Improving the validity of neuroimaging decoding tests of invariant and configural neural representation
Fig 9
Description and results of simulation 2.
A: Encoding model for simulation 2. See main text for details. Panels B-E show the decoding results from simulation 2. B: Classifier accuracy scores for model-generated data from both levels of the context dimension. The y-axis represents accuracy scores, the x-axis represents magnitude of noise added to measurement weights for the second level model, the dotted line is chance performance. Proportion of positive tests of each type. The y-axis represents proportion of positives, the x-axis represents measurement noise, the dotted line represents the accepted false discovery rate of 5%. Panels C-D show the proportion of each type of conclusion in Table 1 (specificity/sensitivity in red, invariance/tolerance in blue, and no conclusion in green) reached from jointly testing against specificity and invariance. In both cases, the cross-classification test is used against specificity. D: Conclusions reached by using the classification accuracy invariance test against invariance. E: Conclusions reached by using the decoding separability test against invariance.