Claims

Under the Leitner Queue Network model, raising the rate of new-item introduction beyond a threshold causes a phase transition in learning rate, which a Mechanical Turk vocabulary experiment reproduced.

Claim · All claims
Evidence · 1 study (2 entries) · causal r? · q1–q2
Currency · newest study 2016 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q1 i? Under the mean-recall approximation, when the arrival rate exceeds the threshold the lowest Leitner deck accumulates items and delays blow up, so the learning rate goes to 0. → Reddy 2016 q2 i? Empirical learning rates from the MTurk arrival-rate conditions closely agreed with the simulated Leitner Queue Network curve and showed the predicted phase transition. → Reddy 2016 (2)

Evidence

Reddy 2016

Reddy, S., Labutov, I., Banerjee, S., & Joachims, T. (2016). Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. KDD ’16, San Francisco, CA, USA. https://doi.org/10.1145/2939672.2939850

q1 · i? · theoretical · r3

Analytical result derived under the mean-recall approximation of the Leitner Queue Network. The article derives that above threshold the lowest deck "experiences packet accumulation and delay blow-up" and the learning rate goes to 0.

"Moreover, if λext > λt, then the lowest Leitner deck (i.e., Q1) experiences packet accumulation and delay blow-up, and thus the learning rate λout goes to 0."

Reddy 2016 (2)

Reddy, S., Labutov, I., Banerjee, S., & Joachims, T. (2016). Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. KDD ’16, San Francisco, CA, USA. https://doi.org/10.1145/2939672.2939850

q2 · i? · causal · r?

Mechanical Turk vocabulary-learning experiment (Japanese words or American Sign Language gestures) with participants assigned to arrival-rate conditions. "The simulated and empirical curves are in close agreement"; the data show the predicted phase transition (Fig. 14). No test statistic is printed.

"The simulated and empirical curves are in close agreement; in particular, the MTurk data shows the phase transition in learning rate predicted by our theoretical model."

Studies bearing on this claim

In a flashcard or review system, how many new items should enter and how should reviews be shared out? This table gathers the studies behind this claim and the 7 narrower claims that bear on it: 1 distinct studies (1 theoretical), q1–q2, 0 of 1 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Study Design Effect as recorded Bears on this claim through
Reddy 2016 q1 / q2 · theoretical · r3 i? this claim; In a Mechanical Turk flashcard experiment, raising the new-item arrival rate first increas (supports); Under the mean-recall approximation, the optimal Leitner Queue Network review schedule spe (qualifies); Under the mean-recall approximation, the optimal Leitner Queue Network schedule increases (qualifies); Under the mean-recall approximation, the maximum achievable learning rate is convex in the (qualifies); Under the Leitner Queue Network optimization, easy items call for roughly uniform time acr (qualifies); In Mnemosyne log data, setting memory strength equal to an item's Leitner deck position pr (qualifies); In Mnemosyne log data, item-specific difficulty parameters outperform a global difficulty (qualifies)

Discussion

Page metadata
Field Value
type claim
id leitner-queue-network-phase-transition-in-learning-rate
title Under the Leitner Queue Network model, raising the rate of new-item introduction beyond a threshold causes a phase transition in learning rate, which a Mechanical Turk vocabulary experiment reproduced.
description Under the Leitner Queue Network model, raising the rate of new-item introduction beyond a threshold causes a phase transition in learning rate, which a Mechanical Turk vocabulary experiment reproduced.
status review
evidence_strength moderate
newest_evidence 2016
source_checked 2026-10-20
generated by claude/unspecified on 2026-09-24
trust unverified — no one has confirmed this page's content
sources 2 — reddy-2016, reddy-2016-2

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