Neural Dynamics Model of Visual Decision-Making: Learning from Human Experts

TL;DR

This paper presents a neural dynamics model of visual decision-making inspired by primate brain structures, which closely mimics human behavior and neural activity, and improves performance through neuroimaging-informed fine-tuning.

Contribution

The study introduces a biologically inspired neural dynamics model that aligns with human behavior and neural data, and incorporates neuroimaging features for enhanced performance.

Findings

Model closely matches human perceptual decision-making behavior.

Neuroimaging-informed fine-tuning improves model performance.

Model demonstrates robustness and biological plausibility.

Abstract

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.