New essay: prioritizing falsifiability in AI cognitive modeling can be reward-hacked
AndrewLampinen · x · 2026-10-16
Researcher Dhara Yu's new essay examines a methodological trap in AI-driven computational cognitive modeling:
- Privileging easily-digestible signals of falsifiability structurally incentivizes highly stylized tasks and simple dependent variables, because they make it easy to appraise whether a model's simulated outputs match human data.
- The core claim: falsifiability itself can be reward-hacked. Treated as an essential box to check, it comes at the cost of other desiderata like studying behavior in naturalistic settings.
- In more complex, naturalistic domains, "absolute" falsification is commensurately harder to achieve.
The essay was boosted by Google DeepMind scientist Andrew Lampinen.
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