Research map / Probabilistic and scientific modeling / Dynamical systems
Cognitive Model Discovery via Disentangled RNNs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase GRU hidden units from 2-3 to 14 (ablation) | GRU on rat bandit data · Rat bandit dataset | normalized likelihood diff (pct points): 0.34 -> 0.50 | Helped |
| increase GRU hidden units beyond 2 (ablation) | GRU on synthetic Q-Learning / Actor-Critic data · Synthetic bandit datasets | normalized likelihood diff (pct points): 0.002 (Q-Learning), 0.02 (Actor-Critic) | No clear effect |
| use very high beta (stronger bottleneck penalty) (ablation) | DisRNN · Synthetic datasets | cross-validated fit | Hurt |
| use medium beta (ablation) | DisRNN · Synthetic datasets | recovered model structure | Helped |
| use very low beta (ablation) | DisRNN · Synthetic datasets | model simplicity vs fit | Mixed |
| replace DisRNN with two-unit GRU (ablation) | disentangled recovery of latent dynamics · Actor-Critic and Bounded Accumulator synthetic data | recovery of true update rules | Hurt |
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