ArticleNeuroscience and biobehavioral reviews2018
Deep temporal models and active inference.
Article in Neuroscience and biobehavioral reviews, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Conative inference as a mechanism of allostatic resilience: wanting to be and being to want.Cognitive, affective & behavioral neuroscience · 2026Review
- Continual decision-making dynamics across biological organisms.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Against (theory-neutral) method (in consciousness science).Neuroscience of consciousness · 2026Article
- Motor and operative intentionality in computational phenomenology: from shallow to deep active inference models.Neuroscience of consciousness · 2026Article
- Computational spirits: a neuroscientific account of psychedelic entity encounters.Neuroscience of consciousness · 2026Article
- Born to act: Deferred action and desire as active inference.Cognitive, affective & behavioral neuroscience · 2025Review
- Article
- Synergizing habits and goals with variational Bayes.Nature communications · 2024Article
- Active Inference and Social Actors: Towards a Neuro-Bio-Social Theory of Brains and Bodies in Their Worlds.Kolner Zeitschrift fur Soziologie und Sozialpsychologie · 2024Article
- I like therefore I can, and I can therefore I like: the role of self-efficacy and affect in active inference of allostasis.Frontiers in neural circuits · 2024Article
- The technology, opportunities, and challenges of Synthetic Biological Intelligence.Biotechnology advances · 2023Review
- Reward Maximization Through Discrete Active Inference.Neural computation · 2023Article
- Time-consciousness in computational phenomenology: a temporal analysis of active inference.Neuroscience of consciousness · 2023Article
- A neural active inference model of perceptual-motor learning.Frontiers in computational neuroscience · 2023Article
- In the Body's Eye: The computational anatomy of interoceptive inference.PLoS computational biology · 2022Article
- Rational arbitration between statistics and rules in human sequence processing.Nature human behaviour · 2022Article
- Applying the Free Energy Principle to Complex Adaptive Systems.Entropy (Basel, Switzerland) · 2022Article
- Self, Me and I in the repertoire of spontaneously occurring altered states of Selfhood: eight neurophenomenological case study reports.Cognitive neurodynamics · 2022Article
- A step-by-step tutorial on active inference and its application to empirical data.Journal of mathematical psychology · 2022Article
- Learned uncertainty: The free energy principle in anxiety.Frontiers in psychology · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.