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thinker:giovanni-pezzulo

Giovanni Pezzulo

orcid 0000-0001-6813-8282openalex A5053290697name_hash e67591b0e8922c6f9b1257c6…
Authored
3
Introduces
0
Studies
4
Affiliations
1
Cited by
12

Authored papers (3)

  • Active Inference, Curiosity and Insight2017ⓒ 377

    Minimizing expected variational free energy under a discrete-state Markov decision process generative model is sufficient to produce curiosity, epistemic learning, and insight without any additional machinery. Friston et al. 2017 demonstrates this across two linked mechanisms: first, including posterior beliefs about likelihood parameters **A** in expected free energy G(π) introduces a novelty term—information gain about model parameters—that drives agents to sample combinations of hidden states and outcomes they have not yet encountered, resolving ignorance rather than merely ambiguity or risk. Second, Bayesian model reduction (implemented via the spm_MDP_VB_X.m routine in SPM) allows post-hoc or online pruning of redundant concentration parameters: a reduced model is accepted when ΔF ≤ −3, corresponding to a Bayes factor of approximately 20:1 in favor of the simpler model. Simulated agents learning a 3-rule, 4-factor abstract contingency task (144 hidden-state combinations, 36 possible outcomes) reach near-perfect performance after roughly 14 trials under pure epistemic learning, dropping to approximately 10 trials when online Bayesian model reduction is applied across 64 simulated agents. The sleep analog—non-REM synaptic pruning followed by REM-like belief re-evaluation—is formalized identically through the same free energy difference equation. The paper argues this implies that aha moments are necessarily subpersonal events (optimization of the generative model itself, not modeling of that optimization), that the quality of intelligence is inversely related to the thermodynamic energy expended during convergence via the Jarzynski equality, and that communicating reduced model priors rather than parameter posteriors constitutes a principled formal account of shared knowledge—consciousness in the pre-Cartesian sense of con-scire.

  • Active Inference: A Process Theory2017ⓒ 1,168

    A single variational principle—minimizing variational free energy via gradient descent on a Markov decision process (MDP) generative model—is sufficient to derive neuronal dynamics that reproduce, without hand-tuning, more than 10 well-characterized empirical phenomena simultaneously: repetition suppression, mismatch negativity, violation responses (peaking ~200 ms in peristimulus time allowing 100 ms conduction delays), place-cell activity, phase precession, theta sequences, theta-gamma coupling (at ~4 Hz theta with nested gamma), evidence accumulation with race-to-bound stepping dynamics, and transfer of dopamine responses from unconditioned to conditioned stimuli. The method introduced is an active inference process theory grounded in belief propagation over discrete-time MDP generative models, where neuronal firing rates encode categorical state expectations, membrane potentials encode their logarithms, and postsynaptic currents correspond to free-energy gradients (state prediction errors). Simulations use outcomes sampled every 250 ms, eight hidden states over four locations and two contexts, and utilities of ±3 nats for rewarding versus unrewarding outcomes (~20-fold preference ratio). Dopamine is formalized as encoding precision (inverse temperature γ) with a postsynaptic time constant of ~1 s (κ₁/κ₂ = 1/64 per 16 ms iteration). Because a gradient descent constitutes a valid description of neuronal activity, variational free energy functions as a Lyapunov function for neuronal dynamics, implying that neural activity conforms to Hamilton's principle of least action and that a single imperative—free energy minimization—unifies perception, action, learning, and neuromodulatory signaling within one coherent process theory.

  • Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs2015ⓒ 177
    candidate

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Studies (4)

Predictive CodingActive InferenceBasal CognitionBioelectricity

Affiliations (1)

Co-authors (11)

Their work is cited by (12)

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Recent mentions (9)