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Related Concept Videos

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Comparing perceptual judgments in large multimodal models and humans.

Billy Dickson1, Sahaj Singh Maini2, Craig Sanders3

  • 1Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, 700 N Woodlawn Ave, Bloomington, IN, 47408, USA. dicksonb@iu.edu.

Behavior Research Methods
|June 19, 2025
PubMed
Summary

Large multimodal models (LMMs) show promise in replacing human judgments for cognitive science research. GPT-4o closely matched human perceptions of rock images, especially for basic features, offering a faster alternative.

Keywords:
High-dimensional similarity spacesLarge language modelsLarge multimodal modelsPerceptual judgmentsVision-language models

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Cognitive scientists use human judgments of stimuli to build models of attention, memory, learning, and decision-making.
  • Collecting these perceptual judgments is often time-consuming and expensive.
  • Large multimodal models (LMMs) offer a potential solution due to their ability to process both text and images.

Purpose of the Study:

  • To evaluate the utility of LMMs as a replacement for human participants in collecting perceptual judgments.
  • To assess the performance of various LMMs on a standardized dataset of rock images used in cognitive science.

Main Methods:

  • Evaluated LMM judgments against human perceptual judgments on a rock image dataset.
  • Dataset included human ratings across 10 dimensions crucial for rock classification.
  • Compared LMM performance, focusing on GPT-4o's correlation with human responses.

Main Results:

  • GPT-4o demonstrated the highest positive correlation with human judgments.
  • GPT-4o showed strong alignment with human ratings for elementary perceptual dimensions (lightness, chromaticity, shininess, texture).
  • Correlation was lower for abstract, rock-specific dimensions (organization, pegmatitic structure).

Conclusions:

  • LMMs, particularly GPT-4o, show potential for replacing human participants in perceptual judgment tasks.
  • Current LMMs approach human consensus for basic perceptual features, though abstract features require further development.
  • This study establishes a benchmark for evaluating future LMMs using human perceptual data.