hypothesis

active

hypothesis:sleep-bayesian-model-reduction-should-improve-performance-on-rule-learning-tasks-with-the-prevalence-of-insight-dependent-performance-changes-roughly-doubling-after-nocturnal-sleep

Sleep (Bayesian model reduction) should improve performance on rule-learning tasks, with the prevalence of insight-dependent performance changes roughly doubling after nocturnal sleep.

Prediction consistent with Wagner et al. (2004) finding; extended to the active inference account of sleep

Source paper

extracted_from

Active Inference, Curiosity and Insight

(2017) · Karl Friston · Marco Lin · Chris Frith · Giovanni Pezzulo +2

Neighborhood — ranked by edge-count

Findings (1)

finding

Nocturnal sleep doubled the prevalence of insight-dependent performance improvements on a stimulus-response sequence task (Wagner et al., 2004).
supports
Empirical support for sleep as facilitator of structure learning; consistent with BMR account

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Sleep implements Bayesian model reduction: non-REM sleep performs synaptic regression (complexity reduction) and REM sleep re-evaluates posteriors under the new reduced model.claim0.869
Neurobiological interpretation linking sleep stages to distinct computational roles in model optimization
Simulated agent achieves perfect performance after trial 12 following Bayesian model reduction (sleep), versus ~14 trials without BMR.finding0.829
Key result showing BMR dramatically accelerates rule learning in simulation
Structure learning via Bayesian model reduction has a clear biological interpretation in terms of synaptic decay and switching off certain synaptic connections, reminiscent of REM sleep.claim0.817
Biological interpretation of Bayesian model reduction.
Human participants in the rule-learning paradigm should acquire insight after approximately 7–8 trials, fewer than the ~14 required by Bayes-optimal inference without model reduction, suggesting they perform Bayesian model selection.hypothesis0.792
Based on informal audience experiments; implies people use prior knowledge about rule structure
Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.763
Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
When a model discovers that its outputs produce effects, it accelerates learning through in-context learning, analogous to lucid dreaming.claim0.760
Describes scaffolding method and the model's meta-learning loop.
Bayesian model expansion allows for generalisation and concept learning in active inference.claim0.752
Definition of Bayesian model expansion, Section 9.2.
Bayesian model reduction formalises post-hoc hypothesis testing to simplify the generative model.claim0.751
Definition of Bayesian model reduction, Section 9.1.