Summary of Constructive Apraxia: An Unexpected Limit Of Instructible Vision-language Models and Analog For Human Cognitive Disorders, by David Noever and Samantha E. Miller Noever
Constructive Apraxia: An Unexpected Limit of Instructible Vision-Language Models and Analog for Human Cognitive Disorders
by David Noever, Samantha E. Miller Noever
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study surprisingly finds a parallel between instructible vision-language models (VLMs) and human cognitive disorders, specifically constructive apraxia. Researchers tested 25 state-of-the-art VLMs on generating images of the Ponzo illusion, requiring basic spatial reasoning. Strikingly, 24 out of 25 models failed to correctly render two horizontal lines against a perspective background, mirroring deficits seen in patients with parietal lobe damage. The models consistently misinterpreted spatial instructions, producing tilted or misaligned lines. This behavior resembles how apraxia patients struggle to copy simple figures despite intact visual perception and motor skills. The study suggests current VLMs lack fundamental spatial reasoning abilities akin to those impaired in constructive apraxia, highlighting a critical area for improvement in VLM architecture and training methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research found that special AI models called vision-language models (VLMs) are actually similar to people who have a brain disorder called constructive apraxia. The researchers tested 25 of these AI models on creating images of an optical illusion, which requires basic spatial reasoning. Surprisingly, most of the models got it wrong! They consistently misinterpreted what they were supposed to do and created distorted lines. This is similar to how people with constructive apraxia have trouble drawing simple shapes despite having good eyesight. The study suggests that these AI models are missing a fundamental ability to understand spatial relationships, which could be important for making them better at tasks like image generation. |
Keywords
» Artificial intelligence » Image generation
