README.md

metadata

title: Centauri Cognitive Simulator
emoji: 🧠
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 6.22.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Small cognition models predict your choices, trial by trial
python_version: '3.12'
startup_duration_timeout: 1h
models:
  - socius/Qwentaur-8B-LoRA-r16
  - socius/Qwentaur-0.6B-LoRA-r16
datasets:
  - marcelbinz/Psych-101

🧠 Centauri Cognitive Simulator

Play a real psychology experiment while three models predict, trial by trial, what a human would do next:

predictor	what it is
Qwentaur-8B	socius/Qwentaur-8B-LoRA-r16 — LoRA r=16 on unsloth/Qwen3-8B-Base
Qwentaur-0.6B	socius/Qwentaur-0.6B-LoRA-r16 — 13× smaller
Qwen3-8B-Base	the same 8B weights with the adapter switched off (no fine-tuning)

From Small Foundation Models of Human Cognition and Behaviour (Oh & Gobet, 2026) — paper · code. The paper's headline claim is that sub-billion-parameter models already match a 70B Centaur at fitting human choices in-distribution; the side-by-side bars and the running loss table let you watch that.

How it works

Everything the models see is plain Psych-101 text, exactly as during fine-tuning: human choices are wrapped in <<...>>, and the choice distribution is read straight off the next-token logits after You press <<, renormalised over the available option letters. No sampling, no prompt engineering, no chat template.

Four experiments are implemented with the dataset's verbatim instruction wording:

• Horizon task (Wilson et al., 2014) — explore or exploit two slot machines
• Iowa Gambling Task (Steingroever et al., 2015) — learning under risk
• Intertemporal choice (Ruggeri et al., 2022) — patience
• Multi-attribute inference (Hilbig & Moshagen, 2014) — cue integration

The second tab scores the models the way the paper does — mean negative log-likelihood of a real participant's actual choices — on human sessions bundled verbatim from Psych-101 (Binz et al., 2025, Apache-2.0).

Credits

• Adapters: socius/* (Apache-2.0), Oh & Gobet, Small Foundation Models of Human Cognition and Behaviour, 2026.
• Base models: unsloth/Qwen3-8B-Base, unsloth/Qwen3-0.6B-Base.
• Task wording and bundled human transcripts: Psych-101 (Binz et al., 2025, Apache-2.0).