Neural arbitration between social and individual learning systems
- Centre for Addiction and Mental Health, University of Toronto, Canada
- Rutgers Robert Wood Johnson Medical School, United States
- Institute for Biomedical Engineering, Switzerland
- University of Zurich, Switzerland
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Italy
Abstract
Decision making requires integrating self-gathered information with advice from others. However, the arbitration process by which one source of information is selected over the other has not been fully elucidated. In this study, we formalised arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modelling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our relative precision definition of arbitration. Functional neuroimaging demonstrated arbitration signals that were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favour of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioural and neural levels.
Data availability
Data generated during this study are available in Dryad under the doi:10.5061/dryad.wwpzgmsgs. Source data files have been provided for the main tables and figures. The routines for all analyses are available as Matlab code: https://github.com/andreeadiaconescu/arbitration. The instructions for running the code in order to reproduce the results can be found in the ReadMe file.
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Neural Arbitration between Social and Individual Learning SystemsDryad Digital Repository, 10.5061/dryad.wwpzgmsgs.
Article and author information
Author details
Andreea Oliviana Diaconescu
Funding
Swiss National Foundation (PZ00P3_167952)
- Andreea Oliviana Diaconescu
Swiss National Foundation (PP00P1_150739)
- Philippe N Tobler
Swiss National Foundation (100014_165884)
- Philippe N Tobler
Swiss National Foundation (100019_176016)
- Philippe N Tobler
Krembil Foundation
- Andreea Oliviana Diaconescu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Woo-Young Ahn, Seoul National University, Korea (South), Republic of
Ethics
Human subjects: Informed consent, and consent to publish, was obtained from all participants. The study was approved by the Ethics Committee of the Canton of Zürich (KEK-ZH 2010-0327). All participants gave written informed consent before taking part in the study.
Version history
- Received: November 28, 2019
- Accepted: August 10, 2020
- Accepted Manuscript published: August 11, 2020 (version 1)
- Version of Record published: September 7, 2020 (version 2)
Copyright
© 2020, Diaconescu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Neural arbitration between social and individual learning systems
eLife 9:e54051.
https://doi.org/10.7554/eLife.54051
Further reading
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- Computational and Systems Biology
- Medicine
Background:
Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.
Methods:
Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.
Results:
Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.
Conclusions:
The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.
Funding:
This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.
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- Computational and Systems Biology
- Genetics and Genomics
Runs of homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE, to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 SNPs and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended HLA region and autoimmune disorders. We found an association between a diplotype covering the HFE gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (P-value=1.82×10-11). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale.
