L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection And Enhancement

2024-12-12

AI Summary

This study addresses the low efficiency of human visual category learning by proposing a model-based image selection and augmentation framework. Methodologically, it pioneers the use of ventral-stream-inspired artificial neural networks (e.g., CORnet, VGG) to actively optimize human learning: first, predicting image recognition difficulty to enable difficulty-aware dynamic sampling; second, generating cognitively favorable adversarial or feature-directed perturbations to enhance learnability. This dual-model-driven approach significantly improves fine-grained classification performance among novices—boosting accuracy by 33–72% and reducing training time by 20–23% across natural images, histopathology, and dermoscopy tasks. The core contribution lies in extending ANNs beyond passive discriminative tools to active cognitive guides for human visual learning, establishing a novel paradigm for model-driven cognitive enhancement.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Active Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The currently leading artificial neural network models of the visual ventral stream - which are derived from a combination of performance optimization and robustification methods - have demonstrated a remarkable degree of behavioral alignment with humans on visual categorization tasks. We show that image perturbations generated by these models can enhance the ability of humans to accurately report the ground truth class. Furthermore, we find that the same models can also be used out-of-the-box to predict the proportion of correct human responses to individual images, providing a simple, human-aligned estimator of the relative difficulty of each image. Motivated by these observations, we propose to augment visual learning in humans in a way that improves human categorization accuracy at test time. Our learning augmentation approach consists of (i) selecting images based on their model-estimated recognition difficulty, and (ii) applying image perturbations that aid recognition for novice learners. We find that combining these model-based strategies leads to categorization accuracy gains of 33-72% relative to control subjects without these interventions, on unmodified, randomly selected held-out test images. Beyond the accuracy gain, the training time for the augmented learning group was also shortened by 20-23%, despite both groups completing the same number of training trials. We demonstrate the efficacy of our approach in a fine-grained categorization task with natural images, as well as two tasks in clinically relevant image domains - histology and dermoscopy - where visual learning is notoriously challenging. To the best of our knowledge, our work is the first application of artificial neural networks to increase visual learning performance in humans by enhancing category-specific image features.

Problem

• Enhance human visual categorization accuracy using model-based image selection.
• Predict human response accuracy to images using neural network models.
• Improve visual learning in challenging domains like histology and dermoscopy.

Innovation

• Model-based image selection enhances learning.
• Image perturbations improve human categorization accuracy.
• Neural networks predict image recognition difficulty.