Evidence map›Paper›PMID 32210892›Full record

ArticleFrontiers in psychology2020

How Sequential Interactive Processing Within Frontostriatal Loops Supports a Continuum of Habitual to Controlled Processing.

Randall C O'Reilly, Ananta Nair, Jacob L Russin, Seth A Herd

Abstract read
In one paragraph

Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. A Neural Network Model of Continual Learning with Cognitive Control.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2022
    Article
  5. Complementary Structure-Learning Neural Networks for Relational Reasoning.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2021
    Article
  6. Neural Mechanisms of Human Decision-Making.Cognitive, affective & behavioral neuroscience · 2021
    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.

Randall C O'ReillyComputational Cognitive Neuroscience Lab, Department of Psychology, Computer Science, and Center for Neuroscience, University of California, Davis, Davis, CA, United States.
Ananta NaireCortex, Inc., Boulder, CO, United States.
Jacob L RussinComputational Cognitive Neuroscience Lab, Department of Psychology, Computer Science, and Center for Neuroscience, University of California, Davis, Davis, CA, United States.
Seth A HerdComputational Cognitive Neuroscience Lab, Department of Psychology, Computer Science, and Center for Neuroscience, University of California, Davis, Davis, CA, United States.

Funding

Learning, Memory, and Plasticity (LaMP) Training ProgramT32MH112507 · NIMH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Karen Zito · 2017 to 2026
$2.4M
A Neurobiologically-based Neural Network Model of Risky Decision-makingR01GM109996 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MILLER, LYNN C, READ, STEPHEN J · 2015 to 2018
$1.8M
NIGMS NIH HHS R01 GM109996NIMH NIH HHS T32 MH112507
6 · The paper itself

Abstract

We address the distinction between habitual/automatic vs. goal-directed/controlled behavior, from the perspective of a computational model of the frontostriatal loops. The model exhibits a continuum of behavior between these poles, as a function of the interactive dynamics among different functionally-specialized brain areas, operating iteratively over multiple sequential steps, and having multiple nested loops of similar decision making circuits. This framework blurs the lines between these traditional distinctions in many ways. For example, although habitual actions have traditionally been considered purely automatic, the outer loop must first decide to allow such habitual actions to proceed. Furthermore, because the part of the brain that generates proposed action plans is common across habitual and controlled/goal-directed behavior, the key differences are instead in how many iterations of sequential decision-making are taken, and to what extent various forms of predictive (model-based) processes are engaged. At the core of every iterative step in our model, the basal ganglia provides a "model-free" dopamine-trained Go/NoGo evaluation of the entire distributed plan/goal/evaluation/prediction state. This evaluation serves as the fulcrum of serializing otherwise parallel neural processing. Goal-based inputs to the nominally model-free basal ganglia system are among several ways in which the popular model-based vs. model-free framework may not capture the most behaviorally and neurally relevant distinctions in this area.

Indexed as

automatic processingbasal gangliacomputational modelingcontrolled processingfrontal cortexgoalshabits

Identifiers

PMID32210892
PMCPMC7076192

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.