Evidence map›Paper›PMID 41326245›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2026

Orthogonal-Rotational Dynamics Supports Efficient Encoding and Updating for Streaming Information in Working Memory.

Binghao Yang, Shan Yu

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Binghao YangLaboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Shan YuLaboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China shan.yu@nlpr.ia.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We rely on the working memory (WM) to organize, store, and process the perpetual stream of information. Efficient encoding and processing of WM requires a framework that (1) separates individual memory items while accurately maintaining their temporal rank and (2) updates the sequence by discarding no-longer-needed items and accommodating newly arrived ones. To investigate the computational mechanisms underlying this functional implementation of WM, we analyzed the neural information representation in both a recurrent neural network (RNN) model and human subjects (

Indexed as

BrainMemory, Short-TermModels, NeurologicalNeural Networks, ComputerAdultElectroencephalographyFemaleHumansMaleYoung AdultEEGorthogonalityRNN modelrotationworking memory

Identifiers

PMID41326245
PMCPMC12828889

What OpenQuestion holds

Textmetadata
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.