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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
Published on: January 13, 2023
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What drives the automatic retrieval of real-world object size knowledge?
Simen Hagen1, Yuanfang Zhao1, Lydia Moonen1
1Donders Institute for Brain, Cognition and Behaviour, Radboud University.
Journal of Experimental Psychology. Human Perception and Performance
|February 1, 2024
Summary
The size Stroop effect, influenced by real-world object size, is driven by both object recognition and visual feature associations. This study clarifies the cognitive processes underlying this phenomenon.
Area of Science:
- Cognitive Psychology
- Visual Perception
- Object Recognition
Background:
- The size Stroop effect demonstrates faster responses when object images match their real-world size.
- Two theories explain this effect: automatic retrieval post-recognition or associations with low-level visual features.
Purpose of the Study:
- To investigate whether object recognition or visual feature associations primarily drive the size Stroop effect.
- To differentiate the contributions of object recognition versus visual features in the size Stroop effect.
Main Methods:
- Experiment 1: Compared upright and inverted object stimuli to assess the role of object recognition.
- Experiment 2: Contrasted manmade objects with animals to examine feature-based associations.
- Experiment 3: Used unrecognizable 'texform' objects to isolate visual feature contributions.
Main Results:
- Object inversion significantly reduced the size Stroop effect, highlighting the role of recognition.
- Rectilinearity also contributed to the effect, independent of inversion.
- The effect was larger for animals than manmade objects, suggesting feature associations are not solely responsible.
- Unrecognizable objects showed a small Stroop effect, indicating a role for visual features.
Conclusions:
- The size Stroop effect is influenced by both object recognition and visual feature associations.
- Both high-level (recognition) and low-level (visual features) processing contribute to the automatic retrieval of real-world size information.
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