Evidence map›Paper›PMID 26584306›Full record

ArticlePLoS computational biology2015

Learning of Chunking Sequences in Cognition and Behavior.

Jordi Fonollosa, Emre Neftci, Mikhail Rabinovich

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.6field-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

24 citing papers in PubMed, 66 citations in OpenAlex.

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  4. Motor working memory.Trends in cognitive sciences · 2026
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  8. Beyond neurons and spikes:Cognitive neurodynamics · 2024
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  9. Examining the neural basis of unitization: A review.Cognitive, affective & behavioral neuroscience · 2024
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  14. Neuronal Sequence Models for Bayesian Online Inference.Frontiers in artificial intelligence · 2021
    Review
  15. Active inference on discrete state-spaces: A synthesis.Journal of mathematical psychology · 2020
    Review
  16. Article
  17. Review
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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

3 authors at 1 institution in 2 countries.

Jordi FonollosaBiocircuits Institute, University of California, San Diego, La Jolla, California, United States of America.
Emre NeftciBiocircuits Institute, University of California, San Diego, La Jolla, California, United States of America.
Mikhail RabinovichBiocircuits Institute, University of California, San Diego, La Jolla, California, United States of America.
University of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson's disease and Schizophrenia.

Indexed as

Models, NeurologicalAlgorithmsCognitionComputational BiologyComputer SimulationHumansLearningMental Recall

Identifiers

PMID26584306
PMCPMC4652905
OpenAlexW2175824441

What OpenQuestion holds

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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.