This research is about how sequences of stimuli can be recognized and remembered. It is often confused with learning of behavior, e.g. motor sequence learning (which of course is important in its own right). However, it's hard to reply seriously to an account called Alfred Nobel, are you a troll?

"sequence-struggle hypothesis" 😅

2) And unfortunately they didn't test why performance was low, 3) and if systematic variation adhered to the model proposed in our first sequence paper.
royalsocietypublishing.org/doi/epdf/10....
At the same time, the Reindl et al. paper was creative and ambitious, looking forward to more tests! 8/8

For this reason, we wrote this comment, published yesterday. royalsocietypublishing.org/doi/10.1098/...
We reason that 1) their data supports rather than rejects the sequence hypothesis, as monkeys and chimps did not perform with any precision in these sequential tasks. 7/n

No squirrel monkeys (n=23), no capuchins (n=24) entered the test phase, task too difficult. 10 chimps failed, 3 completed tests of 300 trials, no chimp learned the task. One chimp reached criterion after 324 trials. Humans did well (humans perform well on sequential tasks, that's the point). 6/n

As suggested by the title, they doubt the generality of this said hypothesis "Humans may not havea uniquely enhancedsequence memory: sequence discrimination is facilitated by causal–logical framing inhumans and chimpanzees". But, what do their results look like?? 5/n

Few tests have been made of this hypothesis (that only humans can in general represent sequential information faithfully), and then a creative new study came out, with impressive sample sizes:
royalsocietypublishing.org/doi/10.1098/... 4/n

We presented this, and related ideas, in this book from 2023.
press.princeton.edu/books/paperb...

We also presented a model that successfully accounts for how non-human animals represent sequential information:
royalsocietypublishing.org/doi/10.1098/...

Importantly, this model better explains sequence discrimination experiments than do ideas from rule learning, artificial grammar, etc.

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