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Bingjie Xu1, Mohan S Kankanhalli2, Qi Zhao3

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Top-down processing influences rapid visual object categorization, even with brief stimuli. Factors like animacy, category level, and object size impact how quickly we recognize items.

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Area of Science:

  • Cognitive Psychology
  • Computational Vision
  • Neuroscience

Background:

  • Visual object recognition is rapid and effortless, typically involving bottom-up and top-down cognitive processing.
  • The role of top-down processing in ultra-rapid categorization (brief stimuli, limited response time) remains unclear.
  • Feedforward information is often assumed sufficient for initial scene discrimination in ultra-rapid scenarios.

Purpose of the Study:

  • To investigate the interaction of top-down manipulations (category level, type, real-world size) in ultra-rapid object categorization.
  • To determine if top-down processing contributes to object recognition under time constraints.
  • To compare human performance with computational models in ultra-rapid categorization tasks.

Main Methods:

  • Developed a dataset of real-world scene images with measured target object display sizes.
  • Measured human ultra-rapid object categorization performance using these images.
  • Utilized standard feedforward computational models and a state-of-the-art object detection model for comparison.

Main Results:

  • Ultra-rapid categorization was influenced by animacy (animal, vehicle, food), level of abstraction (people, sport), and real-world size.
  • Evidence supports the involvement of top-down processing in rapidly categorizing specific objects, like 'sport' at a fine-grained level.
  • Human and model comparisons revealed insights into potential collaboration and integration of cognitive processes.

Conclusions:

  • Top-down processing plays a role even in ultra-rapid visual object categorization.
  • Category level, type, and object size are significant factors influencing rapid recognition.
  • Findings offer valuable perspectives for both experimental and computational vision research.