Evidence map›Paper›PMID 35353316›Full record

ArticleBehavior research methods2023

The Oddity Detection in Diverse Scenes (ODDS) database: Validated real-world scenes for studying anomaly detection.

Michael C Hout, Megan H Papesh, Saleem Masadeh, Hailey Sandin, Stephen C Walenchok, Phillip Post, Jessica Madrid, Bryan White, Juan D Guevara Pinto, Julian Welsh and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Eye-Tracking as a Lens into Expertise Development in Visual Search.Proceedings of the ACM on human-computer interaction · 2025
    Article
  2. Article
  3. 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

13 authors.

Michael C HoutDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA. mhout@nmsu.edu.
Megan H PapeshDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Saleem MasadehDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Hailey SandinDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Stephen C WalenchokExponent, Tempe, AZ, USA.
Phillip PostDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Jessica MadridDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Bryan WhiteDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Juan D Guevara PintoRollins College, Winter Park, FL, USA.
Julian WelshDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Dre GoodeDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Rebecca SkulskyDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.
Mariana Cazares RodriguezDepartment of Psychology, New Mexico State University, P.O. Box 30001 / MSC 3452, Las Cruces, NM, 88003, USA.

Funding

NM-INBRE Sequencing and Bioinformatics CoreP20GM103451 · NIGMS · NEW MEXICO STATE UNIVERSITY LAS CRUCES · PI Charlotte C. Gard · 2012 to 2026
$61.2M
NIGMS NIH HHS P20 GM103451
6 · The paper itself

Abstract

Many applied screening tasks (e.g., medical image or baggage screening) involve challenging searches for which standard laboratory search is rarely equivalent. For example, whereas laboratory search frequently requires observers to look for precisely defined targets among isolated, non-overlapping images randomly arrayed on clean backgrounds, medical images present unspecified targets in noisy, yet spatially regular scenes. Those unspecified targets are typically oddities, elements that do not belong. To develop a closer laboratory analogue to this, we created a database of scenes containing subtle, ill-specified "oddity" targets. These scenes have similar perceptual densities and spatial regularities to those found in expert search tasks, and each includes 16 variants of the unedited scene wherein an oddity (a subtle deformation of the scene) is hidden. In Experiment 1, eight volunteers searched thousands of scene variants for an oddity. Regardless of their search accuracy, they were then shown the highlighted anomaly and rated its subtlety. Subtlety ratings reliably predicted search performance (accuracy and response times) and did so better than image statistics. In Experiment 2, we conducted a conceptual replication in which a larger group of naïve searchers scanned subsets of the scene variants. Prior subtlety ratings reliably predicted search outcomes. Whereas medical image targets are difficult for naïve searchers to detect, our database contains thousands of interior and exterior scenes that vary in difficulty, but are nevertheless searchable by novices. In this way, the stimuli will be useful for studying visual search as it typically occurs in expert domains: Ill-specified search for anomalies in noisy displays.

Indexed as

Pattern Recognition, VisualVisual PerceptionDatabases, FactualHumansReaction TimeAnomaly detectionDatabaseMedical image perceptionVisual search

Identifiers

PMID35353316
PMCPMC8966608

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.