Cool, that makes things a little easier. Here& #39;s a quick, incomplete list, including some that are actually incorrect predictions.
Example 1: A choice can only be triggered after sampling a piece of evidence for the to-be-chosen option [sorta incorrect]
https://www.sciencedirect.com/science/article/pii/0022249688900429">https://www.sciencedirect.com/science/a... https://twitter.com/DobyRahnev/status/1379130150722228226">https://twitter.com/DobyRahne...
Example 2: Choice is determined by the balance of evidence, not support for any individual option, so the magnitude of support / total evidence for different options should not affect choice as long as balance is maintained [incorrect] https://www.nature.com/articles/s41598-019-56392-0">https://www.nature.com/articles/...
Example 3: The distribution of evidence sampled by decision makers following a diffusion strategy should not match the true distribution of evidence - it should be more extreme [correct, and my personal recent favorite] https://psyarxiv.com/h9zcv/ ">https://psyarxiv.com/h9zcv/&qu...
Example 5: This isn& #39;t totally explicit, but if you buy into the log-odds interpretation of the evidence state (some don& #39;t), then drift should be equally responsive to manipulations of sample size and sample proportion [incorrect]
https://www.sciencedirect.com/science/article/pii/001002859290013R">https://www.sciencedirect.com/science/a... https://www.sciencedirect.com/science/article/pii/S0010027716300968">https://www.sciencedirect.com/science/a...
Example 6: Evidence represented by decision makers should not be autocorrelated across time if the underlying information itself isn& #39;t autocorrelated (Markov property) [incorrect - Rachel Heath has been working on this a lot] https://journals.sagepub.com/doi/abs/10.1080/713755987">https://journals.sagepub.com/doi/abs/1...
Example 7: Absorbing choice boundaries imply that once a choice is made (where having made a choice has a monotonically increasing probability over time), subsequent evidence is ignored. [mostly correct - there& #39;s a cool recent neurofeedback paper on this] https://doi.org/10.1038/s41586-020-03181-9">https://doi.org/10.1038/s...
Of course, modifications like collapsing bounds can make it work in some scenarios, but I& #39;ve been surprised by how well the DDM has stuck around despite failed predictions (truthfully, I still use it..). Maybe part of the problem is that the alternatives are hard to work with?
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