Model generalization across input discrepancies and input gains.

<p>After training, the model was tested on data that differ from its training distribution. (A) Discrepant-input data: prop input (orange) is shifted by progressively greater increments of the input covariance (see text), leading to suboptimal integration, as expected, and structured error distributions. The hidden-layer error mean, like the optimal error mean, shifts rightward with the prop “bias.” (B) Gain-modulated data: The training data had gains between 12 and 18. Testing on gains (ratios listed between panels (B) and (C)) outside this regime yields suboptimal error covariances but essentially zero biases. (C) Gain-modulated, input-discrepant data: As the relative reliability of PROP is increased, the optimal estimate shifts toward PROP and away from VIS. The green and black dotted lines, nearly identical, trace this movement for the machine-based and optimal estimates, resp. For larger discrepancies (not shown), this optimal behavior breaks down, the green and black lines diverging.