Generative models, linguistic communication and active inference
•New (hierarchical) generative model for linguistic interactions.•Builds on active inference formulations of dyadic interactions.•We simulate agents that ask and answer questions together.•Theta-gamma coupling emerges from belief updating under this framework. This paper presents a biologically plau...
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| Published in | Neuroscience and biobehavioral reviews Vol. 118; pp. 42 - 64 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.11.2020
Pergamon Press |
| Subjects | |
| Online Access | Get full text |
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| Abstract | •New (hierarchical) generative model for linguistic interactions.•Builds on active inference formulations of dyadic interactions.•We simulate agents that ask and answer questions together.•Theta-gamma coupling emerges from belief updating under this framework.
This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the ‘Twenty Questions’ game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. |
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| AbstractList | This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure-necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. •New (hierarchical) generative model for linguistic interactions.•Builds on active inference formulations of dyadic interactions.•We simulate agents that ask and answer questions together.•Theta-gamma coupling emerges from belief updating under this framework. This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the ‘Twenty Questions’ game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. • New (hierarchical) generative model for linguistic interactions. • Builds on active inference formulations of dyadic interactions. • We simulate agents that ask and answer questions together. • Theta-gamma coupling emerges from belief updating under this framework. This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the ‘Twenty Questions’ game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure-necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another.This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure-necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. |
| Author | Parr, Thomas Sajid, Noor Yufik, Yan Price, Catherine J. Holmes, Emma Friston, Karl J. |
| Author_xml | – sequence: 1 givenname: Karl J. surname: Friston fullname: Friston, Karl J. email: k.friston@ucl.ac.uk organization: The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK – sequence: 2 givenname: Thomas surname: Parr fullname: Parr, Thomas email: thomas.parr.12@ucl.ac.uk organization: The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK – sequence: 3 givenname: Yan surname: Yufik fullname: Yufik, Yan email: imc.yufik@att.net organization: Virtual Structures Research, Inc., 12204 Saint James Rd, Potomac, MD 20854, USA – sequence: 4 givenname: Noor surname: Sajid fullname: Sajid, Noor email: noor.sajid.18@ucl.ac.uk organization: The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK – sequence: 5 givenname: Catherine J. surname: Price fullname: Price, Catherine J. email: c.j.price@ucl.ac.uk organization: The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK – sequence: 6 givenname: Emma surname: Holmes fullname: Holmes, Emma email: emma.holmes@ucl.ac.uk organization: The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32687883$$D View this record in MEDLINE/PubMed |
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| Keywords | Hierarchical Message passing Language Bayesian Inference Connectivity Neuronal Free energy |
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| Snippet | •New (hierarchical) generative model for linguistic interactions.•Builds on active inference formulations of dyadic interactions.•We simulate agents that ask... This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who... • New (hierarchical) generative model for linguistic interactions. • Builds on active inference formulations of dyadic interactions. • We simulate agents that... |
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| SubjectTerms | Auditory Perception Bayesian Communication Computer Simulation Connectivity Free energy Hierarchical Humans Inference Language Linguistics Message passing Neuronal |
| Title | Generative models, linguistic communication and active inference |
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