Evidence map›Paper›PMID 42375494›Full record

ArticleNeurocomputing2026

Automated Behavior Analysis in the Novel Object Recognition Test.

Emily Alfs-Votipka, Bhavana Sivayokan, Aliva Bakshi, Sanaz Gheibuni, Doina Caragea, Bethany Plakke, Dave Turner, Daniel Andresen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurocomputing, 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

8 authors.

Emily Alfs-VotipkaDepartment of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, 66506, KS, US.
Bhavana SivayokanDepartment of Psychological Sciences, Kansas State University, 492 Bluemont Hall, 1114 Mid-Campus Dr North, Manhattan, 66506, KS, US.
Aliva BakshiDepartment of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, 66506, KS, US.
Sanaz GheibuniDepartment of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, 66506, KS, US.
Doina CarageaDepartment of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, 66506, KS, US.
Bethany PlakkeDepartment of Psychological Sciences, Kansas State University, 492 Bluemont Hall, 1114 Mid-Campus Dr North, Manhattan, 66506, KS, US.
Dave TurnerDepartment of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, 66506, KS, US.
Daniel AndresenDepartment of Computer Science, Kansas State University, 2184 Engineering Hall, 1701D Platt St., Manhattan, 66506, KS, US.

Funding

The role of prestimulus oscillatory brain dynamics in auditory memoryP20GM113109 · NIGMS · KANSAS STATE UNIVERSITY · PI David Edward Thompson · 2017 to 2026
$24.1M
NIGMS NIH HHS P20 GM113109
6 · The paper itself

Abstract

Accurate identification of animal behaviors is fundamental to behavioral neuroscience, yet manual annotation remains a major bottleneck due to its subjectivity, limited scalability, and high labor cost. This work presents a unified deep learning and heuristic-based framework for automated behavior classification in the Novel Object Recognition Test (NORT), a widely used paradigm for assessing memory and cognitive function in rodents. We fine-tuned a YOLOv11 Pose model to detect

Indexed as

Behavior ClassificationBehavior RecognitionDeep LearningKeypoint AnnotationNovel Object Recognition Test (NORT)Object InteractionStandingYOLOv11 Pose & Classification Models

Identifiers

PMID42375494
PMCPMC13313593

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.