Invariant recognition drives neural representations of action sequences
Fig 7
Representational Similarity Analysis between model representations and human neural data.
We computed the Spearman Correlation Coefficient (SCC) between the lower triangular portion of the dissimilarity matrix constructed with each of the artificial models we considered and the dissimilarity matrix constructed with neural data (shown and described in Fig 6). We assessed the uncertainty of this measure by resampling the rows and columns of the matrices we constructed. In order to give the SCC score a meaningful interpretation we reported here a normalized score: the SCC is normalized so that the noise ceiling is 1 and the noise floor is 0. The noise ceiling was assessed by computing the SCC between each individual human subjects’ dissimilarity matrix and the average dissimilarity matrix over the rest of the subjects. The noise floor was computed by assessing the SCC between the lower portion of the dissimilarity matrix constructed using each of the model representation and a scrambled version of the neural dissimilarity matrix. This normalization embeds the intuition that we cannot expect artificial representations to match human data better than an individual human subject’s data matches the mean of other humans and that we should only be concerned care with how much better the models we considered are, on this scale, than a random guess. Models with learned templates agree with the neural data significantly better than models with fixed templates. Among these, models with Structured Pooling outperform both purely Convolutional and Unstructured models. Horizontal lines at the top indicate significant difference between two conditions (p < 0.05) based on group ANOVA or Bonferroni corrected paired t-test (see Materials and Methods).