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A bias–variance trade-off governs individual differences in on-line learning in an unpredictable environment


Decisions often benefit from learned expectations about the sequential structure of the evidence. Here we show that individual differences in this learning process can reflect different implicit assumptions about sequence complexity, leading to performance trade-offs. For a task requiring decisions about dynamic evidence streams, human subjects with more flexible, history-dependent choices (low bias) had greater trial-to-trial choice variability (high variance). In contrast, subjects with more history-independent choices (high bias) were more predictable (low variance). We accounted for these behaviours using models in which assumed complexity was encoded by the size of the hypothesis space over the latent rate of change of the source of evidence. The most parsimonious model used an efficient sampling algorithm in which the range of sampled hypotheses represented an information bottleneck that gave rise to a bias–variance trade-off. This trade-off, which is well known in machine learning, may thus also have broad applicability to human decision-making.

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Fig. 1: Task.
Fig. 2: Individual differences in hazard-rate learning.
Fig. 3: Relationship between choice variability and adaptivity across subjects.
Fig. 4: Learning models.
Fig. 5: Relationships between adaptivity and choice variability from simulations using the ideal-observer, sampling and delta-rule models.
Fig. 6: Fits of the ideal-observer, sampling and delta-rule models to choice data.
Fig. 7: Effects of prior precision (φ H ) from the 20-sample model on simulated choice patterns.
Fig. 8: A model-independent measure of inference complexity.

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We thank G. Kroch and T. Kim for help with data collection and K. Krishnamurthy for comments. Funded by NSF-NCS 1533623. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript

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C.M.G., J.W.K. and J.I.G. designed the experiment; C.M.G. collected and analysed the data and implemented the models; A.L.S.F. implemented the complexity analysis; all five authors interpreted the results and drafted and/or revised the manuscript.

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Correspondence to Joshua I. Gold.

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Glaze, C.M., Filipowicz, A.L.S., Kable, J.W. et al. A bias–variance trade-off governs individual differences in on-line learning in an unpredictable environment. Nat Hum Behav 2, 213–224 (2018).

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