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ΔV=α (λ−V)\Delta V = \alpha \,(\lambda - V)

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The evidence assembled here fits inside a formulation more modest than “an evolved fear module” and, for that reason, more resilient to the objections just described. Treat ordinary associative learning as a single general mechanism, and treat what evolution supplies not as a separate circuit but as a prior on that mechanism’s own parameters. A standard error-correction rule for associative strength states the update on each learning trial as ΔV=α (λ−V)\Delta V = \alpha \,(\lambda - V). where V is the current associative strength between a stimulus and an outcome, λ\lambda is the asymptotic strength that outcome supports, and α\alpha is a learning-rate parameter governing how much of the gap between the two is closed…

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ΔV=α (λ−V)\Delta V = \alpha \,(\lambda - V)

Equation 1 · Evolutionary Psychology

Prepared Fears: How Evolution Biased What the Mind Learns to Dread

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

The evidence assembled here fits inside a formulation more modest than “an evolved fear module” and, for that reason, more resilient to the objections just described. Treat ordinary associative learning as a single general mechanism, and treat what evolution supplies not as a separate circuit but as a prior on that mechanism’s own parameters. A standard error-correction rule for associative strength states the update on each learning trial as ΔV=α (λ−V)\Delta V = \alpha \,(\lambda - V). where V is the current associative strength between a stimulus and an outcome, λ\lambda is the asymptotic strength that outcome supports, and α\alpha is a learning-rate parameter governing how much of the gap between the two is closed…

Meanings in this article

  • α\alpha: a learning-rate parameter governing how much of the gap between the two is closed on a given trial.
  • λ\lambda: the asymptotic strength that outcome supports.
  • VV: the current associative strength between a stimulus and an outcome.
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