Our main results, depicted in Figure 2, show fairly consistent patterns across datasets. In particular, both spatial and temporal features can be recovered with a linear probe, these representations smoothly increase in quality throughout the first half of the layers of the model before reaching a plateau, and the representations are more accurate with increasing model scale.

While the previous results are suggestive, none of our evidence directly shows that the model uses the features learned by the probe. To address this, we search for individual neurons with input or output weights that have high cosine similarity with the learned probe direction. That is, we search for neurons which read from or write to a direction similar to the one learned by the probe.

We find that when we project the activation datasets on to the weights of the most similar neurons, these neurons are indeed highly sensitive to the true location of entities in space or time (see Figure 5). In other words, there exist individual neurons within the model that are themselves fairly predictive feature probes. Moreover, these neurons are sensitive to all of the entity types within our datasets, providing stronger evidence for the claim these representations are unified.