Passive Lottery Task

Field	Value
Name	Passive Lottery Task
Version	v0.2.3-dev
URL / Repository	https://github.com/TaskBeacon/T000022-passive-lottery
Short Description	Passive observation task for gain/loss/mixed lottery outcomes with deterministic profile-driven sampling.
Created By	TaskBeacon
Date Updated	2026-02-24
PsyFlow Version	0.1.9
PsychoPy Version	2025.1.1
Modality	Behavior
Language	Chinese
Voice Name	zh-CN-YunyangNeural (voice disabled by default)

1. Task Overview

In this task, participants passively observe lottery cues, offer displays, and outcome feedback without making trial-level responses. The paradigm includes gain, loss, and mixed lotteries and is suitable for studying expectancy and outcome processing without action-selection confounds.

2. Task Flow

Block-Level Flow

Step	Description
1. Prepare block schedule	Custom condition generation builds balanced, randomized trial sequences and pre-sampled outcomes.
2. Execute trials	BlockUnit(...).run_trial(...) executes each planned trial.
3. Block summary	Block hit rate, block score, and cumulative score are displayed.

Trial-Level Flow

Step	Description
Condition cue	Condition cue (gain/loss/mixed) is displayed.
Pre-lottery fixation	Central fixation stage.
Lottery reveal	Trial-specific probability and outcomes are shown.
Outcome feedback	Realized outcome and cumulative score are shown.
Inter-trial interval	Fixed fixation interval before next trial.

Controller Logic

Component	Description
Architecture note	No task controller object is used; condition_generation defines lottery profiles/sampling and a ScoreTracker accumulates score.
Condition balancing	Gain/loss/mixed conditions are balanced across trials.
Outcome sampling	Trial outcome is sampled from profile probabilities.
Score accumulation	Trial deltas and cumulative score are tracked.

Runtime Context Phases

Phase Label	Meaning
condition_cue	Condition cue stage.
pre_lottery_fixation	Pre-offer fixation stage.
lottery_reveal	Lottery information display stage.
outcome_feedback	Outcome feedback stage.
iti	ITI stage.

3. Configuration Summary

a. Subject Info

Field	Meaning
subject_id	Participant identifier.

b. Window Settings

Parameter	Value
size	[1280, 720]
units	pix
screen	0
bg_color	gray
fullscreen	false
monitor_width_cm	35.5
monitor_distance_cm	60

c. Stimuli

Name	Type	Description
condition_cue	text	Condition-specific cue label from condition_generation.lottery_profiles.
lottery_offer	text	Lottery probability-outcome display with two possible outcomes.
outcome_win / outcome_neutral / outcome_loss	text	Valence-specific outcome feedback screens with cumulative score.
fixation	text	Central fixation marker.
block_break / good_bye	text	Block and final summary pages.

d. Timing

Phase	Duration
condition_cue	0.6 s
pre_lottery_fixation	1.2 s
lottery_reveal	1.5 s
outcome_feedback	1.0 s
iti	0.8 s

e. Triggers

Group	Trigger Codes
Experiment	exp_onset=1, exp_end=2
Block	block_onset=10, block_end=11
Condition cue by condition	gain/loss/mixed: 20/21/22
Pre-lottery fixation by condition	gain/loss/mixed: 30/31/32
Lottery reveal by condition	gain/loss/mixed: 40/41/42
Outcome feedback by condition x valence	50-58
Inter-trial interval	iti_onset=60

f. Lottery Profiles (condition_generation)

Profile	Probability of Outcome A	Outcome A	Outcome B
gain	0.75	+10	0
loss	0.75	-10	0
mixed	0.50	+10	-10

4. Methods (for academic publication)

Participants completed a passive lottery-viewing task in which each trial presented condition cue, fixation, lottery information, and outcome feedback. No decision response was required, allowing isolation of expectancy and outcome-related processing.

Lottery profiles covered gain, loss, and mixed valence conditions with distinct probability-outcome mappings. Trial logs included condition, realized outcome type, outcome value, and cumulative score, supporting behavioral and neurocognitive analyses of value and valence processing.

The task supports aligned human, qa, and sim execution with standardized trial context instrumentation.