Interactive Two-Stage Decision Tree
Model-Based (Goal)
Model-Free (Habit)
Manual Choice (Stage 1):
Reinforcement Learning Parameters
0.70
Elevated ADHD traits increase cognitive effort cost, shifting balance away from deliberate model-based search.
0.80
Simulates working memory burden during dual-task conditions.
0.40
Model-Based Weight (ω)
0.28
Model-Free Weight (1-ω)
0.72
Effort Cost
4.12
Total Trials Completed
0
Cognitive Science Mechanics
In the Two-Stage Task, model-free learning repeats actions leading to reward regardless of transition probability. Model-based learning understands state transitions: a rare transition reward prompts switching choice on the next trial to target the state that usually delivers rewards.
"Elevated ADHD traits disrupt implicit statistical habit structures when deliberate control is exhausted."
Stay Probability & Behavioral Strategy Breakdown
Comparing common vs. rare transition outcomes to differentiate model-based planning from model-free habitual reinforcement.
Common Reward / Unreward
Rare Reward / Unreward
Common Reward Stay %
0.68
Common Unreward Stay %
0.32
Rare Reward Stay %
0.64
Rare Unreward Stay %
0.25
Current Execution Profile:
Initialized lab with ADHD Score 0.70 & Cognitive Load 0.80. Model-Based Weight ω = 0.28, Cognitive Effort Cost = 4.12. Ready for continuous trials or cohort run.