Evidence map›Paper›PMID 38413465›Full record

ReviewCognitive, affective & behavioral neuroscience2024

Examining the neural basis of unitization: A review.

Nancy A Dennis, Catherine M Carpenter, Alexa Becker

Abstract readReview
In one paragraph

Review in Cognitive, affective & behavioral neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

3 authors.

Nancy A Dennis *The Pennsylvania State University, 450 Moore Building, State College, PA, 16801, USA. nad12@psu.edu.
Catherine M Carpenter *The Pennsylvania State University, 450 Moore Building, State College, PA, 16801, USA.
Alexa Becker *The Pennsylvania State University, 450 Moore Building, State College, PA, 16801, USA.ORCID 0000-0001-9178-9876

Funding

Psychosocial Determinants and Biological Pathway to Healthy Aging (Pathways)T32AG049676 · NIA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LYNN M. MARTIRE · 2016 to 2026
$3.9M
INVESTIGATING AGE-RELATED DIFFERENCES IN COGNITIVE AND NEURAL REPRESENTATIONS IN ASSOCIATIVE MEMORYR15AG052903 · NIA · XAVIER UNIVERSITY · PI OVERMAN, AMY A · 2016 to 2020
$758k
Understanding the neural mechanisms underlying the benefits of unitization on associative memory in young and older adultsR56AG070014 · NIA · PENNSYLVANIA STATE UNIVERSITY, THE · PI DENNIS, NANCY ANNE · 2021 to 2021
$508k
NIA NIH HHS R15 AG052903NIA NIH HHS R56 AG070014NIA NIH HHS T32 AG049676
6 · The paper itself

Abstract

Associative memory refers to the ability to form and remember associations between individual pieces of information rather than memory for a single object or word. Encoding associations in memory tends to be a more difficult task than item (only) encoding, because associative memory requires encoding multiple items as well as the specific links amongst the items. Accordingly, researchers have worked to identify interventions and strategies to reduce the effort and neural resources required for successful associative memory processing. Unitization is one such strategy that has traditionally been defined as the process by which two or more discrete items are processed, or encoded, such that they are perceived as a single ensemble. The current review explores the neural research on unitization while considering the behavioral benefits that accompany the process.

Indexed as

Association LearningBrainAnimalsHumansMemoryAssociative memoryEegfMRIMemoryUnitization

Identifiers

PMID38413465
PMCPMC11542030

What OpenQuestion holds

Textmetadata
LicenceTDM
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.