Evidence map›Paper›PMID 42516064›Full record

ArticleAmerican journal of primatology2026

CapuchinAI 1.0: Development of a Machine Learning-Based Touchscreen Paradigm to Test Cognition in Wild Capuchins.

Federico Sánchez Vargas, Sai Rakshith Potluri, Jacob Abernethy, Marcela E Benítez

Abstract read
In one paragraph

Article in American journal of primatology, 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

5 · Who and what money

Authors and funding

4 authors.

Federico Sánchez VargasDepartment of Anthropology, Emory University, Atlanta, Georgia, USA.ORCID 0009-0000-5456-5881
Sai Rakshith PotluriSchool of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA.
Jacob AbernethySchool of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA.
Marcela E BenítezDepartment of Anthropology, Emory University, Atlanta, Georgia, USA.

Funding

Building an “AI Forest” to identify the social and environmental factors underlying complex behavioral traits in wild primates.R34DA061925 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jacinta Beehner, Marcela Eugenia Benitez · 2025 to 2026
$563k
American Philosophical Association Lewis and Clark Fund For Exploration and Field ResearchEmory University A.I. Humanity Seed GrantLeakey Foundation S202610730National Science Foundation BCS-212737National Science Foundation BCS- 2615421NIDA NIH HHS R34 DA061925NIH HHS R34DA061925
6 · The paper itself

Abstract

Advancing the study of primate cognition requires methods that preserve ecological validity while enabling the experimental control typical of laboratory research. We introduce CapuchinAI v1.0., a field-deployable touchscreen system currently integrating real-time species recognition with automated cognitive testing, providing a novel methodology for studying cognition in wild primates. Our approach combines an adapted version of a high-performing YOLOv7-based facial recognition model (MultipleCapuchins) with a portable Raspberry Pi touchscreen-reward apparatus designed for automated operation in natural habitats. The system detects approaching capuchins, initiates video recording, presents stimuli (in our initial deployment, a blue screen that rewards all touches), and dispenses food rewards. During a 2-week presentation to two habituated groups of wild white-faced capuchins (Cebus imitator) at the Taboga Forest Reserve, 16 individuals voluntarily interacted with the apparatus, 10 triggered rewards, and 8 formed and retained robust screen-reward associations. The rapid habituation and learning rates demonstrate the feasibility of deploying AI-mediated cognitive experiments in the wild. Ongoing development of CapuchinAI aims to address several long-standing challenges in field cognition research, with the goal of enabling: (1) autonomous, individualized task administration without researcher intervention; (2) standardized, repeatable trials across individuals and sessions; (3) scalable deployment across groups and sites; and (4) parallel data collection on behavior, identity, and performance. This methodology provides a blueprint for integrating machine learning and touchscreen testing to study within- and between-individual cognitive variation under natural conditions. CapuchinAI represents a significant step toward long-term comparative research on primate cognition, bridging the gap between lab and field.

Indexed as

CebusCognitionMachine LearningAnimalsFemaleMaleRewardfield experimentsmachine learningprimate cognition

Identifiers

PMID42516064
PMCPMC13408553

What OpenQuestion holds

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Registered trials

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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.