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Chinese hand laterality judgment task with practice and test blocks, left/right key responses, and four generated hand-image stimuli.
Created By
TaskBeacon
Date Updated
2026-04-16
PsyFlow Version
0.1.12
PsychoPy Version
2025.2.4
Modality
Behavior
Language
Chinese
1. Task Overview
This task measures hand laterality judgment using the classic left-hand/right-hand mental-rotation paradigm. Participants see a hand image and decide whether it is a left hand or a right hand, regardless of whether the image shows the back or palm view and regardless of rotation.
The implementation is built around four generated hand assets, a Chinese instruction screen, one practice block, and six formal test blocks. Responses use f for left and j for right, while space advances instruction and break screens.
2. Task Flow
Block-Level Flow
Step
Description
Load Config
main.py loads the mode-specific YAML file and initializes task settings.
Collect Subject Info
Human mode uses the subject form; QA and sim modes inject deterministic IDs.
Initialize Runtime
The PsychoPy window, triggers, and stimulus bank are created.
Show Instructions
Participants read the Chinese instructions and press space to begin.
Run Practice Block
One practice block presents 18 trials with feedback after each response.
Run Test Blocks
Six test blocks present 32 trials each with no feedback.
Show Break Screens
Practice and test blocks use separate break screens with summary metrics.
Finish
A goodbye screen reports summary metrics, then the data are saved and the task exits.
Trial-Level Flow
Step
Description
Fixation
A centered fixation cross appears before each judgment trial.
Hand Stimulus
A generated hand image is shown in the center of the screen.
Response Window
Participants choose left (f) or right (j) within the response deadline.
Practice Feedback
Practice trials show correctness feedback or timeout feedback.
ITI
A brief fixation-only inter-trial interval separates trials.
Controller Logic
Feature
Description
Trial generation
build_session_plan() creates an 18-trial practice block followed by six 32-trial test blocks.
Stimulus selection
build_trial_sequence() cycles through left/right hand side, back/palm view, and four orientation labels.
Response mapping
f means left hand and j means right hand; space is reserved for continuation screens.
Determinism
overall_seed and block index drive a repeatable trial sequence for QA and simulation.
Summary metrics
The task reports accuracy, mean correct RT in milliseconds, and timeout count.
Triggering
Visible screens and trial phases emit dedicated onset triggers for QA, sim, and plot auditing.
Other Logic
Component
Description
Generated assets
Four line-art hand images are stored in assets/ and rotated at runtime.
View variants
Back and palm views are represented as separate image assets.
QA/sim support
QA and simulation modes use a phase-aware responder that can press continue keys and lateralized response keys.
Localization
Participant-facing text remains in YAML and uses SimHei for Chinese rendering.
3. Configuration Summary
All human settings are defined in config/config.yaml. QA and smoke-test overrides live in config/config_qa.yaml, config/config_scripted_sim.yaml, and config/config_sampler_sim.yaml.
a. Subject Info
Field
Meaning
subject_id
3-digit participant identifier used to seed the run.
b. Window Settings
Parameter
Value
size
[1280, 720]
units
pix
screen
0
bg_color
black
fullscreen
false
monitor_width_cm
35.5
monitor_distance_cm
60
c. Stimuli
Name
Type
Description
fixation
text
Centered fixation cross used before trials and during ITIs.
hand_back_left
image
Generated left-hand back-view stimulus.
hand_back_right
image
Generated right-hand back-view stimulus.
hand_palm_left
image
Generated left-hand palm-view stimulus.
hand_palm_right
image
Generated right-hand palm-view stimulus.
instruction_text
text
Chinese instructions for the left/right judgment rule and key mapping.
practice_break_text
text
Break screen between the practice block and formal blocks.
block_break_text
text
Summary screen between formal test blocks.
practice_feedback_correct
text
Correct-response feedback shown during practice.
practice_feedback_incorrect
text
Incorrect-response feedback shown during practice.
practice_feedback_timeout
text
Timeout feedback shown during practice.
good_bye_text
text
Final summary and exit screen.
d. Timing
Parameter
Value
task.practice_trials
18
task.test_series_count
6
task.test_trials_per_series
32
task.fixation_duration
2.0 s
task.response_deadline
8.0 s
task.practice_feedback_duration
0.8 s
task.iti_min_duration
0.5 s
task.iti_max_duration
1.0 s
e. Triggers
Event
Code
Experiment onset
1
Experiment end
2
Block onset
10
Block end
11
Fixation onset
20
Stimulus onset
30
Left response
31
Right response
32
Response timeout
33
Correct feedback onset
40
Incorrect feedback onset
41
Timeout feedback onset
42
Instruction onset
50
Practice break onset
51
Block break onset
52
Goodbye onset
53
ITI onset
60
f. Adaptive Controller
Parameter
Value
condition_generation
Custom practice-first generator in src/utils.py.
left_key
f
right_key
j
continue_key
space
overall_seed
45045
qa responder
TaskSamplerResponder with perfect-hit QA defaults.
sampler responder
TaskSamplerResponder with non-zero error and lapse rates.
4. Methods (for academic publication)
Participants completed a computer-based hand laterality judgment task. On each trial, a left or right hand image was shown in either back or palm view and at one of four orientation levels. Participants indicated whether the hand was a left hand or a right hand using dedicated keyboard responses.
The task follows a practice-first structure: an 18-trial practice block with correctness feedback is followed by six formal 32-trial blocks without feedback. Trial order is deterministic given the configured seed and includes all combinations of hand side, view, and orientation. The response deadline, inter-trial interval, and break screens are configuration-driven and documented in the reference bundle.
This implementation uses PsychoPy/PsyFlow with all participant-facing wording stored in YAML for auditability and localization portability. QA and simulation modes reuse the same state machine so the runtime can be checked without manual input before publication or deployment.
