Neural Dynamics Model of Visual Decision-Making: Learning from Human Experts
This work addresses the challenge of creating biologically plausible AI models for visual decision-making, offering incremental improvements in replicating human-like behavior and neural activity.
The study tackled the problem of modeling visual decision-making by developing a neural dynamics model inspired by primate brain pathways, achieving accuracy comparable to CNNs and improving performance through neuroimaging-informed fine-tuning to better replicate human behavior with enhanced resilience.
Uncovering the fundamental neural correlates of biological intelligence, developing mathematical models, and conducting computational simulations are critical for advancing new paradigms in artificial intelligence (AI). In this study, we implemented a comprehensive visual decision-making model that spans from visual input to behavioral output, using a neural dynamics modeling approach. Drawing inspiration from the key components of the dorsal visual pathway in primates, our model not only aligns closely with human behavior but also reflects neural activities in primates, and achieving accuracy comparable to convolutional neural networks (CNNs). Moreover, magnetic resonance imaging (MRI) identified key neuroimaging features such as structural connections and functional connectivity that are associated with performance in perceptual decision-making tasks. A neuroimaging-informed fine-tuning approach was introduced and applied to the model, leading to performance improvements that paralleled the behavioral variations observed among subjects. Compared to classical deep learning models, our model more accurately replicates the behavioral performance of biological intelligence, relying on the structural characteristics of biological neural networks rather than extensive training data, and demonstrating enhanced resilience to perturbation.