DeepLabCut. Covers single/multi-animal tracking, SuperAnimal pretrained models, 2D/3D pose estimation, keypoint labeling GUI, model training/evaluation, video analysis, and behavioral quantification. Use when the user needs animal pose estimation, behavior tracking, keypoint detection in videos, or mentions DeepLabCut/DLC/SuperAnimal.