A simple model of recognition and recall memory
We show that several striking differences in memory performance between recognition and recall tasks are explained by an ecological bias endemic in classic memory experiments -that such experiments universally involve more stimuli than retrieval cues. We show that while it is sensible to think of recall as simply retrieving items when probed with a cue -typically the item list itself -it is better to think of recognition as retrieving cues when probed with items. To test this theory, by manipulating the number of items and cues in a memory experiment, we show a crossover effect in memory performance within subjects such that recognition performance is superior to recall performance when the number of items is greater than the number of cues and recall performance is better than recognition when the converse holds. We build a simple computational model around this theory, using sampling to approximate an ideal Bayesian observer encoding and retrieving situational co-occurrence frequencies of stimuli and retrieval cues. This model robustly reproduces a number of dissociations in recognition and recall previously used to argue for dual-process accounts of declarative memory.
This is a beautifully written paper on a central aspect of human memory research. The authors present a simple model along with a clever experimental test and simulations of several other benchmark phenomena.
My main concern is that this paper is not appropriate for NIPS. Most computer scientists at NIPS will not be familiar with, or care much about, the intricate details of recognition and recall paradigms. Moreover, the paper is necessarily compact and thus it's hard to understand even the basic descriptions of phenomena without appropriate background. I also think this paper falls outside the scope of the psych/neuro audience at NIPS, which traditionally has focused on issues at the interface with machine learning and AI. That being said, I don't think this entirely precludes publication at NIPS; I just think it belongs in a psychology journal. I think the authors make some compelling points addressing this in the response. If these points can be incorporated into the MS, then I would be satisfied.
Other comments:
The model doesn't include any encoding noise, which plays an important role in other Bayesian models like REM. The consequences of this departure are not clear to me, since the models are so different in many ways, but I thought it worth mentioning.
In Figure 2, I think the model predictions should be plotted the same way as the experimental results (d').
I didn't understand the point about associative recognition in section 5.3. I don't think a model of associative recognition was presented. And I don't see how reconsolidation is relevant here.
Minor:
- Eq. 1: M is a set, but in the summation it denotes the cardinality of the set. Correct notation would be m \in \mathcal{M} underneath the summation symbol. Same issue applies to Eq. 2.
- I'm confused about the difference between M' and M_k.
- What was k in the simulations?