Noever, D., & Noever, S. E. M. (2024). CONSTRUCTIVE APRAXIA: AN UNEXPECTED LIMIT OF INSTRUCTIBLE VISION-LANGUAGE MODELS AND ANALOG FOR HUMAN COGNITIVE DISORDERS.
This study investigates the limitations of vision-language models (VLMs) in spatial reasoning tasks, drawing a parallel with constructive apraxia, a human cognitive disorder. The authors aim to assess the ability of VLMs to accurately interpret and execute spatial instructions, particularly in relation to basic geometric concepts and figure reproduction.
The researchers tested 25 state-of-the-art VLMs, including GPT-4 Vision, DALL-E 3, and Midjourney v5, on their ability to generate images based on specific spatial instructions. The primary test involved instructing the models to draw two horizontal lines of equal length against a perspective background, mimicking the Ponzo Illusion, a visual task often used to assess constructive apraxia in humans. Additional tests included recreating a complex diamond pattern and other spatial reasoning challenges. The researchers analyzed the models' outputs for accuracy and consistency in following instructions, comparing their performance to the typical challenges faced by individuals with constructive apraxia.
The study found that the majority of tested VLMs (24 out of 25) failed to accurately render the horizontal lines in the Ponzo Illusion task. Instead of producing perfectly horizontal lines, the models consistently generated tilted or misaligned lines that followed the perspective of the background, mirroring the spatial reasoning deficits observed in patients with constructive apraxia. Similar failures were observed in other spatial tasks, such as reproducing the diamond pattern.
The authors conclude that current VLMs, despite their advanced capabilities in other areas, lack fundamental spatial reasoning abilities, drawing a strong parallel with constructive apraxia in humans. This limitation, attributed to the models' training methodologies that lack explicit encoding of geometric principles and physical laws, presents a significant challenge for their application in fields requiring precise spatial understanding, such as medicine, manufacturing, and autonomous driving.
This research highlights a critical limitation in current VLM technology and suggests a need for new approaches to incorporate spatial reasoning abilities into their architecture and training. Furthermore, the observed parallels between VLM limitations and human cognitive disorders open up new avenues for interdisciplinary research, potentially leading to a deeper understanding of both artificial and biological intelligence.
The study primarily focused on a limited set of spatial tasks, and further research is needed to explore a wider range of spatial reasoning challenges. Future work could investigate the impact of different training methodologies and architectural modifications on VLMs' spatial understanding. Additionally, exploring the potential of these findings for developing novel diagnostic tools for cognitive disorders and improving rehabilitation strategies is a promising direction.
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by David Noever... في arxiv.org 10-07-2024
https://arxiv.org/pdf/2410.03551.pdfاستفسارات أعمق