Experiment Design | Skills Pool

Skill ファイル

Experiment Design

Use this skill for "design experiment", "create PsychoPy experiment", "stimulus presentation", "experiment protocol", "timing validation", "trial structure", "block design", "event-related design", "PsychoPy builder", "create stimuli", "LSL markers", "Lab Streaming Layer", "event markers", "trigger codes", or when the user wants to design or implement a neuroscience experiment.

neuromechanist15 スター2026/04/02

職業
カテゴリ: 教育

スキル内容

Design and implement neuroscience experiments with PsychoPy, including stimulus presentation, timing validation, event markers, and Lab Streaming Layer (LSL) integration.

When to Use

Designing a new behavioral or neuroimaging experiment
Creating PsychoPy scripts for stimulus presentation
Setting up event markers via LSL or parallel port
Validating timing accuracy
Converting an experiment protocol to code

Experiment Design Principles

Trial Structure

Every trial consists of:

[Fixation] -> [Stimulus] -> [Response Window] -> [Inter-trial Interval]
   |              |               |                    |
  marker       marker          marker              marker

Design Types

Design

関連 Skill

from psychopy import visual, core, event, data, gui
import numpy as np

# Experiment parameters
exp_info = {
    "participant": "",
    "session": "01",
    "task": "experiment_name",
}

# GUI dialog
dlg = gui.DlgFromDict(exp_info, title="Experiment")
if not dlg.OK:
    core.quit()

# Window setup
win = visual.Window(
    size=[1920, 1080],
    fullscr=True,
    monitor="testMonitor",
    units="deg",
    color=[0, 0, 0],
)

# Stimuli
fixation = visual.TextStim(win, text="+", height=2)
stimulus = visual.ImageStim(win, image=None, size=[10, 10])
feedback = visual.TextStim(win, text="", height=1.5)

# Trial handler
conditions = data.importConditions("conditions.xlsx")
trials = data.TrialHandler(
    conditions,
    nReps=1,
    method="random",
)

# Clock
clock = core.Clock()

# Main experiment loop
for trial in trials:
    # Fixation
    fixation.draw()
    win.flip()
    core.wait(0.5)  # 500 ms fixation

    # Stimulus
    stimulus.image = trial["stimulus_file"]
    stimulus.draw()
    win.flip()
    # Send marker here

    # Response
    clock.reset()
    keys = event.waitKeys(
        maxWait=2.0,
        keyList=["left", "right", "escape"],
        timeStamped=clock,
    )

    if keys:
        if keys[0][0] == "escape":
            core.quit()
        trials.addData("response", keys[0][0])
        trials.addData("rt", keys[0][1])

    # ITI (jittered)
    iti = np.random.uniform(0.8, 1.2)
    core.wait(iti)

# Save data
import os
os.makedirs("data", exist_ok=True)
trials.saveAsWideText(f"data/sub-{exp_info['participant']}_task-{exp_info['task']}.csv")
win.close()
core.quit()

# conditions.xlsx or conditions.csv
stimulus_file,condition,correct_response
stimuli/face01.png,face,left
stimuli/house01.png,house,right
stimuli/face02.png,face,left

from pylsl import StreamInfo, StreamOutlet

# Create marker stream
info = StreamInfo(
    name="ExperimentMarkers",
    type="Markers",
    channel_count=1,
    nominal_srate=0,  # irregular rate
    channel_format="string",
    source_id="psychopy_markers",
)
outlet = StreamOutlet(info)

# Send marker at stimulus onset
stimulus.draw()
win.flip()
outlet.push_sample(["stimulus_onset"])  # Send immediately after flip

Marker	Code	Description
stimulus_onset	S1-S99	Stimulus presentation
response	R1-R4	Participant response
feedback	F1-F2	Correct/incorrect feedback
block_start	B1-B10	Block onset
block_end	BE	Block offset
trial_start	T	Trial onset
experiment_start	EXP_START	First trial
experiment_end	EXP_END	Last trial

onset	duration	trial_type	value	HED
0.0	0.0	stimulus	S1	Sensory-event, Visual-presentation, (Image, Face)
1.5	0.0	response	R1	Agent-action, (Press, Key/Left)

# Add a small white square in the corner that flashes with stimulus
photodiode = visual.Rect(win, width=50, height=50, pos=[900, -500], units="pix")

# During stimulus presentation
stimulus.draw()
photodiode.fillColor = [1, 1, 1]  # White
photodiode.draw()
win.flip()
# Photodiode sensor on screen corner measures actual onset time

# Check for dropped frames
win.recordFrameIntervals = True
# After experiment:
frame_intervals = win.frameIntervals
dropped = sum(1 for fi in frame_intervals if fi > 1.5 * (1.0 / 60.0))
print(f"Dropped frames: {dropped}/{len(frame_intervals)}")

data/
  sub-01/
    sub-01_task-name_events.tsv    # onset, duration, trial_type, response, rt
    sub-01_task-name_beh.tsv       # behavioral data
    sub-01_task-name_beh.json      # metadata