Process 1
A computational step in reward learning gone sticky.
Darker cells mean a higher score for this topic on that metric.
Research69
Awareness60
Complexity77
Clinical weight57
Plasticity74
Live debate50
LessMore
Last reviewed Sources & creditsMedia creditsMethodology
Quick answers
Where should I start with Addiction?
Read Overview → Anatomy → Function, then use compare rails.
Is this page advice for Addiction?
Educational atlas — not a diagnosis, treatment plan, or medical advice.
What does the local lens add?
It names everyday desks for Disorders before English norms look universal.
Are the scores a ranking of Addiction?
No — editorial 0–100 browsing aids inside this atlas only.
How do I compare Addiction to neighbours?
Use related cards and best-of / quiz rails after the first three chapters.
Which chapter is densest?
Anatomy and Function carry mechanisms; skim Overview first.
Function pages turn anatomy into computations for Addiction.
Processes summarise mainstream models, not a single dogma.
Processes
Process 2
A computational step in reward learning gone sticky.
Process 3
A computational step in reward learning gone sticky.
Process 4
A computational step in reward learning gone sticky.
Process 5
A computational step in reward learning gone sticky.
Process 6
A computational step in reward learning gone sticky.
Networks
Network 172
Network 258
Network 341
Network 485
Network 530
Network 1
Relative emphasis of network 1 in reward learning gone sticky (editorial).
Network 2
Relative emphasis of network 2 in reward learning gone sticky (editorial).
Network 3
Relative emphasis of network 3 in reward learning gone sticky (editorial).
Network 4
Relative emphasis of network 4 in reward learning gone sticky (editorial).
Network 5
Relative emphasis of network 5 in reward learning gone sticky (editorial).
Examples
Example 1
Lab or everyday example of reward learning gone sticky.
Example 2
Lab or everyday example of reward learning gone sticky.
Example 3
Lab or everyday example of reward learning gone sticky.
Example 4
Lab or everyday example of reward learning gone sticky.
Compare network percentages with the profile radar.
Sources & research
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