I’ve been studying dynamics of reader memory with the mnemonic medium, running experiments on interventions, etc. A big challenge has been that I'm roughly trying to understand changes in a continuous value (depth of encoding) through discrete measurements (remembered / didn’t).
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I can approximate a continuous measure by looking at populations: “X% of users in situation Y remembered.” Compare that % for situations Y and Y’ to sorta measure an effect. This works reasonably well when many users are “just on the edge” of remembering, and poorly otherwise…
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It’s a threshold function on the underlying distribution. Imagine that a person will remember something iff their depth-of-encoding (a hidden variable)—plus some random noise (situation)—is greater than some threshold. Our population measure can distinguish A vs A’, not B vs B’.
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For a given prompt,
(i) the avg. num of trials required for the first successful recall, &
(ii) % of people who successfully recall the answer a function of this trial number,
could possibly inform expected threshold value as well as the shift in A or B with each trial (?)
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Yes, I think that’s right! I’ve had some trouble disentangling these measures from the schedule: if you struggle, you’re given more trials in a given time period, so higher trial numbers are confounded with lapses. But may be able to arrange some hold-outs to avoid this.