Evidence map›Paper›PMID 33705386›Full record

ArticlePLoS computational biology2021

Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice.

Warren Woodrich Pettine, Kenway Louie, John D Murray, Xiao-Jing Wang

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Contributions of Attention to Learning in Multidimensional Reward Environments.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  3. Article
  4. Article
  5. The competition dynamics of approach and avoidance motivations following interpersonal transgression.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Warren Woodrich PettineCenter for Neural Science, New York University, New York, United States of America.ORCID 0000-0003-0063-0902
Kenway LouieCenter for Neural Science, New York University, New York, United States of America.ORCID 0000-0002-9665-5436
John D MurrayDepartment of Psychiatry, Yale University School of Medicine, New Haven, United States of America.ORCID 0000-0003-4115-8181
Xiao-Jing WangCenter for Neural Science, New York University, New York, United States of America.ORCID 0000-0003-3124-8474
New York University · USYale University · US

Funding

Recurrent Neual Circuit Basis of Time Integration and Decision MakingR01MH062349 · NIMH · YALE UNIVERSITY · PI WANG, XIAO-JING · 2001 to 2022
$7.2M
Linking Large-Scale Dysconnectivity in Schizophrenia to Cortical Circuit Function at the Individual Level through Computational Modeling and Multimodal NeuroimagingR01MH112746 · NIMH · YALE UNIVERSITY · PI MURRAY, JOHN DAVID · 2017 to 2020
$1.7M
NIMH NIH HHS R01 MH062349NIMH NIH HHS R01 MH112746
6 · The paper itself

Abstract

We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-inhibitory (E/I) tone and cascading nonlinearities, shape attribute processing and choice behavior. Furthermore, how such properties govern choice performance under varying levels of environmental uncertainty is unknown. We investigated two-attribute, two-alternative decision-making in a dynamical, cascading nonlinear neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a final layer producing the decision. Depending on intermediate layer E/I tone, the network displays distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option's attribute information is additively integrated. In regime III, time-varying nonlinear operations amplify the separation between offer distributions by selectively attending to the attribute with the larger differences in input values. At low environmental uncertainty, a linear combination most consistently selects higher valued alternatives. However, at high environmental uncertainty, regime III is more likely than a linear operation to select alternatives with higher value. Furthermore, there are conditions where readout from the intermediate layer could be experimentally indistinguishable from the final layer. Finally, these principles are used to examine multi-attribute decisions in systems with reduced inhibitory tone, leading to predictions of different choice patterns and overall performance between those with restrictions on inhibitory tone and neurotypicals.

Indexed as

Decision MakingModels, NeurologicalNeural Networks, ComputerBrainComputational BiologyHumansUncertainty

Identifiers

PMID33705386
PMCPMC7987200
OpenAlexW3134924743

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.