A larger expected temporal distance between learning and application increases learners' expected variability of the application context¶

Claim · All claims
Evidence · 1 study · causal r2 · q3 · i3 large
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i3 Participants who expected to apply learning a year later, versus minutes later, expected the application batch to differ more from the learning batch (d = 2.32). → 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 · i3 · causal · r2

Preregistered online experiment (final N = 91, random assignment) with vignettes manipulating whether a second bacteria batch was collected minutes or a year later; the distal condition expected more difference. A paired-sample test also favored the "more likely" over "less likely" statement, t(90) = 8.59, p < 0.001, d = 3.45.

"An independent-samples t-test revealed that participants in the distal condition rated the likelihood that the second batch would be different as higher (M =6 . 6 1 ,SD = 2.61) than participants in the proximal condition (M = 3.88, SD = 1.95),t(89) = 5.57, p < 0.001,d = 2.32."

Discussion¶

Related Claims¶

• Explicitly instructing learners to expect high variability in the application context broadens generalization in a predictive learning task — related
• Anticipating application of learning in the distant future broadens generalization relative to anticipating near-future application — related
• When exemplars are already interleaved, larger temporal spacing between same-category exemplars yields better inductive learning than smaller spacing — related