- Most importantly, the explanations are very qualitative and whenever simulation or experiment-based evidence is given, the procedures are described very minimally or not at all, and some figures are confusing, e.g. what is "sample count" in fig. 2? It would really help adding more details to the paper and/or supplementary information in order to appreciate what exactly was done in each simulation. Whenever statistical inferences are made, there should be error bars and/or p-values.
- Although in principle the argument that in case of recognition lists are recalled based on items makes sense, in the most common case of recognition, old vs new judgments, new items comprise the list of all items available in memory (minus the ones seen), and it's hard to see how such an exhaustive list could be effectively implemented and concrete predictions tested with simulations.
- Model implementation should be better justified: for example, the stopping rule with n consecutive identical samples seems a bit arbitrary (at least it's hard to imagine neural/behavioral parallels for that) and sensitivity with regard to n is not discussed.
- Finally it's unclear how perceptual modifications apply for the case of recall: in my understanding the items are freely recalled from memory and hence can't be perceptually modified. Also what are speeded/unspeeded conditions?
- Although in principle the argument that in case of recognition lists are recalled based on items makes sense, in the most common case of recognition, old vs new judgments, new items comprise the list of all items available in memory (minus the ones seen), and it's hard to see how such an exhaustive list could be effectively implemented and concrete predictions tested with simulations.