Dopamine Lab
Cue capture, omission dips, and reinforcement phenotypes
The dopamine module now teaches more than a single cueward shift. It frames temporal-difference learning as a way to compare blunted transfer, cue-dominant expectation, and brittle omission sensitivity without pretending this simple model is a literal disease simulator.
Teaching presets
Start from a learning phenotype, not just loose sliders
Each preset is a teaching lens for a different reinforcement story: clean transfer, cue capture, blunting, or brittle omission sensitivity.
Classical transfer
A clean teaching baseline where reward prediction error migrates from reward delivery toward the predictive cue over repeated trials.
Use this as the canonical reinforcement-learning scaffold before discussing disease, addiction, or motivational blunting.
This is a teaching baseline, not a literal patient phenotype.
Prediction error across a trial
Snapshot traces through learning
Anchor trials
Cue and reward peaks by checkpoint
Anchor trials let you compare the cue takeover directly against the omission trial dip instead of only reading one full trace.
Transfer balance
Cue takeover index
The curve crosses zero around trial 11 and ends cue-dominant, which is the clean signature of transfer.
Learning phenotype
Brittle high-expectation learning
Cue value rises quickly, but the learned expectation is fragile: once the expected reward fails to appear, the negative prediction error is disproportionately deep.
Final cue response
0.454
How strongly the predictive cue now carries positive error.
Final reward response
+0.042
Positive means reward is still surprising; small or near-zero means value has shifted upstream.
Cue / reward ratio
9.08
A fast way to see whether the system is still reward-locked or already cue-dominant.
Shift trial
11
The first trial where cue response overtakes reward response.
Transfer index
+0.412
Positive values mean the cue has inherited more of the predictive burden.
Omission dip
-0.998
How hard the system crashes when expected reward fails to appear.
Value function
Final trial expectation
Learning curve
Cue versus reward responses
Snapshot comparison
Trial-by-trial anchor cards
Novel reward
Trial 1
Cue peak
0.000
Reward peak
+1.000
Cue value
0.004
Reward value
0.200
Early transfer
Trial 12
Cue peak
+0.132
Reward peak
+0.086
Cue value
0.152
Reward value
0.931
Late transfer
Trial 24
Cue peak
+0.344
Reward peak
+0.006
Cue value
0.364
Reward value
0.995
Reward omitted
Trial 28
Cue peak
+0.397
Reward peak
-0.998
Cue value
0.411
Reward value
0.798
Well learned
Trial 36
Cue peak
+0.454
Reward peak
+0.042
Cue value
0.467
Reward value
0.966
Clinical lens
Volatile expectation
Helpful when teaching frustration sensitivity, brittle reward expectation, and the difference between strong prediction and stable control.
Behavioral readout
What a learner should notice
- Predictive cues quickly dominate the response profile.
- Omission produces a large negative dip because expectation outruns resilience.
- Behavior would likely feel highly expectation-bound and abruptly disrupted by reward failure.
Differential traps
What this model should not make you overclaim
- A large omission dip does not necessarily mean the model is healthier; it can mean the expectation is brittle.
- Fast learning is not the same thing as stable learning.
Next questions
Useful follow-up experiments
- Does lowering learning rate or increasing trace stability soften the omission penalty?
- How much of the volatility is driven by discounting versus the learning rate itself?
Model notes
Four reminders for students
- Unexpected reward produces a strong positive prediction error when the model has not yet assigned value to the cue.
- With learning, value back-propagates toward the predictive cue, so positive error shifts earlier in time.
- Once expectation is established, omitted reward generates a negative error around the expected reward time.
- This model is deliberately explanatory rather than biologically exhaustive: it separates learning transfer, cue capture, and omission sensitivity without claiming to be a literal disease simulator.
Continue the loop
Use this with anatomy, plasticity, and tutoring
Brain Atlas
Post-clinical anatomical convergence
Basal Ganglia Loop Explorer
Movement-disorders circuitry
Synaptic Plasticity
Mechanistic learning theory
Neuro Tutor
Cross-module consult reasoning with explicit scoring