FIGURE 6.

Stimuli which are adaptively optimized for model comparison can lead to more accurate model selection. Adapted with permission from DiMattina and Zhang (2011). (A) A hypothetical two phase procedure for estimating and comparing multiple competing models. During the estimation (E) phase, stimuli are optimized in turn for estimating each model. During the comparison (C) phase, stimuli are optimized for comparing all of the models. (B) Two candidate models were fit to data generated by a true additive model whose input weights (w₁ and w₂) were 12 × 12 Gabor patches shown at the right. The two competing models differ only in their method of integrating subunit activities (additive versus multiplicative). (C) At the end of the estimation phase (“Start”), the BIC does not consistently prefer either model. Presenting additional stimuli optimized for model discrimination yields almost perfect model selection (red curve), while presenting additional random stimuli (green curve), or stimuli optimized for model estimation (blue curve) either does not improve or only somewhat improves model selection.