question

active

question:do-human-participants-demonstrate-the-same-insight-dynamics-predicted-by-active-inference-in-the-rule-learning-paradigm-currently-under-investigation-with-eye-tracking-and-crowd-sourced-reaction-times

Do human participants demonstrate the same insight dynamics predicted by active inference in the rule-learning paradigm (currently under investigation with eye tracking and crowd-sourced reaction times)?

Empirical gap explicitly acknowledged; experiments reportedly in progress at time of writing

Source paper

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Active Inference, Curiosity and Insight

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

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Concepts (1)

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Active Inference, Curiosity and Insight (Friston et al., 2017)
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Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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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.hypothesis0.831
Based on informal audience experiments; implies people use prior knowledge about rule structure
These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.concept0.807
Connection between active inference neuronal dynamics and predictive processing theory.
Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.795
Abstract and §3, preference learning section.
Friston, FitzGerald et al. (2016) — Active inference and learningconcept0.791
Prior active inference paper providing detailed neurophysiological implementation of belief updates
Active inference offers a degree of plausibility as a process theory of brain function.claim0.790
Overall assessment from Discussion.
The key challenge for active inference is finding the generative model that best explains observable data.claim0.788
Identifies an outstanding problem, Section 10.
The natural curiosity emerging in active inference contrasts with handcrafted exploration in reinforcement learning such as epsilon-greedy or ad hoc novelty bonuses.claim0.787
§2, comparing exploration mechanisms.
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model (Li et al., 2023)concept0.782
Safety intervention that relies on activation modification, which ESR might undermine