Update beliefs explicitly when new information arrives

Treat new information as a reason to state a revised probability, not as confirmation of the old one.

Why it works

Motivated reasoning causes people to accept confirming evidence uncritically and scrutinize disconfirming evidence heavily. Explicitly updating a stated probability when new information arrives counteracts this: it forces engagement with whether the new information actually moves the probability and by how much, rather than allowing the old position to persist through selective attention.

How to do it

1. When new information arrives, ask: does this change my probability estimate? By how much?
2. State the updated probability explicitly, not just the direction of change.
3. Distinguish between new information (genuine update trigger) and noise (random fluctuation without evidential weight).
4. Track how often new information actually moved your estimate — this calibrates your updating sensitivity.

Evidence

Bayesian updating is the normative model for belief revision under uncertainty. Research on belief perseverance and confirmation bias shows people update less than the evidence warrants; interventions that force explicit probability statements improve updating. (mechanistic)

Bayesian updating is prescriptive; human updating is imperfect even with training, and overconfident updating (moving too much on weak evidence) is also a documented error.

Sources

• Lord, Ross & Lepper (1979), belief perseverance after evidential challenge, Journal of Personality and Social Psychology

Common mistake

Saying "that changes things" without specifying how your probability changed — vague acknowledgment of new information is not an update.