The images made up six sequences, which were learned right before scanning, then shown three times in each of the five scanning runs. As shown here, different objects were used in the Fixed, X, Y, and Random sequences; two objects were shared between the X sequences, and three between the Y sequences. Each sequence had the images shown in the order in the figure, except for the Random sequence, which was randomly different each time (the camel could be first one time, then third the next).
This set of sequences made it possible to look for order and identity effects: once you saw the rake, you would know (since the participants memorized the sequences before scanning) that you would see the truck next, followed by the cabinet, etc. If you saw the rhino you would know the drill and strawberry would be next, but not whether the chair or elk would be in the fourth position. Seeing the camel first would have you expect the tractor, shears, stand, or pineapple to be next, but not which one (though by the fourth object you'd know which of the Random set hadn't yet been shown).
The presented results are all ROI-based, with the hippocampus, parahippocampal cortex (PHc), and perirhinal cortex (PRc). The ROIs were individually drawn for each person, but I didn't see a list of how many voxels went into the ROIs for each person, or a mention of how much variability there was in the size across people and ROIs. If they kept the voxels at the acquired 3.2×3.2×3.0 mm, I'd guess there'd be less than 20 voxels in each ROI, but it would be nice to have had the exact counts. (And I wonder if they looked outside the ROIs; seems likely, since they acquired whole-brain images.)
Anyway, they created parameter estimate images (fit a canonical HRF) for each image presentation (90 per run), then created an RSA matrix (with Pearson correlation) for each same-sequence repetition within a run, then averaged those three matrices to get one matrix per sequence per run, then averaged across runs, then across people (Figure 3).
The clever bit is how they made the RSA matrices for the Random sequences: based on position or object (Figure 3). For position, they did the RSA with the true sequences: correlating the first-presented image against the first, even though they were different images. There's very little correlation in the upper left corner of this matrix, but more in the lower right - perhaps because the last few images could be guessed. Then, they did the RSA based on object: correlating the same images together (camel to camel), regardless of order. They used these three RSA matrices to test their hypotheses (Figure 8): which ROIs had information about object identity? Which about the order? Which had both?
Hsieh, L., Gruber, M., Jenkins, L., & Ranganath, C. (2014). Hippocampal Activity Patterns Carry Information about Objects in Temporal Context Neuron, 81 (5), 1165-1178 DOI: 10.1016/j.neuron.2014.01.015
Thanks for your thoughtful review of our study! We are, indeed looking at regions outside of the MTL in a follow-up paper (as you can imagine, it is a huge dataset). So far, we have not seen any cortical area that looks quite like the hippocampus. Surprisingly, many areas (including most of PFC) shows RS for any trial that has the same ordinal position in a sequence, including the random sequence. Of course ventral stream areas show object coding, and a couple of areas, like Angular gyrus, show both object and ordinal position coding.
ReplyDeleteThe data are, as you say, noisy. Pattern correlations in hippocampus are low, even across repetitions of same object in same sequence, and they benefit from averaging across repetitions within runs.
I don't know what to make of negative correlations in RSA. To me, the Pearson correlation is used here only as a similarity metric, like Euclidean Distance or mutual information. So, I would see a negative correlation as signifying less similarity between the patterns than a zero correlation, rather than interpreting it as a systematic relationship as you might interpret correlations in other contexts. Of course, it would be weird to see a very large negative correlation. However, we have not yet seen any meaningful negative pattern correlations in the datasets that we have looked at so far, so I'm not so worried about that.