Task dossier

Dictator Game

T000025T000025-dictator-gameDraftWeb preview availableUpdated Jun 20, 2026

Three-option allocation decisions (generous/equal/selfish) under different stake levels.

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Quick start

Clone locally, then follow the README for install and run instructions.

git clone https://github.com/TaskBeacon/T000025-dictator-game.git
cd T000025-dictator-game
# Follow the README for local setup and run steps

README

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Dictator Game

Field	Value
Name	Dictator Game
Version	v0.2.2-dev
URL / Repository	https://github.com/TaskBeacon/T000025-dictator-game
Short Description	Three-option allocation decisions (generous/equal/selfish) under different stake levels.
Created By	TaskBeacon
Date Updated	2026-02-19
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

This task implements a standard Dictator Game. On each trial, participants choose one of three allocation rules (generous, equal, selfish) for a given stake amount. Choices directly determine self and other payoffs without partner response stages.

2. Task Flow

Task Flow

Block-Level Flow

Step	Description
1. Schedule trials	`Controller.prepare_block(...)` generates balanced low/medium/high-stake trial sequences.
2. Execute trials	`BlockUnit(...).run_trial(...)` executes stake prompt, decision, and outcome stages.
3. Block summary	Allocation-type proportions and block-level earnings are shown.
4. Final summary	Cumulative self/other totals are reported at task end.

Trial-Level Flow

Step	Description
Stake prompt	Stake amount for current trial is shown.
Pre-decision fixation	Fixation before decision panel.
Allocation decision	Three-choice allocation panel is presented and response is captured.
Decision confirmation	Selected rule (or timeout fallback) is shown.
Outcome feedback	Trial and cumulative self/other amounts are shown.
Inter-trial interval	Fixation interval before next trial.

Controller Logic

Component	Description
Stake schedule	Balances low/medium/high stake conditions.
Rule mapping	Maps chosen allocation type to `self_amount` and `other_amount`.
Totals	Updates cumulative self and other payoffs.

Runtime Context Phases

Phase Label	Meaning
`stake_prompt`	Stake prompt stage.
`pre_decision_fixation`	Fixation before allocation choice.
`decision`	Three-option allocation decision stage.
`choice_feedback`	Decision confirmation stage.
`outcome_feedback`	Outcome feedback stage.
`iti`	ITI stage.

3. Configuration Summary

a. Subject Info

Field	Description
`subject_id`	Participant identifier.
`subname`	Participant name token.
`age`	Participant age.
`gender`	Participant gender.

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
`stake_prompt_text`	text	Stake prompt shown before choice.
`decision_panel`	text	Three-option allocation panel.
`decision_*`	text	Choice confirmation screens.
`outcome_feedback`	text	Trial and cumulative payoff summary.
`fixation`	text	Central fixation marker.

d. Timing

Phase	Duration
stake_prompt	0.6 s
pre_decision_fixation	0.5 s
decision	2.2 s
choice_feedback	0.5 s
outcome_feedback	1.0 s
iti	0.8 s

4. Methods (for academic publication)

Participants acted as allocators in a repeated Dictator Game. For each stake level, they selected one of three allocation rules that determined how much money they kept versus gave to the other party.

The task emphasizes unilateral social allocation behavior without reciprocal feedback. Trial-level logs include stake condition, selected rule, response timing, self payoff, other payoff, and cumulative totals.

The implementation supports human, qa, and sim modes with consistent trial flow and responder context annotations.