For some context, Tryon's maze was a massive 17-unit T-maze that took up an entire room.

It was entirely automated, with attached home cages that would rotate to train new rats sequentially, and electronic sensors in the T-maze alleyways to record errors.

Not bad for 1927.

#HistoryOfPsychology

Check out the paper here: doi.org/10.1016/j.bb...

If interested, also check out Mou and Ji, (2016): dx.doi.org/10.7554/eLif... where rats exhibited odd spinning behaviours while watching other rats run a linear track and their place cells activated in sequences corresponding to the track.

These results suggest that the hippocampus may underlie understanding the behavior and intentions of others, allowing animals to capitalize on observed experiences.

If this effect is due to emulation, it would also be consistent with findings that the hippcampus represents task structure and rules.

However, they did find that (dorsal) hippocampus is in involved in this observation > performance enhancement, whatever the underlying mechanism.

Fuentealba et al. (2025) argue this resulted from emulation (performing a behaviour required to reach the same goal) rather than imitation (copying the exact same behaviour) or local enhancement (going to the same places as the demonstrator).

I think these are very difficult to tease apart though.

Fuentealba et al. (2025) developed a dry land watermaze task where one rat could observe a conspecific solving the task.

Observing a pretrained rat (expert), led to significantly better performance than observing a naive rat (non-expert), suggesting that rats can learn through observation.

There is good evidence for social coding in the rodent hippocampus: cells representing the location of another rat (Danjo et al. 2018): doi.org/10.1126/scie...

Cells 'preplaying' a linear track, after observing another rat solving the task (Mou and Ji, 2016): dx.doi.org/10.7554/eLif...

If interested, also check out related experimental papers characterising this phenomenon from Kay et al. (2020) in the Frank lab:
www.cell.com/cell/fulltex...

And Vollan et al. (2025) in the Moser lab:
www.nature.com/articles/s41...

Ooops, I guess copy pasting BlueSky posts doesn't capture the full URL, it is here:

doi.org/10.1016/j.ap...

Fraser, D. (2025). Two paradigms of research and their influence on the study of animal behaviour. Applied Animal Behaviour Science, 106550.

Tinbergen's four questions I think stand out historically: doi.org/10.1016/j.tr...

Recently, I also enjoyed this nice discussion and comparison of the 'natural history' and 'natural philosophy' approaches to the study of animal behaviour: www.sciencedirect.com/science/arti...

Also check out these fantastic papers on similar topics if interested:

Tanni et al. 2022
'perceptual change model' www.cell.com/current-biol...

And Harland et al. 2021
'megaspace'
doi.org/10.1016/j.cu...

Interesting paper/model looking at interesting problems.

But frustrating that the most direct comparison to flying bats (climbing rats) was overlooked.

See our 3D place cells in rats here:
www.nature.com/articles/s41...
And 3D grid cells here:
www.nature.com/articles/s41...

Also not sure why largest fields were excluded 👇 field size & local maxima are key to the model

Rate maps in Yartsev & Ulanovsky were quite highly smoothed (adaptive & kernel smoothing) not sure how/if this was accounted for?

Small fields will adopt the shape of a smoothing kernel more readily...

Or field elongation along boundaries, something repeatedly shown in rodents in 2D & 3D:
doi.org/10.1038/s414...

Could probably be achived by swapping spatial inputs to the model for 3D boundary cell inputs, but then we would have a BVC model...
www.degruyterbrill.com/document/doi...

However, Mainali et al.'s model doesn't capture the finding that field size scales with proximity to boundaries:
doi.org/10.1016/j.cu...
Tanni et al. - also not cited

When we recorded place cells in climbing rats we found that cells exhibited fewer & larger place fields. Space was underrepresented with fewer fields per m3.

These results are consistent with 2D rodent results in large spaces and Mainali et al.'s model.

Changing the model thresholds alters the size of the resulting place fields, so this could explain larger fields in larger environments.

Not sure how/why these thresholds would change in the brain? They would need to scale with environment size, the mechanism for that remains unexplained...