 The accuracy of the subject’s judgment \(Y_s\) about the target criterion value \(Y_e\) is measured by an achievement index, \(r_a\), which is computed by Pearson’s correlation coefficient \(\rho\) of \(Y_e\) and \(Y_s\). The subject’s predicted response \(\hat{Y}_s\) to the cues is determined by the weights \(\beta_{s_i}\) the subject assigns to each cue \(X_i\), and the linearity of the subject’s response, \(R_s\), measures the noise in the system, \(\epsilon_s\). Thus, the subject’s response is conceived to be a weighted linear sum of subject-weighted cues plus noise. The analogue of response linearity in the environment is environmental predictability, \(R_e\). The environment, on this model, is thought to be probabilistic—or “chancy” as some say. Finally, the environment-weighted sum of cues, \(\hat{Y}_e\), is compared to the subject-weighted sum of cues, \(\hat{Y}_s\), by a matching index, G.