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Development and validation of an AI-generated real-world object stimuli set.

Gerard Campbell1, Graeme Nicholls2, Rebecca Hart3

  • 1Department of Psychological Sciences and Health, University of Strathclyde, 40 George St, Glasgow, G1 1QE, UK. gerard.campbell@strath.ac.uk.

Behavior Research Methods
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

Researchers can now access 200 AI-generated object images for visual cognition studies. These realistic, customizable stimuli were validated for nameability, realism, and familiarity across age groups.

Keywords:
AI image generationArtificial intelligenceReal-world object stimuliValidated stimuliVisual Cognition

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Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Neuroscience

Background:

  • Manual curation of object stimuli for visual cognition research presents challenges due to feature variability.
  • Existing datasets often lack control over stimulus parameters like size, color, and orientation.
  • Artificial intelligence (AI) offers potential for generating highly realistic, customizable visual stimuli.

Purpose of the Study:

  • To generate and validate a dataset of 200 AI-created images of everyday objects for research use.
  • To provide stimuli suitable for embedding in virtual scenes and rendering in various colors.
  • To assess the nameability, perceived realism, and familiarity of AI-generated stimuli across different age groups.

Main Methods:

  • Utilized AI to generate 200 realistic images of everyday objects, oriented on a flat surface.
  • Images were created in greyscale, allowing for flexible color rendering.
  • Conducted a validation study with 90 adults (younger and older) assessing nameability, realism, and familiarity.

Main Results:

  • The majority of AI-generated stimuli received high ratings for nameability, realism, and familiarity.
  • No significant age-related differences were found in the perception of the stimuli.
  • The dataset includes stimuli in seven colors and their validation scores, along with image statistics.

Conclusions:

  • The AI-generated object stimuli are validated and suitable for diverse research applications in visual cognition.
  • The open availability of this dataset facilitates future research in the field.
  • AI advancements enable the creation of tailored visual stimuli, overcoming limitations of traditional datasets.