Explicitly instructing learners to expect high variability in the application context broadens generalization in a predictive learning task¶

Claim · All claims
Evidence · 1 study · causal r2 · q3 · i2 medium
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i2 Experiment 1 found broader generalization in the high-expected-variability condition than in the low-expected-variability condition (η2p = 0.060 in the first generalization block). → Hadar Ram 2024

Evidence¶

Hadar Ram 2024¶

Hadar Ram, Guy Grinfeld & Nira Liberman. (2024). Anticipated variability increases generalization of predictive learning. npj Science of Learning. https://doi.org/10.1038/s41539-024-00269-z

q3 · i2 · causal · r2

Preregistered online experiment (final N = 96, random assignment) using a predictive learning task with bacteria-colony rings; 4 × 2 mixed ANOVA showed "broader generalization in the high-expected-variability condition (M = 7.03, SD = 1.90)"; Table 1 reports expected variability F(1, 94) = 5.99, p = 0.016, η2p = 0.060 (first block) and η2p = 0.044 (all blocks).

"As predicted, we found broader generalization in the high-expected-variability condition (M = 7.03, SD = 1.90) than in the low-expected-variability condition (M = 5.91,SD = 2.53; Fig.1d, Table1)."

Discussion¶

Related Claims¶

• Anticipating application of learning in the distant future broadens generalization relative to anticipating near-future application — related
• A larger expected temporal distance between learning and application increases learners' expected variability of the application context — related
• Expectations about the application context, not only actual experience at learning, shape generalization breadth — a broader claim this one bears on