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Emotion Dynamics as Hierarchical Bayesian Inference in Time

Gargi Majumdar, Fahd Yazin, Arpan Banerjee, View ORCID ProfileDipanjan Roy
doi: https://doi.org/10.1101/2021.11.30.470667
Gargi Majumdar
1Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana, 122052, India
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Fahd Yazin
1Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana, 122052, India
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Arpan Banerjee
1Cognitive Brain Dynamics Lab, National Brain Research Centre, NH 8, Manesar, Gurgaon, Haryana, 122052, India
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Dipanjan Roy
2Centre for Brain Science and Applications, School of AIDE, IIT Jodhpur NH 62, Surpura Bypass Rd, Karwar, Rajasthan 342037, India
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  • ORCID record for Dipanjan Roy
  • For correspondence: droy@iitj.ac.in
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Abstract

What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in our daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modelling, we show that emotional experiences naturally arise due to ongoing uncertainty estimations in a hierarchical neural architecture. This hierarchical organization involves a number of prefrontal sub-regions, with the lateral orbitofrontal cortex having the highest representational complexity of uncertainty. Crucially, this representational complexity, was sensitive to temporal fluctuations in uncertainty and was predictive of participants’ predisposition to anxiety. Furthermore, the temporal dynamics of uncertainty revealed a distinct functional double dissociation within the OFC. Specifically, the medial OFC showed higher connectivity with the DMN, while the lateral OFC with that of the FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating individual’s beliefs swiftly in the face of fluctuating uncertainty in the lateral OFC. A biologically relevant and computationally crucial parameter in theories of brain function, we extend uncertainty to be a defining component of complex emotions.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 02, 2021.
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Emotion Dynamics as Hierarchical Bayesian Inference in Time
Gargi Majumdar, Fahd Yazin, Arpan Banerjee, Dipanjan Roy
bioRxiv 2021.11.30.470667; doi: https://doi.org/10.1101/2021.11.30.470667
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Emotion Dynamics as Hierarchical Bayesian Inference in Time
Gargi Majumdar, Fahd Yazin, Arpan Banerjee, Dipanjan Roy
bioRxiv 2021.11.30.470667; doi: https://doi.org/10.1101/2021.11.30.470667

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