Fig. 4.

Model prediction errors for one subject (A and B, same subject as in Fig. 3) and averaged across eight subjects (C and D), in the difficult (A and C) and the easy (B and D) versions of the contrast decrement detection task. The prediction error is the r.m.s. distance between observed and predicted data in the divided attention conditions (i.e., set sizes 2, 3, and 4). This error is computed for each possible value of the model parameter (sampling period for the sample-always and the sample-when-divided models, lower horizontal axis; division cost for the “parallel” model, upper horizontal axis). The mean prediction error (±SEM, shaded area) across set sizes is plotted for each model. The optimal model is the one yielding the lowest prediction error (horizontal arrows on the left vertical axis), which in A is the sample-always model and in B is the parallel model. The optimal parameter values for each model are indicated by the vertical arrows. (C) Average prediction errors across eight subjects in the difficult version of the task. The shaded area represents SEM across subjects. The sample-always model was the optimal for all subjects tested, with an optimal “sampling period” of 140 ms on average (range 100–190 ms). (D) Average prediction errors across eight subjects in the easy version of the task. The optimal model in this case is the parallel model, with an optimal “division cost” of 17% on average (range 9–25%). (E and F) Prediction error of each model at its optimal parameter value (average across eight subjects). (E) For a difficult contrast decrement detection task, the model that best reflected human performance was a sampling model, in which periodic sampling occurred even during undivided attention (sample-always model). (F) For an easier version of the same task, the parallel strategy yielded the lowest prediction error. The classic idea of a switching spotlight (sample-when-divided model) did not closely reflect our observers' strategy in either situation.