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Furthermore, the paper highlights that any model that integrates both choice and context features for reasoning may leverage the shortcut solution present in the original RAVEN dataset. Context-blind trained models $\{$ResNet, CoPINet$\}$ (as reported in ~\cite{hu2020stratified}) and DCNet (our trained) models attain $71.9 \%$, $94.2 \%$ and $94.1 \%$ test accuracy respectively. We conducted similar tests on SARN in a context-blind setting, where we removed the reasoning module and used the extracted attended choice vectors extracted from the visual transformer, passed through an MLP, to output an eight-dimensional logit vector. Our model's performance remained at 12.2\% even after 100 epochs of training, which is similar to the random guess of 1/8 or 12.5\%. This suggests that the Backbone with the Visual Transformer did not contribute to finding a shortcut in solving RPM questions. Therefore, our semantic tokenized representation combined with the reasoning module learned rules from the context to solve RPM questions.