| Field | Value |
|---|---|
| Name | DRM False Memory Paradigm |
| Version | v0.1.0-dev |
| URL / Repository | TaskBeacon/T000055-drm-false-memory-paradigm-task |
| Short Description | Visual DRM list-learning task with immediate confidence-based recognition of studied words, critical lures, and foils |
| Created By | TaskBeacon build pipeline |
| Date Updated | 2026-04-17 |
| PsyFlow Version | 0.1.12 |
| PsychoPy Version | 2025.2.4 |
| Modality | Behavior |
| Language | English |
This task implements a visual Deese-Roediger-McDermott false-memory paradigm. Participants study six short semantic word lists, one list per block, and then complete an immediate recognition test after each list. Recognition uses a 1-4 confidence judgment scale, allowing the task to quantify endorsed studied words, false recognition of the critical lure, and endorsements of unrelated foils.
The implementation is deterministic and audit-friendly. Each list theme is fixed in config, the recognition item order is seeded, and the runtime keeps participant-facing wording in YAML stimuli rather than hardcoded strings.
| Step | Description |
|---|---|
| Load Config | Read the mode-specific config and task metadata. |
| Collect Subject Info | Collect subject ID in human mode or inject deterministic IDs in QA/sim. |
| Initialize Runtime | Create the PsychoPy window, keyboard, triggers, and stimulus bank. |
| Show Instructions | Present the general DRM instructions and confidence scale. |
| Run List Block | Show one list theme, study its words, and run the immediate recognition test. |
| Repeat Blocks | Run all six DRM themes in the configured order. |
| Finish | Present the goodbye screen, send the end trigger, and quit PsychoPy. |
| Step | Description |
|---|---|
| Block Intro | Show the list label and explain that a recognition test will follow. |
| Study Instructions | Present the global task instructions and confidence-scale reminder. |
| Study Word | Show one study word at a time for fixed-duration encoding. |
| Study Gap | Show a brief blank interval between study words. |
| Recognition Instructions | Remind the participant about the 1-4 confidence scale. |
| Recognition Item | Show one test word and collect a confidence response. |
| Block Summary | Show studied-item, lure, and foil endorsement rates. |
| Goodbye | Show the overall false-memory summary for the session. |
| Feature | Description |
|---|---|
| Condition Scheduling | BlockUnit.generate_conditions(...) expands the six list themes into ordered list blocks. |
| Determinism | build_drm_block_plan(...) uses the overall seed, block index, and list theme to generate study order, lure selection, and foil sampling. |
| Trial Context | Every participant-visible phase in src/run_trial.py calls set_trial_context(...) before display or response capture. |
| Simulation | The scripted responder presses the first valid key for smoke tests, and the sampler responder models studied/lure/foil endorsement patterns. |
| Setting | Value |
|---|---|
| Subject Info | subject_id collected as a 3-digit integer in human mode. |
| Window | 1280 x 720, white background, pix units, fullscreen off. |
| Stimuli | Config-defined instructions, study-word text, recognition text, confidence labels, and blank screens. |
| Timing | Study word 1.25 s, study ISI 0.35 s, recognition response window 2.0 s. |
| Triggers | Experiment, block, study, recognition, summary, response, and timeout triggers are mapped in config/*.yaml. |
subject_id is collected as a 3-digit integer in human mode.
1280 x 720, white background, pix units, fullscreen off.
Config-defined instructions, study-word text, recognition word text, confidence labels, and blank screens.
Study word 1.25 s, study ISI 0.35 s, and recognition response window 2.0 s.
Participants study several short semantic word lists that are designed to elicit false recognition of a nonpresented critical lure. After each list, they complete an immediate recognition test using a 4-point confidence scale. The task measures the tendency to endorse studied items, to falsely recognize the lure, and to make false-alarm judgments to unrelated foils.
The implementation is designed for reproducibility and auditability. List order, foil sampling, and recognition order are deterministic from the task seed, and all participant-facing wording remains config-driven so the task can be reviewed or localized without changing runtime code.