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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.11516 |
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| _version_ | 1866916362346037248 |
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| author | Ding, Yue Shi, Hongqiao Song, Shuang Wang, Yonghui Li, Ya |
| author_facet | Ding, Yue Shi, Hongqiao Song, Shuang Wang, Yonghui Li, Ya |
| contents | The integration of local elements into shape contours is critical for target detection and identification in cluttered scenes. Previous studies have shown that observers can learn to use image regularities for contour integration and target identification. However, we still know little about the generalization of perceptual learning in contour integration. Specifically, whether training in contour detection task could transfer to untrained contour type, path or orientation is still unclear. In a series of four experiments, human perceptual learning in contour detection was studied using psychophysical methods. We trained participants to detect contours in cluttered scenes over several days, which resulted in a significant improvement in sensitivity to trained contour type. This improved sensitivity was highly specific to contour type, but transfer across changes in contour path and contour orientation. These results suggest that short-term training improves the ability to integrate specific types of contours by optimizing the ability of the visual system to extract specific image regularities. The differential specificity and generalization across different stimulus features may support the involvement of both low-level and higher-level visual areas in perceptual learning in contour detection. These findings provide further insights into understanding the nature and the brain plasticity mechanism of contour integration learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11516 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Perceptual learning in contour detection transfer across changes in contour path and orientation Ding, Yue Shi, Hongqiao Song, Shuang Wang, Yonghui Li, Ya Neurons and Cognition The integration of local elements into shape contours is critical for target detection and identification in cluttered scenes. Previous studies have shown that observers can learn to use image regularities for contour integration and target identification. However, we still know little about the generalization of perceptual learning in contour integration. Specifically, whether training in contour detection task could transfer to untrained contour type, path or orientation is still unclear. In a series of four experiments, human perceptual learning in contour detection was studied using psychophysical methods. We trained participants to detect contours in cluttered scenes over several days, which resulted in a significant improvement in sensitivity to trained contour type. This improved sensitivity was highly specific to contour type, but transfer across changes in contour path and contour orientation. These results suggest that short-term training improves the ability to integrate specific types of contours by optimizing the ability of the visual system to extract specific image regularities. The differential specificity and generalization across different stimulus features may support the involvement of both low-level and higher-level visual areas in perceptual learning in contour detection. These findings provide further insights into understanding the nature and the brain plasticity mechanism of contour integration learning. |
| title | Perceptual learning in contour detection transfer across changes in contour path and orientation |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2403.11516 |